R-TF-015-002 Preclinical and clinical evaluation record_2023_001
Scope
This literature review compiles and evaluates state-of-the-art publications on computer vision in medicine. The overall objective of this strategy is to identify, select and collect the relevant literature to determine whether the device can achieve its intended purpose.
Medical device information
Manufacturer contact details
| Manufacturer data | |
|---|---|
| Legal manufacturer name | AI Labs Group S.L. |
| Address | Street Gran Vía 1, BAT Tower, 48001, Bilbao, Bizkaia (Spain) |
| SRN | ES-MF-000025345 |
| Person responsible for regulatory compliance | Alfonso Medela, María Diez, Giulia Foglia |
| office@legit.health | |
| Phone | +34 638127476 |
| Trademark | Legit.Health |
Medical device characterization
| Information | |
|---|---|
| Device name | Legit.Health Plus (hereinafter, the device) |
| Model and type | NA |
| Version | 1.0.0.0 |
| Basic UDI-DI | 8437025550LegitCADx6X |
| Certificate number (if available) | MDR 792790 |
| EMDN code(s) | Z12040192 (General medicine diagnosis and monitoring instruments - Medical device software) |
| GMDN code | 65975 |
| Class | Class IIb |
| Classification rule | Rule 11 |
| Novel product (True/False) | FALSE |
| Novel related clinical procedure (True/False) | FALSE |
| SRN | ES-MF-000025345 |
Intended purpose
Intended use
The device is a computational software-only medical device intended to support health care providers in the assessment of skin structures, enhancing efficiency and accuracy of care delivery, by providing:
- quantification of intensity, count, extent of visible clinical signs
- interpretative distribution representation of possible International Classification of Diseases (ICD) classes.
Quantification of intensity, count and extent of visible clinical signs
The device provides quantifiable data on the intensity, count and extent of clinical signs such as erythema, desquamation, and induration, among others; including, but not limited to:
- erythema,
- desquamation,
- induration,
- crusting,
- dryness,
- oedema,
- oozing,
- excoriation,
- swelling,
- lichenification,
- exudation,
- depth,
- edges,
- undermining,
- pustulation,
- hair loss,
- type of necrotic tissue,
- amount of necrotic tissue,
- type of exudate,
- peripheral tissue edema,
- peripheral tissue induration,
- granulation tissue,
- epithelialization,
- nodule count,
- papule count,
- pustule count,
- cyst count,
- comedone count,
- abscess count,
- draining tunnel count,
- lesion count
Image-based recognition of visible ICD classes
The device is intended to provide an interpretative distribution representation of possible International Classification of Diseases (ICD) classes that might be represented in the pixels content of the image.
Device description
The device is computational software-only medical device leveraging computer vision algorithms to process images of the epidermis, the dermis and its appendages, among other skin structures. Its principal function is to provide a wide range of clinical data from the analyzed images to assist healthcare practitioners in their clinical evaluations and allow healthcare provider organisations to gather data and improve their workflows.
The generated data is intended to aid healthcare practitioners and organizations in their clinical decision-making process, thus enhancing the efficiency and accuracy of care delivery.
The device should never be used to confirm a clinical diagnosis. On the contrary, its result is one element of the overall clinical assessment. Indeed, the device is designed to be used when a healthcare practitioner chooses to obtain additional information to consider a decision.
Intended medical indication
The device is indicated for use on images of visible skin structure abnormalities to support the assessment of all diseases of the skin incorporating conditions affecting the epidermis, its appendages (hair, hair follicle, sebaceous glands, apocrine sweat gland apparatus, eccrine sweat gland apparatus and nails) and associated mucous membranes (conjunctival, oral and genital), the dermis, the cutaneous vasculature and the subcutaneous tissue (subcutis).
Intended patient population
The device is intended for use on images of skin from patients presenting visible skin structure abnormalities, across all age groups, skin types, and demographics.
Intended user
The medical device is intended for use by healthcare providers to aid in the assessment of skin structures.
User qualification and competencies
In this section we specificy the specific qualifications and competencies needed for users of the device, to properly use the device, provided that they already belong to their professional category. In other words, when describing the qualifications of HCPs, it is assumed that healthcare professionals (HCPs) already have the qualifications and competencies native to their profession.
Healthcare professionals
No official qualifications are needes, but it is advisable if HCPs have some competencies:
- Knowledge on how to take images with smartphones.
IT professionals
IT professionals are responsible for the integration of the medical device into the healthcare organisation's system.
No specific official qualifications are needed, but it is advisable that IT professionals using the device have the following competencies:
- Basic knowledge of FHIR
- Understanding of the output of the device.
Use environment
The device is intended to be used in the setting of healthcare organisations and their IT departments, which commonly are situated inside hospitals or other clinical facilities.
The device is intended to be integrated into the healthcare organisation's system by IT professionals.
Operating principle
The device is computational medical tool leveraging computer vision algorithms to process images of the epidermis, the dermis and its appendages, among other skin structures.
Body structures
The device is intended to use on the epidermis, its appendages (hair, hair follicle, sebaceous glands, apocrine sweat gland apparatus, eccrine sweat gland apparatus and nails) and associated mucous membranes (conjunctival, oral and genital), the dermis, the cutaneous vasculature and the subcutaneous tissue (subcutis).
In fact, the device is intended to use on visible skin structures. As such, it can only quantify clinical signs that are visible, and distribute the probabilities across ICD classes that are visible.
Variants and models
The device does not have any variants.
Expected lifetime
Its expected lifetime is considered unlimited because the device will be updated with each improvement opportunity extracted from the information and analysis of the data provided by the continuous and systematic post-market data follow-up.
List of any accessories
The device does not have any accessories.
The device can be used from any device with an internet connection.
Explanation of any novel features
The device is the result of an incremental improvement of an existing technology. It has been developed to improve the current state of the art to process photographs of skin structure and then processes them with artificial intelligence algorithms.
We detail the novel features of the devide within the Legit.Health Plus Description and specifications 2023_001 document.
Literature search methodology
The literature search methodology establishes the strategy used to search and compile scientific articles, as well as the criteria used to include/exclude and evaluate the doctuments compiled.
Review team undertaking the literature search
The literature search protocol has been developed and executed by professionals with expertise in information retrieval and understanding of the scope of the clinical evaluation set out by the manufacturer, who have been compensated for the time taken to perform the review.
The involvement of the literature review team will help to optimize literature retrieval to identify all relevant published literature. The review team has prepared the protocol to describe an objective, non-biased systematic search and review methods, using information found in the IFU and risk management process to develop the clinical research questions.
Period covered by the search
The present literature search complements the last Clinical Evaluation literature search (2011 to 2020) by analyzing the work published in a 3-year period from 2021 to 2023, and further extends the above search to include the condition of facial palsy.
Goals of the literature search
This literature search was conducted to include the relevant scientific articles that are related to:
- Clinical trials that probe the safety and usefulness of Computer-Aided Systems for obtaining interpretative distribution representation of possible International Classification of Diseases (ICD) classes that might be represented in the pixels content of the image and quantifiable data on the intensity, count and extent of clinical signs such as erythema, desquamation, and induration, among others.
- State-of-the-art algorithms and methods in the fields of visible conditions, specially dermatoses and facial palsy, to obtain an interpretative distribution representation of possible International Classification of Diseases (ICD) classes that might be represented in the pixels content of the image, and quantifiable data on the intensity, count and extent of clinical signs such as erythema, desquamation, and induration, among others.
- Datasets that are useful for the development of technology to obtain an interpretative distribution representation of possible International Classification of Diseases (ICD) classes that might be represented in the pixels content of the image, and quantifiable data on the intensity, count and extent of clinical signs such as erythema, desquamation, and induration, among others, in skin structures.
- Smartphone applications that implement or evaluate skin structures to obtain an interpretative distribution representation of possible International Classification of Diseases (ICD) classes that might be represented in the pixels content of the image, and quantifiable data on the intensity, count and extent of clinical signs such as erythema, desquamation, and induration, among others.
- Reviews and surveys of the available literature related to obtain an interpretative distribution representation of possible International Classification of Diseases (ICD) classes that might be represented in the pixels content of the image, and quantifiable data on the intensity, count and extent of clinical signs such as erythema, desquamation, and induration, among others, in skin structures.
Data sources
For the scientific article compilation we have used different sources of information:
Medical Literature Databases
- PubMed (MEDLINE): comprises more than 36 million citations for biomedical literature from MEDLINE, life science journals, and online books.
- Cochrane Library: collection of databases that contain high-quality, independent evidence to inform healthcare decision-making. The Cochrane Library is owned by Cochrane and published by Wiley.
Google Scholar
Google Scholar provides a simple way to broadly search for scholarly literature across a wide variety of disciplines and sources: articles, theses, books, abstracts and court opinions. Different from the Medical Literature Databases, Google Scholar also includes medical articles that have been published in non-medical journals and conferences and that might not have been indexed by the previous Medical Literature Databases.
Article Citations
Scientific articles are also a meaningful source of information when describing their related work. Relevant articles that are cited within a given published manuscript are also considered for their evaluation.
The PICO format
The Literature Search methodology is based on the PICO format.
This format has been designed for high-quality clinical research evidence by defining a set of relevant keywords in four different categories (Population, Intervention, Comparison, and Outcome), that are then used to construct the search queries. Since our research is not limited to clinical research, by also to articles related to state-of-the-art Deep Learning algorithms, paper reviews, and the deployment of Deep Learning solutions, we have modified these categories to also include keywords related to non-clinical terms.
| Description | |
|---|---|
| Population Patient Problem (P) | Diseases that the software is able to identify (skin cancer, melanoma, chronic skin conditions). |
| Intervention (I) | Intervention, prognostic factor or exposure. |
| Comparator (C) | Known software, algorithm, or method with the same characteristics as the device. |
| Outcome (O) | Diagnosis, identification, analysis, or assessment of certain skin pathologies. |
Screening, selection, and organizing literature
The articles retrieved from each data source go through an initial set of filters to ensure their quality and adequacy to the goals of this literature review:
Metadata check
Duplicate articles, non-English articles, and articles published before 2021 are not considered for the Data Appraisal.
Scope analysis
We read the abstract, motivation, and conclusions of each article to discern if they match the scope of topics pursued in the Literature Review. Articles that are our of the scope of this literature review are filtered out. After this initial look, we save a brief summary of the content and contribution of the articles.
Publication source quality analysis
For each article, we analyze the impact factor of the journal, conference, or book where they have been published. The impact factor is a metric that reflects the ratio between the number of citations received in a year and the number of citable publications that were published in the same source during the two preceding years. In our case, we retrieve the Impact Factor from Academic Accelerator that has been calculated for the year 2021. Finally, we filter out the articles that have an Impact Factor under 3.5, which stands for a low-quality publication source. Since the Impact Factor measures the quality of the publication source and not the quality of the publication itself, we also included for the Data Appraisal the papers that we considered relevant during the Scope Analysis step.
The articles that have passed the screening quality evaluation are then forwarded to their appraisal.
Appraisal of selected literature
The Data Appraisal evaluates the suitability of the selected articles for establishing the safety and performance of the device. To this end, we take into consideration three parameters: the methodological quality and scientific validity of the information (Q), the relevance to the device and its intended use (R), and the contribution to the overall evaluation (C). Articles are finally accepted or rejected after applying the following weight-based system. In this process, we also compile some notes related to the content and contribution of the articles.
| Data characteristics | Methodological quality Q | Relevance, R | Contribution, C | Weighted value, W (W=Q+R+C) | Appraisal data | Notes |
|---|---|---|---|---|---|---|
| Very relevant information in relation to the product and its intended use | Up to 40 | Up to 30 | Up to 30 | W ≥ 70 | Accepted | Pivotal data |
| Relevant information in relation to the product and its intended use | Up to 40 | Up to 30 | Up to 30 | 30 < W < 70 | Accepted | Other data |
| Little relevant information in relation to the product and its intended use | Up to 40 | Up to 30 | Up to 30 | W ≤ 30 | Rejected | No contribution, rejected |
Methodological quality and scientific validity of the information (Q):
- The scientific article properly describes the relevant related work.
- The scientific article compares its results and conclusions with the relevant related work.
- Clinical trials use data extracted from RCTs (Randomized Clinical Trials), meta-analysis and clinical trials, from publications based on scientific evidence or from previous experience on the market.
- The study design is adequate in terms of type, sample size, relevance of endpoints, randomization of patients, inclusion and exclusion criteria, stratification of patients, prognostic factors, follow-up, recording and reporting of serious adverse events, reliability of the methods used for quantifying outcomes, or procedures for retrieving complete information.
- Data consists of an adequate number of observations that can confirm or refute the claims of the device under evaluation.
- The methods for data processing and statistics are suitable.
- It complies with Good Clinical Practice (GCP) according to EN ISO 14155 or equivalent standards.
- The information is disclosed adequately, including a summary, introduction, methods, results, discussion and conclusions.
Relevance to the device and its intended use (R):
- It is representative of the device under evaluation, including data concerning the medical conditions that are managed with the device, other devices and medical alternatives, or equivalent devices.
- The aspects covered are related to clinical safety and performance, claims, hazards and hazardous situations, management of risks, establishment of current knowledge and state of the art, determination and justification of the benefit/risk ratio and the acceptability of undesirable side-effects, or the determination of equivalence.
- It is representative to the intended purpose or claims of the device under evaluation.
- It is representative to the user group, patient population, medical indication, age group, gender, type and severity of the medical condition, or range of time.
- It develops algorithms or techniques to improve the performance of skin-related image analysis.
Contribution to the overall evaluation (C):
- Device and/or related techniques intended use. The contribution of the analyzed data in relation to the device or technique features and in relation to the intended use must be strong and clear.
- Device and/or related techniques risk analysis. The contribution of the analyzed data in relation to the device or techniques of risk analysis must be strong and clear, based on keywords definition.
Literature Review results
Screening
For each one of the PICO components we have defined the set of keywords as:
| Category | Keywords |
|---|---|
| Population, Patient, Problem (P) | dermatosis, skin cancer, chronic skin conditions, inflammatory skin diseases, malignant skin lesions, pigmented skin lesions, melanoma, basal cell carcinoma, squamous cell carcinoma, atypical nevus, acne, psoriasis, urticaria, atopic dermatitis, onychomycosis, melasma, solar lentigo, dermatofibroma, skin diseases, skin lesions |
| Intervention (I) | clinical image, digital imaging, web application, smartphone, dermatoscopy, camera, CAD, dermatoscope |
| Comparator (C) | artificial intelligence, machine learning, deep learning, computer vision, deep neural networks, convolutional neural networks, metaoptima, automated |
| Outcome (O) | diagnosis, diagnosis support, followup, segmentation, detection, estimation, classification, assessment, severity assessment, improving |
In the following, we describe how we use these keywords to form queries for each source of information, the retrieved articles, and the outcome of the Data Screening.
PubMed
Following the PICO format and using the PubMed advanced search tool, we have defined a global query to retrieve the articles whose title or abstract relate to the query:
(("dermatosis") OR ("skin cancer") OR ("chronic skin conditions") OR ("inflammatory skin diseases") OR ("malignant skin lesions") OR ("pigmented skin lesions") OR ("melanoma") OR ("basal cell carcinoma") OR ("squamous cell carcinoma") OR ("atypical nevus") OR ("acne") OR ("psoriasis") OR ("urticaria") OR ("atopic dermatitis") OR ("onychomycosis") OR ("melasma") OR ("solar lentigo") OR ("dermatofibroma") OR ("skin diseases") OR ("skin lesions")) AND (("clinical image") OR ("digital imaging") OR ("web application") OR ("smartphone") OR ("dermatoscopy") OR ("camera") OR ("CAD")) AND (("artificial intelligence") OR ("machine learning") OR ("deep learning") OR ("computer vision") OR ("deep neural networks") OR ("convolutional neural networks") OR ("metaoptima") OR ("automated")) AND (("diagnosis") OR ("diagnosis support") OR ("followup") OR ("segmentation") OR ("detection") OR ("estimation") OR ("classification") OR ("assessment") OR ("severity assessment") OR ("improving"))
As a result, we have compiled 112 articles of which 52 articles have passed the Data Screening:
The editable table is here: https://docs.google.com/spreadsheets/d/1QrUi-Q0REVfh-X6YBD8ExUm0WsYbRoXZXyodLp_fJIo/edit?usp=sharing
| ID | Name | Journal/Conference | Impact factor | In scope? Not duplicated? | Appraisal included |
|---|---|---|---|---|---|
| 001 | Explainable artificial intelligence in skin cancer recognition: A systematic review | European Journal of Cancer | 10.002 | Y | Y |
| 002 | Dermatoscopy | Clinics in dermatology | 2.797 | Y | N |
| 003 | Legal and ethical considerations of artificial intelligence in skin cancer diagnosis | The Australasian journal of dermatology | 2.481 | N | N |
| 004 | DermIA: Machine Learning to Improve Skin Cancer Screening | Journal of digital imaging | 4.903 | Y | Y |
| 005 | Artificial intelligence and melanoma: A comprehensive review of clinical, dermoscopic, and histologic applications | Pigment cell & melanoma research | 4.159 | N | N |
| 006 | Deep Learning for Clinical Image Analyses in Oral Squamous Cell Carcinoma: A Review | JAMA otolaryngology-- head & neck surgery | 8.961 | N | N |
| 007 | Non-Melanoma Skin Cancer Detection in the Age of Advanced Technology: A Review | Cancers | 6.575 | Y | Y |
| 008 | Artificial Intelligence as a Diagnostic Tool in Non-Invasive Imaging in the Assessment of Coronary Artery Disease | Medical sciences (Basel, Switzerland) | 3.642 | N | N |
| 009 | Telemedicine and e-Health in the Management of Psoriasis: Improving Patient Outcomes A Narrative Review | Psoriasis (Auckland, N.Z.) | N/A | Y | Y |
| 010 | Computational Intelligence-Based Melanoma Detection and Classification Using Dermoscopic Images | Computational intelligence and neuroscience | 3.120 | Y | N |
| 011 | Differential Diagnosis of Rosacea Using Machine Learning and Dermoscopy | Clinical, cosmetic and investigational dermatology | 2.765 | Y | N |
| 012 | Evaluation of a smartphone application for diagnosis of skin diseases | Postepy dermatologii i alergologii | 1.664 | Y | N |
| 013 | Artificial intelligence for the automated single-shot assessment of psoriasis severity | Journal of the European Academy of Dermatology and Venereology : JEADV | 9.228 | Y | Y |
| 014 | A survey, review, and future trends of skin lesion segmentation and classification | Computers in biology and medicine | 6.698 | Y | Y |
| 015 | Digital skin imaging applications, part I: Assessment of image acquisition technique features | Skin Research and Technology | 2.240 | Y | Y |
| 016 | Melanoma segmentation using deep learning with test-time augmentations and conditional random fields | Scientific reports | 4.997 | Y | Y |
| 017 | Development and Validation of a Model to Predict Posttraumatic Stress Disorder and Major Depression After a Motor Vehicle Collision | JAMA psychiatry | 25.936 | N | N |
| 018 | DenseNet-II: an improved deep convolutional neural network for melanoma cancer detection | Soft computing | 3.732 | Y | Y |
| 019 | Automatic skin disease diagnosis using deep learning from clinical image and patient information | Skin health and disease | N/A | Y | Y |
| 020 | An interpretable CNN-based CAD system for skin lesion diagnosis | Artificial intelligence in medicine | 7.011 | Y | Y |
| 021 | A cell phone app for facial acne severity assessment | Applied intelligence (Dordrecht, Netherlands) | 5.019 | Y | Y |
| 022 | Deep learning detection of melanoma metastases in lymph nodes | European journal of cancer (Oxford, England : 1990) | 10.002 | N | N |
| 023 | Automated detection of mouse scratching behaviour using convolutional recurrent neural network | Scientific reports | 4.997 | N | N |
| 024 | Extravasation Screening and Severity Prediction from Skin Lesion Image using Deep Neural Networks | Annual International Conference of the IEEE Engineering in Medicine & Biology Society | 0.346 | Y | N |
| 025 | Monitoring of Pigmented Skin Lesions Using 3D Whole Body Imaging | Computer methods and programs in biomedicine | 7.027 | Y | Y |
| 026 | Computer-Aided Assessment of Melanocytic Lesions by Means of a Mitosis Algorithm | Diagnostics | 3.992 | N | N |
| 027 | Machine learning based skin lesion segmentation method with novel borders and hair removal techniques | PloS one | 3.752 | Y | Y |
| 028 | The role of mobile teledermoscopy in skin cancer triage and management during the COVID-19 pandemic | Indian journal of dermatology, venereology and leprology | 2.217 | Y | N |
| 029 | Medical tumor image classification based on Few-shot learning | IEEE/ACM transactions on computational biology and bioinformatics | 3.702 | N | N |
| 030 | Convolutional neural network assistance significantly improves dermatologists' diagnosis of cutaneous tumours using clinical images | European journal of cancer (Oxford, England : 1990) | 10.002 | Y | Y |
| 031 | Skin Lesion Segmentation Using an Ensemble of Different Image Processing Methods | Diagnostics | 3.992 | Y | Y |
| 032 | Attention Cost-Sensitive Deep Learning-Based Approach for Skin Cancer Detection and Classification | Cancers | 6.575 | Y | Y |
| 033 | The Role of Pathology-Based Methods in Qualitative and Quantitative Approaches to Cancer Immunotherapy | Cancers | 6.575 | N | N |
| 034 | ExAID: A multimodal explanation framework for computer-aided diagnosis of skin lesions | Computer methods and programs in biomedicine | 7.027 | Y | Y |
| 035 | Automatic Acne Object Detection and Acne Severity Grading Using Smartphone Images and Artificial Intelligence | Diagnostics | 3.992 | Y | Y |
| 036 | Computer-aided clinical image analysis for non-invasive assessment of tumor thickness in cutaneous melanoma | BMC research notes | 0.527 | N | N |
| 037 | An IoMT-Based Melanoma Lesion Segmentation Using Conditional Generative Adversarial Networks | Sensors | 3.900 | Y | Y |
| 038 | Smartphone-Based Hyperspectral Imaging Low-Cost Application for Telemedicine | Studies in health technology and informatics | 0.277 | N | N |
| 039 | Human Monkeypox Classification from Skin Lesion Images with Deep Pre-trained Network using Mobile Application | Journal of medical systems | 4.920 | Y | Y |
| 040 | A Visually Interpretable Deep Learning Framework for Histopathological Image-Based Skin Cancer Diagnosis | IEEE journal of biomedical and health informatics | 7.021 | N | N |
| 041 | ZooME: Efficient Melanoma Detection Using Zoom-in Attention and Metadata Embedding Deep Neural Network | Annual International Conference of the IEEE Engineering in Medicine & Biology Society | 0.346 | Y | N |
| 042 | Melanoma classification from dermatoscopy images using knowledge distillation for highly imbalanced data | Computers in biology and medicine | 6.698 | Y | Y |
| 043 | Adopting low-shot deep learning for the detection of conjunctival melanoma using ocular surface images | Computer methods and programs in biomedicine | 7.027 | N | N |
| 044 | A rotation meanout network with invariance for dermoscopy image classification and retrieval | Computers in biology and medicine | 6.698 | Y | Y |
| 045 | Implementation of artificial intelligence algorithms for melanoma screening in a primary care setting | PloS one | 3.752 | Y | Y |
| 046 | A Two-Stage Automatic Color Thresholding Technique | Sensors | 3.900 | N | N |
| 047 | A Deep CNN Transformer Hybrid Model for Skin Lesion Classification of Dermoscopic Images Using Focal Loss | Diagnostics | 3.992 | Y | Y |
| 048 | Image analysis of cutaneous melanoma histology: a systematic review and meta-analysis | Scientific reports | 4.997 | N | N |
| 049 | Application of an artificial intelligence system for endoscopic diagnosis of superficial esophageal squamous cell carcinoma | World journal of gastroenterology | 5.374 | N | N |
| 050 | A deep learning based multimodal fusion model for skin lesion diagnosis using smartphone collected clinical images and metadata | Frontiers in surgery | 2.350 | Y | N |
| 051 | A novel approach toward skin cancer classification through fused deep features and neutrosophic environment | Frontiers in public health | 6.461 | Y | Y |
| 052 | Developing a Recognition System for Diagnosing Melanoma Skin Lesions Using Artificial Intelligence Algorithms | Computational and mathematical methods in medicine | 2.809 | Y | N |
| 053 | A machine learning-based, decision support, mobile phone application for diagnosis of common dermatological diseases | Journal of the European Academy of Dermatology and Venereology : JEADV | 9.228 | Y | Y |
| 054 | AutoRadiomics: A Framework for Reproducible Radiomics Research | Frontiers in radiology | N/A | N | N |
| 055 | Development and Clinical Evaluation of an Artificial Intelligence Support Tool for Improving Telemedicine Photo Quality | JAMA dermatology | 11.816 | Y | Y |
| 056 | The Role of Machine Learning and Deep Learning Approaches for the Detection of Skin Cancer | Healthcare (Basel, Switzerland) | N/A | Y | Y |
| 057 | Diagnosis of Oral Squamous Cell Carcinoma Using Deep Neural Networks and Binary Particle Swarm Optimization on Histopathological Images: An AIoMT Approach | Computational intelligence and neuroscience | 3.120 | N | N |
| 058 | Automatic identification of benign pigmented skin lesions from clinical images using deep convolutional neural network | BMC biotechnology | 3.329 | Y | Y |
| 059 | Classification and diagnostic prediction of breast cancer metastasis on clinical data using machine learning algorithms | Scientific reports | 4.997 | N | N |
| 060 | Reevaluation of missed lung cancer with artificial intelligence | Respiratory medicine case reports | 0.354 | N | N |
| 061 | Accuracy of a Smartphone-Based Artificial Intelligence Application for Classification of Melanomas, Melanocytic Nevi, and Seborrheic Keratoses | Diagnostics | 3.992 | Y | Y |
| 062 | Hair removal in dermoscopy images using variational autoencoders | Skin research and technology | 2.240 | Y | N |
| 063 | Lesion identification and malignancy prediction from clinical dermatological images | Scientific reports | 4.997 | Y | Y |
| 064 | Ms RED: A novel multi-scale residual encoding and decoding network for skin lesion segmentation | Medical image analysis | 13.828 | Y | Y |
| 065 | Data-Efficient Sensor Upgrade Path Using Knowledge Distillation | Sensors | 3.900 | N | N |
| 066 | Quantitative active super-resolution thermal imaging: The melanoma case study | Biomolecular concepts | 0.730 | N | N |
| 067 | Ensemble Method of Convolutional Neural Networks with Directed Acyclic Graph Using Dermoscopic Images: Melanoma Detection Application | Sensors | 3.900 | Y | Y |
| 068 | Incorporating clinical knowledge with constrained classifier chain into a multimodal deep network for melanoma detection | Computers in biology and medicine | 6.698 | Y | Y |
| 069 | NDB-UFES: An oral cancer and leukoplakia dataset composed of histopathological images and patient data | Data in brief | 0.131 | N | N |
| 070 | A Novel Approach for the Shape Characterisation of Non-Melanoma Skin Lesions Using Elliptic Fourier Analyses and Clinical Images | Journal of clinical medicine | 4.964 | Y | Y |
| 071 | Multiclass skin lesion localization and classification using deep learning based features fusion and selection framework for smart healthcare | Neural networks : the official journal of the International Neural Network Society | N/A | Y | Y |
| 072 | Light-Dermo: A Lightweight Pretrained Convolution Neural Network for the Diagnosis of Multiclass Skin Lesions | Diagnostics | 3.992 | Y | Y |
| 073 | Evaluation of Computer-Aided Detection (CAD) in Screening Automated Breast Ultrasound Based on Characteristics of CAD Marks and False-Positive Marks | Diagnostics | 3.992 | N | N |
| 074 | Homology-Based Image Processing for Automatic Classification of Histopathological Images of Lung Tissue | Cancers | 6.575 | N | N |
| 075 | Multi-Task and Few-Shot Learning-Based Fully Automatic Deep Learning Platform for Mobile Diagnosis of Skin Diseases | IEEE journal of biomedical and health informatics | 7.021 | N | N |
| 076 | A Multi-Feature Fusion Framework for Automatic Skin Cancer Diagnostics | Diagnostics | 3.992 | Y | Y |
| 077 | Improving Skin cancer Management with ARTificial Intelligence (SMARTI): protocol for a preintervention/postintervention trial of an artificial intelligence system used as a diagnostic aid for skin cancer management in a specialist dermatology setting | BMJ open | 3.007 | Y | N |
| 078 | Computer Aided Diagnosis of Melanoma Using Deep Neural Networks and Game Theory: Application on Dermoscopic Images of Skin Lesions | International journal of molecular sciences | 6.208 | Y | Y |
| 079 | Deeply Supervised Skin Lesions Diagnosis with Stage and Branch Attention | IEEE journal of biomedical and health informatics | 7.021 | Y | Y |
| 080 | The Role in Teledermoscopy of an Inexpensive and Easy-to-Use Smartphone Device for the Classification of Three Types of Skin Lesions Using Convolutional Neural Networks | Diagnostics | 3.992 | Y | Y |
| 081 | Validation of a Market-Approved Artificial Intelligence Mobile Health App for Skin Cancer Screening: A Prospective Multicenter Diagnostic Accuracy Study | Dermatology (Basel, Switzerland) | N/A | Y | Y |
| 082 | Non-melanoma skin cancer diagnosis: a comparison between dermoscopic and smartphone images by unified visual and sonification deep learning algorithms | Journal of cancer research and clinical oncology | 4.322 | Y | Y |
| 083 | The Classification of Six Common Skin Diseases Based on Xiangya-Derm: Development of a Chinese Database for Artificial Intelligence | Journal of medical Internet research | 7.077 | Y | Y |
| 084 | Evaluation of Erythema Severity in Dermatoscopic Images of Canine Skin: Erythema Index Assessment and Image Sampling Reliability | Sensors | 3.900 | N | N |
| 085 | Performance of a deep neural network in teledermatology: a single-centre prospective diagnostic study | Journal of the European Academy of Dermatology and Venereology : JEADV | 9.228 | Y | Y |
| 086 | Comparative study on artificial intelligence systems for detecting early esophageal squamous cell carcinoma between narrow-band and white-light imaging | World journal of gastroenterology | 5.374 | N | N |
| 087 | Imtidad: A Reference Architecture and a Case Study on Developing Distributed AI Services for Skin Disease Diagnosis over Cloud, Fog and Edge | Sensors | 3.900 | Y | Y |
| 088 | Over-Detection of Melanoma-Suspect Lesions by a CE-Certified Smartphone App: Performance in Comparison to Dermatologists, 2D and 3D Convolutional Neural Networks in a Prospective Data Set of 1204 Pigmented Skin Lesions Involving Patients’ Perception | Cancers | 6.575 | Y | Y |
| 089 | Design and validation of a new machine-learning-based diagnostic tool for the differentiation of dermatoscopic skin cancer images | PloS one | 3.752 | Y | Y |
| 090 | A CAD system for automatic dysplasia grading on H&E cervical whole-slide images | Scientific reports | 4.997 | N | N |
| 091 | Artificial intelligence-based computer-aided diagnosis system supports diagnosis of lymph node metastasis in esophageal squamous cell carcinoma: A multicenter study | Heliyon | 3.776 | N | N |
| 092 | Reimagining leprosy elimination with AI analysis of a combination of skin lesion images with demographic and clinical data | Lancet regional health. Americas | N/A | Y | Y |
| 093 | A smart LED therapy device with an automatic facial acne vulgaris diagnosis based on deep learning and internet of things application | Computers in biology and medicine | 6.698 | Y | Y |
| 094 | Stain color translation of multi-domain OSCC histopathology images using attention gated cGAN | Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society | 7.422 | N | N |
| 095 | AI-based smartphone apps for risk assessment of skin cancer need more evaluation and better regulation | British journal of cancer | 9.082 | N | N |
| 096 | Dermatoscopy of combined blue nevi: a multicentre study of the International Dermoscopy Society | Journal of the European Academy of Dermatology and Venereology : JEADV | 9.228 | Y | Y |
| 097 | Bridging the digital divide among advanced age skin cancer patients | Journal of plastic, reconstructive & aesthetic surgery : JPRAS | N/A | N | N |
| 098 | Melanoma: update on dermatoscopy, artificial intelligence for diagnosis, histopathology, genetics, surgery and systemic medical treatment | Italian journal of dermatology and venereology | N/A | N | N |
| 099 | Dermatoscopy of Cutaneous Lichen Planus Attempt to Translate Metaphoric Terminology Into Descriptive Terminology | Dermatology practical & conceptual | N/A | N | N |
| 100 | Experiences Regarding Use and Implementation of Artificial Intelligence-Supported Follow-Up of Atypical Moles at a Dermatological Outpatient Clinic: Qualitative Study | JMIR dermatology | N/A | N | N |
| 101 | Applying an adaptive Otsu-based initialization algorithm to optimize active contour models for skin lesion segmentation | Journal of X-ray science and technology | 2.442 | Y | N |
| 102 | MNet-10: A robust shallow convolutional neural network model performing ablation study on medical images assessing the effectiveness of applying optimal data augmentation technique | Frontiers in medicine | 4.900 | N | N |
| 103 | An attention-based weakly supervised framework for spitzoid melanocytic lesion diagnosis in whole slide images | Artificial intelligence in medicine | 7.011 | N | N |
| 104 | Ex vivo fluorescent confocal microscopy images of oral mucosa: Tissue atlas and evaluation of the learning curve | Journal of biophotonics | 3.390 | N | N |
| 105 | The dermoscopic inverse approach significantly improves the accuracy of human readers for lentigo maligna diagnosis | Journal of the American Academy of Dermatology | 15.487 | N | N |
| 106 | Perilesional sun damage as a diagnostic clue for pigmented actinic keratosis and Bowen's disease | Journal of the European Academy of Dermatology and Venereology : JEADV | 9.228 | N | N |
| 107 | The need for action by evaluators and decision makers in Europe to ensure safe use of medical software | Frontiers in medical technology | N/A | N | N |
| 108 | Agreement Between Experts and an Untrained Crowd for Identifying Dermoscopic Features Using a Gamified App: Reader Feasibility Study | JMIR medical informatics | N/A | N | N |
| 109 | Assessing the Potential for Patient-led Surveillance After Treatment of Localized Melanoma (MEL-SELF): A Pilot Randomized Clinical Trial | JAMA dermatology | 11.816 | N | N |
| 110 | Response to "Reply to 'A deep learning-based smartphone platform for cutaneous lupus erythematosus classification assistance: Simplifying the diagnosis of complicated diseases.' Has the complicated disease been simplified too much?" Artificial intelligence system is helpful for diagnosis of cutaneous lupus erythematosus | Journal of the American Academy of Dermatology | 15.487 | N | N |
| 111 | A prospective, randomized, single-blinded, crossover trial to investigate the effect of a wearable device in addition to a daily symptom diary for the remote early detection of SARS-CoV-2 infections (COVID-RED): a structured summary of a study protocol for a randomized controlled trial | Trials | 2.728 | N | N |
| 112 | A prospective, randomized, single-blinded, crossover trial to investigate the effect of a wearable device in addition to a daily symptom diary for the Remote Early Detection of SARS-CoV-2 infections (COVID-RED): a structured summary of a study protocol for a randomized controlled trial | Trials | 2.728 | N | N |
Cochrane
Following the PICO format and using the Cochrane advanced search tool, we have defined a global query to retrieve the articles whose title, abstract, or keywords relate to the query:
("dermatosis" OR "skin cancer" OR "chronic skin conditions" OR "inflammatory skin diseases" OR "malignant skin lesions" OR "pigmented skin lesions" OR "melanoma" OR "basal cell carcinoma" OR "squamous cell carcinoma" OR "atypical nevus" OR "acne" OR "psoriasis" OR "urticaria" OR "atopic dermatitis" OR "onychomycosis" OR "melasma" OR "solar lentigo" OR "dermatofibroma" OR "skin diseases" OR "skin lesions") AND ("clinical image" OR "digital imaging" OR "web application" OR "smartphone" OR "dermatoscopy" OR "camera" OR "CAD") AND ("artificial intelligence" OR "machine learning" OR "deep learning" OR "computer vision" OR "deep neural networks" OR "convolutional neural networks" OR "metaoptima" OR "automated") AND ("diagnosis" OR "diagnosis support" OR "followup" OR "segmentation" OR "detection" OR "estimation" OR "classification" OR "assessment" OR "severity assessment" OR "improving")
As a result, we have compiled 14 articles of which 1 articles have passed the Data Screening:
The editable table is here: https://docs.google.com/spreadsheets/d/1QrUi-Q0REVfh-X6YBD8ExUm0WsYbRoXZXyodLp_fJIo/edit?usp=sharing
| ID | Name | Journal/Conference | Impact factor | In scope? Not duplicated? | Appraisal included |
|---|---|---|---|---|---|
| 173 | Menopausal status, ultrasound and biomarker tests in combination for the diagnosis of ovarian cancer in symptomatic women | Cochrane Database of Systematic Reviews | N/A | N | N |
| 174 | Clinical assessment for the detection of oral cavity cancer and potentially malignant disorders in apparently healthy adults | Cochrane Database of Systematic Reviews | N/A | N | N |
| 175 | Mobile phone-based interventions for improving contraception use | Cochrane Database of Systematic Reviews | N/A | N | N |
| 176 | Electronic symptom monitoring for patients with advanced cancer | The Cochrane Database of Systematic Reviews | N/A | N | N |
| 177 | Dermoscopy Augmented Histology Trial, Consensus Agreement Diagnosis Made by Dermatopathology Experts | Not-published yet | N/A | N | N |
| 178 | Integrated Basic Science Within the Instructional Design of Pattern Recognition Training | Not-published yet | N/A | N | N |
| 179 | At-Home Dermoscopy Artificial Intelligence for Optimizing Early Triage of Skin Cancer | Not-published yet | N/A | N | N |
| 180 | AI Augmented Training for Skin Specialists | Not-published yet | N/A | N | N |
| 181 | Assessment of deep neural networks for the diagnosis of benign and malignant skin neoplasms in comparison with dermatologists: A retrospective validation study | PLoS medicine | N/A | N | N |
| 182 | Perilesional sun damage as a diagnostic clue for pigmented actinic keratosis and Bowen's disease | Journal of the European Academy of Dermatology and Venereology | N/A | N | N |
| 183 | Dermoscopy Augmented Histology Trial | Not-published yet | N/A | N | N |
| 184 | Development and assessment of an artificial intelligence-based tool for skin condition diagnosis by primary care physicians and nurse practitioners in teledermatology practices | JAMA network open | N/A | Y | Y |
| 185 | Study of AI Chromoendoscopy system for detection of early stage esophageal squamous cell carcinoma: a single-center, randomized, open-label, controlled, clinical trials | N/A | N | N | |
| 186 | Coronavirus (COVID-19): remote care through telehealth | Journal of Primary Health Care | N/A | N | N |
Google Scholar
Since the Google Scholar search tool does not allow as much detail as PubMed and Cochrane, we have defined 3 different queries to retrieve the articles. From the retrieved list of articles, sorted by relevance, we compile the first 20 results (2 pages):
Query 1: skin (lesion OR disease OR cancer) (detection OR classification)
As a result, 15 articles have passed the Data Screening:
| ID | Name | Journal/Conference | Impact factor | In scope? Not duplicated? | Appraisal included |
|---|---|---|---|---|---|
| 113 | Skin cancer detection: a review using deep learning techniques | International journal of environmental research and public health | 4.614 | Y | Y |
| 114 | A machine learning model for skin disease classification using convolution neural network | International Journal of Computing | 0.313 | Y | N |
| 115 | Deep learning techniques for skin lesion analysis and melanoma cancer detection: a survey of state-of-the-art | Artificial Intelligence Review | 9.588 | N | N |
| 116 | Skin cancer classification via convolutional neural networks: systematic review of studies involving human experts | European Journal of Cancer | 10.002 | Y | Y |
| 117 | An enhanced technique of skin cancer classification using deep convolutional neural network with transfer learning models | Machine Learning with Applications | N/A | Y | Y |
| 118 | Multiclass skin cancer classification using EfficientNets-a first step towards preventing skin cancer | Neuroscience Informatics | N/A | Y | Y |
| 119 | Intelligence Skin Cancer Detection using IoT with a Fuzzy Expert System | In 2022 International Conference on Cyber Resilience (ICCR) | N/A | Y | Y |
| 120 | Machine learning and deep learning methods for skin lesion classification and diagnosis: a systematic review | Diagnostics | 3.992 | Y | Y |
| 121 | Skin lesion classification based on deep convolutional neural networks architectures | Journal of Applied Science and Technology Trends | N/A | Y | Y |
| 122 | Multi-class skin lesion detection and classification via teledermatology | IEEE journal of biomedical and health informatics | 7.021 | Y | Y |
| 123 | Monkeypox skin lesion detection using deep learning models: A feasibility study | arXiv | N/A | N | N |
| 124 | Intelligent skin cancer detection applying autoencoder, MobileNetV2 and spiking neural networks | Chaos, Solitons & Fractals | 9.922 | Y | Y |
| 125 | Skin lesion analyser: an efficient seven-way multi-class skin cancer classification using MobileNet | Advanced Machine Learning Technologies and Applications: Proceedings of AMLTA 2020 | N/A | Y | Y |
| 126 | Multiclass skin lesion classification using hybrid deep features selection and extreme learning machine | Sensors | 3.900 | Y | Y |
| 127 | Soft attention improves skin cancer classification performance | In Interpretability of Machine Intelligence in Medical Image Computing | N/A | Y | Y |
| 128 | An attention-based mechanism to combine images and metadata in deep learning models applied to skin cancer classification | IEEE journal of biomedical and health informatics | 7.021 | Y | Y |
| 129 | Uncertainty quantification in skin cancer classification using three-way decision-based Bayesian deep learning | Computers in biology and medicine | 6.698 | Y | Y |
| 130 | Skin cancer detection from dermoscopic images using deep learning and fuzzy k-means clustering | Microscopy research and technique | 2.893 | Y | N |
| 131 | A convolutional neural network framework for accurate skin cancer detection | Neural Processing Letters | 2.565 | N | N |
| 132 | Single model deep learning on imbalanced small datasets for skin lesion classification | IEEE transactions on medical imaging | 11.037 | Y | Y |
Query 2: skin (lesion OR disease OR cancer) (detection OR classification) smartphone
As a result, 7 articles have passed the Data Screening:
| ID | Name | Journal/Conference | Impact factor | In scope? Not duplicated? | Appraisal included |
|---|---|---|---|---|---|
| 133 | Review of smartphone mobile applications for skin cancer detection: what are the changes in availability, functionality, and costs to users over time? | International Journal of Dermatology | 3.204 | Y | N |
| 134 | A smartphone based application for skin cancer classification using deep learning with clinical images and lesion information | arXiv | N/A | N | N |
| 135 | Skin cancer diagnosis using convolutional neural networks for smartphone images: A comparative study | Journal of Radiation Research and Applied Sciences | N/A | Y | Y |
| 136 | Human monkeypox classification from skin lesion images with deep pre-trained network using mobile application | Journal of Medical Systems | 4.920 | N | N |
| 137 | Accuracy of commercially available smartphone applications for the detection of melanoma | The British Journal of Dermatology | 11.113 | N | N |
| 138 | A smartphone-based application for an early skin disease prognosis: Towards a lean healthcare system via computer-based vision | Advanced Engineering Informatics | 7.862 | Y | Y |
| 139 | Machine learning and deep learning methods for skin lesion classification and diagnosis: a systematic review | Diagnostics | 3.992 | N | N |
| 140 | New AI-algorithms on smartphones to detect skin cancer in a clinical setting—A validation study | Plos one | 3.752 | Y | Y |
| 141 | Artificial intelligence algorithm with SVM classification using dermascopic images for melanoma diagnosis | Journal of Artificial Intelligence and Capsule Networks | N/A | Y | Y |
| 142 | The development of skin lesion detection application in smart handheld devices using deep neural networks | Multimedia Tools and Applications | 2.577 | Y | N |
| 143 | Artificial intelligence and machine learning algorithms for early detection of skin cancer in community and primary care settings: a systematic review | The Lancet Digital Health | N/A | Y | Y |
| 144 | Skin cancer disease images classification using deep learning solutions | Multimedia Tools and Applications | 2.577 | Y | N |
| 145 | Over-Detection of Melanoma-Suspect Lesions by a CE-Certified Smartphone App: Performance in Comparison to Dermatologists, 2D and 3D Convolutional Neural Networks in a Prospective Data Set of 1204 Pigmented Skin Lesions Involving Patients’ Perception | Cancers | 6.575 | N | N |
| 146 | Artificial intelligence in the detection of skin cancer | Journal of the American Academy of Dermatology. | 15.487 | Y | Y |
| 147 | Real-time skin cancer detection using neural networks on an embedded device | Bachelor's thesis, University of Twente | N/A | N | N |
| 148 | A deep learning based multimodal fusion model for skin lesion diagnosis using smartphone collected clinical images and metadata | Frontiers in Surgery | 2.350 | N | N |
| 149 | Smartphone-based Skin Cancer Detection using Image Processing and Convolutional Neural Network | International Conference on Computing Communication and Networking Technologies (ICCCNT) | N/A | Y | Y |
| 150 | The role in teledermoscopy of an inexpensive and easy-to-use smartphone device for the classification of three types of skin lesions using convolutional neural networks | Diagnostics | 3.992 | N | N |
| 151 | Automatic skin disease diagnosis using deep learning from clinical image and patient information | Skin Health and Disease | N/A | N | N |
| 152 | A mobile augmented reality application for supporting real-time skin lesion analysis based on deep learning | Journal of Real-Time Image Processing | 2.293 | Y | N |
Query 3: skin (lesion OR disease OR cancer) diagnosis (cnn OR convolutional OR deep learning OR artificial intelligence)
As a result, 4 articles have passed the Data Screening:
| ID | Name | Journal/Conference | Impact factor | In scope? Not duplicated? | Appraisal included |
|---|---|---|---|---|---|
| 153 | DSCC_Net: Multi-Classification Deep Learning Models for Diagnosing of Skin Cancer Using Dermoscopic Images | Cancers | 6.575 | Y | Y |
| 154 | Ovary cancer diagnosing empowered with machine learning | International Conference on Business Analytics for Technology and Security (ICBATS) | N/A | N | N |
| 155 | Deep Learning-Based Classification of Dermoscopic Images for Skin Lesions | Sakarya University Journal of Computer and Information Sciences | N/A | Y | Y |
| 156 | Deep Learning-Based Cancer Detection-Recent Developments, Trend and Challenges | CMES-Computer Modeling in Engineering & Sciences | 2.027 | Y | N |
| 157 | The classification of six common skin diseases based on Xiangya-Derm: development of a Chinese database for artificial intelligence | Journal of Medical Internet Research | 7.077 | N | N |
| 158 | Detection of melanoma with hybrid learning method by removing hair from dermoscopic images using image processing techniques and wavelet transform | Biomedical Signal Processing and Control | 5.076 | Y | Y |
| 159 | A fully automated approach involving neuroimaging and deep learning for Parkinson’s disease detection and severity prediction | PeerJ Computer Science | 2.411 | N | N |
| 160 | Deep learning as a new tool in the diagnosis of mycosis fungoides | Archives of Dermatological Research | 3.033 | N | N |
| 161 | Use of deep learning approaches in cancer diagnosis | Deep Learning for Cancer Diagnosis | N/A | N | N |
| 162 | Cancer detection in breast cells using a hybrid method based on deep complex neural network and data mining | Journal of Cancer Research and Clinical Oncology | 4.322 | N | N |
| 163 | Crccn-net: Automated framework for classification of colorectal tissue using histopathological images | Biomedical Signal Processing and Control | 5.076 | N | N |
| 164 | PoxNet22: A fine-tuned model for the classification of monkeypox disease using transfer learning | IEEE Access | 3.476 | Y | N |
| 165 | MITNET: a novel dataset and a two-stage deep learning approach for mitosis recognition in whole slide images of breast cancer tissue | Neural Computing and Applications | 5.102 | N | N |
| 166 | DermoCC-GAN: A new approach for standardizing dermatological images using generative adversarial networks | Computer Methods and Programs in Biomedicine | 7.027 | Y | Y |
| 167 | Deep learning models for cancer stem cell detection: a brief review | Frontiers in Immunology | 8.787 | N | N |
| 168 | RADIC: A tool for diagnosing COVID-19 from chest CT and X-ray scans using deep learning and quad-radiomics | Chemometrics and Intelligent Laboratory Systems | 4.175 | N | N |
| 169 | Tubule-U-Net: a novel dataset and deep learning-based tubule segmentation framework in whole slide images of breast cancer | Scientific Reports | 4.997 | N | N |
| 170 | The Deep Learning Method Differentiates Patients with Bipolar Disorder from Controls with High Accuracy Using EEG Data | Clinical EEG and Neuroscience | 2.046 | N | N |
| 171 | A Novel Deep Learning-Based Approach for Segmentation of Different Type Caries Lesions on Panoramic Radiographs | Diagnostics | 3.992 | N | N |
| 172 | Artificial intelligence in oncology: From bench to clinic | Seminars in Cancer Biology (Vol. 84, pp. 113-128). Academic Press. | 17.012 | N | N |
Cited articles
Two articles, cited by the article with ID 14, have passed the Data Screening:
| ID | Name | Journal/Conference | Impact factor | In scope? Not duplicated? | Appraisal included |
|---|---|---|---|---|---|
| 187 | A survey on deep learning for skin lesion segmentation | Medical Image Analysis | 13.828 | Y | Y |
| 188 | Characteristics of publicly available skin cancer image datasets: a systematic review | The Lancet Digital Health | N/A | Y | Y |
Data appraisal
All the articles that passed the Data Screening have been reviewed thoroughly to assess their Quality, Relevance, and Contribution. As a result:
- 31 articles have been evaluated as highly relevant.
- 25 articles have been evaluated as relevant.
- 25 articles have been evaluated as non-relevant.
The editable table is here: https://docs.google.com/spreadsheets/d/1eIGPeoYd3BqZFDWA8h6jRL36UBbsy0J6nNunvM-g12g/edit?usp=sharing
| ID | Name | Authors | Journal/Conference | Q (<40) | R (<30) | C (<30) | Weighted value |
|---|---|---|---|---|---|---|---|
| 001 | Explainable artificial intelligence in skin cancer recognition: A systematic review | Hauser, K., Kurz, A., Haggenmueller, S., Maron, R. C., von Kalle, C., Utikal, J. S., ... & Brinker, T. J. | European Journal of Cancer | 40 | 20 | 10 | 70 |
| 004 | DermIA: Machine Learning to Improve Skin Cancer Screening | Shoen, Ezra; Shoen, Ezra | Journal of digital imaging | 0 | 20 | 5 | 25 |
| 007 | Non-Melanoma Skin Cancer Detection in the Age of Advanced Technology: A Review | Stafford, Haleigh; Buell, Jane; Yaniv, Dan | Cancers | 30 | 25 | 15 | 70 |
| 009 | Telemedicine and e-Health in the Management of Psoriasis: Improving Patient Outcomes A Narrative Review | Havelin, Alison; Hampton, Philip; Hampton, Philip | Psoriasis (Auckland, N.Z.) | 25 | 20 | 5 | 50 |
| 013 | Artificial intelligence for the automated single-shot assessment of psoriasis severity | Okamoto, T; Kawai, M; Kawamura, T | Journal of the European Academy of Dermatology and Venereology : JEADV | 10 | 5 | 5 | 20 |
| 014 | A survey, review, and future trends of skin lesion segmentation and classification | Hasan, Md Kamrul; Ahamad, Md Asif; Yang, Guang | Computers in biology and medicine | 38 | 27 | 10 | 75 |
| 015 | Digital skin imaging applications, part I: Assessment of image acquisition technique features | Sun, Mary D; Kentley, Jonathan; Halpern, Allan C | Skin Research and Technology | 35 | 25 | 25 | 85 |
| 016 | Melanoma segmentation using deep learning with test-time augmentations and conditional random fields | Ashraf, Hassan; Waris, Asim; Niazi, Imran Khan | Scientific reports | 35 | 25 | 15 | 75 |
| 018 | DenseNet-II: an improved deep convolutional neural network for melanoma cancer detection | Girdhar, Nancy; Sinha, Aparna; Gupta, Shivang | Soft computing | 5 | 20 | 0 | 25 |
| 019 | Automatic skin disease diagnosis using deep learning from clinical image and patient information | Muhaba, K A; Dese, K; Simegn, G L | Skin health and disease | 35 | 30 | 10 | 75 |
| 020 | An interpretable CNN-based CAD system for skin lesion diagnosis | López-Labraca, Javier; González-Díaz, Iván; Fueyo-Casado, Alejandro | Artificial intelligence in medicine | 40 | 25 | 25 | 90 |
| 021 | A cell phone app for facial acne severity assessment | Wang, Jiaoju; Luo, Yan; Zhang, Jianglin | Applied intelligence (Dordrecht, Netherlands) | 40 | 28 | 28 | 96 |
| 025 | Monitoring of Pigmented Skin Lesions Using 3D Whole Body Imaging | Ahmedt-Aristizabal, David; Nguyen, Chuong; Wang, Dadong | Computer methods and programs in biomedicine | 20 | 5 | 10 | 35 |
| 027 | Machine learning based skin lesion segmentation method with novel borders and hair removal techniques | Rehman, Mohibur; Ali, Mushtaq; Mustafa Hilal, Anwer | PloS one | 20 | 20 | 10 | 50 |
| 030 | Convolutional neural network assistance significantly improves dermatologists' diagnosis of cutaneous tumours using clinical images | Ba, Wei; Wu, Huan; Li, Cheng X | European journal of cancer (Oxford, England : 1990) | 35 | 30 | 8 | 73 |
| 031 | Skin Lesion Segmentation Using an Ensemble of Different Image Processing Methods | Tamoor, Maria; Naseer, Asma; Zafar, Kashif | Diagnostics | 15 | 15 | 5 | 35 |
| 032 | Attention Cost-Sensitive Deep Learning-Based Approach for Skin Cancer Detection and Classification | Ravi, Vinayakumar; Ravi, Vinayakumar | Cancers | 15 | 10 | 0 | 25 |
| 034 | ExAID: A multimodal explanation framework for computer-aided diagnosis of skin lesions | Lucieri, Adriano; Bajwa, Muhammad Naseer; Ahmed, Sheraz | Computer methods and programs in biomedicine | 30 | 25 | 20 | 75 |
| 035 | Automatic Acne Object Detection and Acne Severity Grading Using Smartphone Images and Artificial Intelligence | Huynh, Quan Thanh; Nguyen, Phuc Hoang; Ngo, Hoan Thanh | Diagnostics | 15 | 25 | 10 | 50 |
| 037 | An IoMT-Based Melanoma Lesion Segmentation Using Conditional Generative Adversarial Networks | Ali, Zeeshan; Naz, Sheneela; Kim, Yongsung | Sensors | 15 | 20 | 10 | 45 |
| 039 | Human Monkeypox Classification from Skin Lesion Images with Deep Pre-trained Network using Mobile Application | Sahin, Veysel Harun; Oztel, Ismail; Yolcu Oztel, Gozde | Journal of medical systems | 5 | 15 | 0 | 20 |
| 042 | Melanoma classification from dermatoscopy images using knowledge distillation for highly imbalanced data | Adepu, Anil Kumar; Sahayam, Subin; Arramraju, Rashmika | Computers in biology and medicine | 35 | 25 | 25 | 85 |
| 044 | A rotation meanout network with invariance for dermoscopy image classification and retrieval | Zhang, Yilan; Xie, Fengying; Liu, Jie | Computers in biology and medicine | 25 | 20 | 15 | 60 |
| 045 | Implementation of artificial intelligence algorithms for melanoma screening in a primary care setting | Giavina-Bianchi, Mara; de Sousa, Raquel Machado; Machado, Birajara Soares | PloS one | 35 | 25 | 15 | 75 |
| 047 | A Deep CNN Transformer Hybrid Model for Skin Lesion Classification of Dermoscopic Images Using Focal Loss | Nie, Yali; Sommella, Paolo; Lundgren, Jan | Diagnostics | 10 | 20 | 5 | 35 |
| 051 | A novel approach toward skin cancer classification through fused deep features and neutrosophic environment | Abdelhafeez, Ahmed; Mohamed, Hoda K; Khalil, Nariman A | Frontiers in public health | 5 | 10 | 5 | 20 |
| 053 | A machine learning-based, decision support, mobile phone application for diagnosis of common dermatological diseases | Pangti, R; Mathur, J; Gupta, S | Journal of the European Academy of Dermatology and Venereology : JEADV | 35 | 30 | 10 | 75 |
| 055 | Development and Clinical Evaluation of an Artificial Intelligence Support Tool for Improving Telemedicine Photo Quality | Vodrahalli, Kailas; Ko, Justin; Daneshjou, Roxana | JAMA dermatology | 30 | 30 | 15 | 75 |
| 056 | The Role of Machine Learning and Deep Learning Approaches for the Detection of Skin Cancer | Mazhar, Tehseen; Haq, Inayatul; Goh, Lucky Poh Wah | Healthcare (Basel, Switzerland) | 15 | 10 | 0 | 25 |
| 058 | Automatic identification of benign pigmented skin lesions from clinical images using deep convolutional neural network | Ding, Hui; Zhang, Eejia; Lin, Tong | BMC biotechnology | 20 | 25 | 5 | 50 |
| 061 | Accuracy of a Smartphone-Based Artificial Intelligence Application for Classification of Melanomas, Melanocytic Nevi, and Seborrheic Keratoses | Liutkus, Jokubas; Kriukas, Arturas; Valiukeviciene, Skaidra | Diagnostics | 25 | 20 | 5 | 50 |
| 063 | Lesion identification and malignancy prediction from clinical dermatological images | Xia, Meng; Kheterpal, Meenal K; Henao, Ricardo | Scientific reports | 25 | 20 | 10 | 55 |
| 064 | Ms RED: A novel multi-scale residual encoding and decoding network for skin lesion segmentation | Dai, Duwei; Dong, Caixia; Luo, Nana | Medical image analysis | 35 | 25 | 20 | 80 |
| 067 | Ensemble Method of Convolutional Neural Networks with Directed Acyclic Graph Using Dermoscopic Images: Melanoma Detection Application | Foahom Gouabou, Arthur Cartel; Damoiseaux, Jean-Luc; Merad, Djamal | Sensors | 10 | 10 | 10 | 30 |
| 068 | Incorporating clinical knowledge with constrained classifier chain into a multimodal deep network for melanoma detection | Wang, Yuheng; Cai, Jiayue; Lee, Tim K | Computers in biology and medicine | 15 | 10 | 5 | 30 |
| 070 | A Novel Approach for the Shape Characterisation of Non-Melanoma Skin Lesions Using Elliptic Fourier Analyses and Clinical Images | Courtenay, Lloyd A; Barbero-García, Inés; Román-Curto, Concepción | Journal of clinical medicine | 10 | 5 | 5 | 20 |
| 071 | Multiclass skin lesion localization and classification using deep learning based features fusion and selection framework for smart healthcare | Maqsood, Sarmad; Damaševičius, Robertas; Damaševičius, Robertas | Neural networks : the official journal of the International Neural Network Society | 5 | 5 | 5 | 15 |
| 072 | Light-Dermo: A Lightweight Pretrained Convolution Neural Network for the Diagnosis of Multiclass Skin Lesions | Baig, Abdul Rauf; Abbas, Qaisar; Ahmed, Alaa E S | Diagnostics | 15 | 15 | 10 | 40 |
| 076 | A Multi-Feature Fusion Framework for Automatic Skin Cancer Diagnostics | Bakheet, Samy; Alsubai, Shtwai; Alqahtani, Abdullah | Diagnostics | 25 | 15 | 10 | 50 |
| 078 | Computer Aided Diagnosis of Melanoma Using Deep Neural Networks and Game Theory: Application on Dermoscopic Images of Skin Lesions | Foahom Gouabou, Arthur Cartel; Collenne, Jules; Merad, Djamal | International journal of molecular sciences | 25 | 20 | 20 | 65 |
| 079 | Deeply Supervised Skin Lesions Diagnosis with Stage and Branch Attention | Dai, Wei; Liu, Rui; Liu, Jun | IEEE journal of biomedical and health informatics | 30 | 25 | 20 | 75 |
| 080 | The Role in Teledermoscopy of an Inexpensive and Easy-to-Use Smartphone Device for the Classification of Three Types of Skin Lesions Using Convolutional Neural Networks | Veronese, Federica; Branciforti, Francesco; Savoia, Paola | Diagnostics | 30 | 25 | 20 | 75 |
| 081 | Validation of a Market-Approved Artificial Intelligence Mobile Health App for Skin Cancer Screening: A Prospective Multicenter Diagnostic Accuracy Study | Sangers, Tobias; Reeder, Suzan; Wakkee, Marlies | Dermatology (Basel, Switzerland) | 35 | 30 | 15 | 80 |
| 082 | Non-melanoma skin cancer diagnosis: a comparison between dermoscopic and smartphone images by unified visual and sonification deep learning algorithms | Dascalu, A; Walker, B N; David, E O | Journal of cancer research and clinical oncology | 30 | 10 | 5 | 45 |
| 083 | The Classification of Six Common Skin Diseases Based on Xiangya-Derm: Development of a Chinese Database for Artificial Intelligence | Huang, Kai; Jiang, Zixi; Zhao, Shuang | Journal of medical Internet research | 30 | 30 | 20 | 80 |
| 085 | Performance of a deep neural network in teledermatology: a single-centre prospective diagnostic study | Muñoz-López, C; Ramírez-Cornejo, C; Navarrete-Dechent, C | Journal of the European Academy of Dermatology and Venereology : JEADV | 35 | 30 | 10 | 75 |
| 087 | Imtidad: A Reference Architecture and a Case Study on Developing Distributed AI Services for Skin Disease Diagnosis over Cloud, Fog and Edge | Janbi, Nourah; Mehmood, Rashid; Yigitcanlar, Tan | Sensors | 40 | 20 | 20 | 80 |
| 088 | Over-Detection of Melanoma-Suspect Lesions by a CE-Certified Smartphone App: Performance in Comparison to Dermatologists, 2D and 3D Convolutional Neural Networks in a Prospective Data Set of 1204 Pigmented Skin Lesions Involving Patients’ Perception | Jahn, Anna Sophie; Navarini, Alexander Andreas; Maul, Lara Valeska | Cancers | 15 | 5 | 0 | 20 |
| 089 | Design and validation of a new machine-learning-based diagnostic tool for the differentiation of dermatoscopic skin cancer images | Tajerian, Amin; Kazemian, Mohsen; Akhavan Malayeri, Ava | PloS one | 5 | 5 | 5 | 15 |
| 092 | Reimagining leprosy elimination with AI analysis of a combination of skin lesion images with demographic and clinical data | Barbieri, Raquel R; Xu, Yixi; Moraes, Milton O | Lancet regional health. Americas | 35 | 25 | 15 | 75 |
| 093 | A smart LED therapy device with an automatic facial acne vulgaris diagnosis based on deep learning and internet of things application | Phan, Duc Tri; Ta, Quoc Bao; Oh, Junghwan | Computers in biology and medicine | 30 | 10 | 20 | 60 |
| 096 | Dermatoscopy of combined blue nevi: a multicentre study of the International Dermoscopy Society | Stojkovic-Filipovic, J; Tiodorovic, D; Kittler, H | Journal of the European Academy of Dermatology and Venereology : JEADV | 30 | 20 | 10 | 60 |
| 113 | Skin cancer detection: a review using deep learning techniques | Dildar, M., Akram, S., Irfan, M., Khan, H. U., Ramzan, M., Mahmood, A. R., ... & Mahnashi, M. H. | International journal of environmental research and public health | 40 | 20 | 0 | 60 |
| 116 | Skin cancer classification via convolutional neural networks: systematic review of studies involving human experts | Haggenmüller, S., Maron, R. C., Hekler, A., Utikal, J. S., Barata, C., Barnhill, R. L., ... & Brinker, T. J. | European Journal of Cancer | 20 | 20 | 5 | 45 |
| 117 | An enhanced technique of skin cancer classification using deep convolutional neural network with transfer learning models | Ali, M. S., Miah, M. S., Haque, J., Rahman, M. M., & Islam, M. K. | Machine Learning with Applications | 5 | 10 | 0 | 15 |
| 118 | Multiclass skin cancer classification using EfficientNets-a first step towards preventing skin cancer | Ali, K., Shaikh, Z. A., Khan, A. A., & Laghari, A. A. | Neuroscience Informatics | 10 | 10 | 0 | 20 |
| 119 | Intelligence Skin Cancer Detection using IoT with a Fuzzy Expert System | Al-Dmour, N. A., Salahat, M., Nair, H. K., Kanwal, N., Saleem, M., & Aziz, N. | In 2022 International Conference on Cyber Resilience (ICCR) | 30 | 15 | 15 | 60 |
| 120 | Machine learning and deep learning methods for skin lesion classification and diagnosis: a systematic review | Kassem, M. A., Hosny, K. M., Damaševičius, R., & Eltoukhy, M. M. | Diagnostics | 35 | 20 | 5 | 60 |
| 121 | Skin lesion classification based on deep convolutional neural networks architectures | Saeed, J., & Zeebaree, S. | Journal of Applied Science and Technology Trends | 10 | 20 | 5 | 35 |
| 122 | Multi-class skin lesion detection and classification via teledermatology | Khan, M. A., Muhammad, K., Sharif, M., Akram, T., & de Albuquerque, V. H. C. | IEEE journal of biomedical and health informatics | 20 | 20 | 20 | 60 |
| 124 | Intelligent skin cancer detection applying autoencoder, MobileNetV2 and spiking neural networks | Toğaçar, M., Cömert, Z., & Ergen, B. | Chaos, Solitons & Fractals | 10 | 10 | 5 | 25 |
| 125 | Skin lesion analyser: an efficient seven-way multi-class skin cancer classification using MobileNet | Chaturvedi, S. S., Gupta, K., & Prasad, P. S. | Advanced Machine Learning Technologies and Applications: Proceedings of AMLTA 2020 | 5 | 10 | 0 | 15 |
| 126 | Multiclass skin lesion classification using hybrid deep features selection and extreme learning machine | Afza, F., Sharif, M., Khan, M. A., Tariq, U., Yong, H. S., & Cha, J. | Sensors | 10 | 10 | 5 | 25 |
| 127 | Soft attention improves skin cancer classification performance | Datta, S. K., Shaikh, M. A., Srihari, S. N., & Gao, M. | In Interpretability of Machine Intelligence in Medical Image Computing | 25 | 25 | 25 | 75 |
| 128 | An attention-based mechanism to combine images and metadata in deep learning models applied to skin cancer classification | Pacheco, A. G., & Krohling, R. A. | IEEE journal of biomedical and health informatics | 25 | 25 | 25 | 75 |
| 129 | Uncertainty quantification in skin cancer classification using three-way decision-based Bayesian deep learning | Abdar, M., Samami, M., Mahmoodabad, S. D., Doan, T., Mazoure, B., Hashemifesharaki, R., ... & Nahavandi, S. | Computers in biology and medicine | 35 | 25 | 20 | 80 |
| 132 | Single model deep learning on imbalanced small datasets for skin lesion classification | Yao, P., Shen, S., Xu, M., Liu, P., Zhang, F., Xing, J., ... & Xu, R. X. | IEEE transactions on medical imaging | 35 | 30 | 30 | 95 |
| 135 | Skin cancer diagnosis using convolutional neural networks for smartphone images: A comparative study | Medhat, S., Abdel-Galil, H., Aboutabl, A. E., & Saleh, H. | Journal of Radiation Research and Applied Sciences | 5 | 5 | 0 | 10 |
| 138 | A smartphone-based application for an early skin disease prognosis: Towards a lean healthcare system via computer-based vision | Shahin, M., Chen, F. F., Hosseinzadeh, A., Koodiani, H. K., Shahin, A., & Nafi, O. A. | Advanced Engineering Informatics | 10 | 5 | 5 | 20 |
| 140 | New AI-algorithms on smartphones to detect skin cancer in a clinical setting—A validation study | Kränke, T., Tripolt-Droschl, K., Röd, L., Hofmann-Wellenhof, R., Koppitz, M., & Tripolt, M. | Plos one | 35 | 30 | 10 | 75 |
| 141 | Artificial intelligence algorithm with SVM classification using dermascopic images for melanoma diagnosis | Balasubramaniam, V. | Journal of Artificial Intelligence and Capsule Networks | 1 | 5 | 5 | 11 |
| 143 | Artificial intelligence and machine learning algorithms for early detection of skin cancer in community and primary care settings: a systematic review | Jones, O. T., Matin, R. N., van der Schaar, M., Bhayankaram, K. P., Ranmuthu, C. K. I., Islam, M. S., ... & Walter, F. M. | The Lancet Digital Health | 35 | 20 | 20 | 75 |
| 146 | Artificial intelligence in the detection of skin cancer | Beltrami, E. J., Brown, A. C., Salmon, P. J., Leffell, D. J., Ko, J. M., & Grant-Kels, J. M. | Journal of the American Academy of Dermatology. | 35 | 20 | 0 | 55 |
| 149 | Smartphone-based Skin Cancer Detection using Image Processing and Convolutional Neural Network | Rahman, S., Raihan, M., & Mithila, S. K. | International Conference on Computing Communication and Networking Technologies (ICCCNT) | 10 | 10 | 0 | 20 |
| 153 | DSCC_Net: Multi-Classification Deep Learning Models for Diagnosing of Skin Cancer Using Dermoscopic Images | Tahir, M., Naeem, A., Malik, H., Tanveer, J., Naqvi, R. A., & Lee, S. W. | Cancers | 10 | 10 | 5 | 25 |
| 155 | Deep Learning-Based Classification of Dermoscopic Images for Skin Lesions | SÖNMEZ, A. F., ÇAKAR, S., CEREZCİ, F., KOTAN, M., DELİBAŞOĞLU, İ., & Gülüzar, Ç. İ. T. | Sakarya University Journal of Computer and Information Sciences | 10 | 10 | 5 | 25 |
| 158 | Detection of melanoma with hybrid learning method by removing hair from dermoscopic images using image processing techniques and wavelet transform | Suiçmez, Ç., Kahraman, H. T., Suiçmez, A., Yılmaz, C., & Balcı, F. | Biomedical Signal Processing and Control | 10 | 10 | 5 | 25 |
| 166 | DermoCC-GAN: A new approach for standardizing dermatological images using generative adversarial networks | Salvi, M., Branciforti, F., Veronese, F., Zavattaro, E., Tarantino, V., Savoia, P., & Meiburger, K. M. | Computer Methods and Programs in Biomedicine | 30 | 20 | 15 | 65 |
| 184 | Development and assessment of an artificial intelligence-based tool for skin condition diagnosis by primary care physicians and nurse practitioners in teledermatology practices | Jain, A., Way, D., Gupta, V., Gao, Y., de Oliveira Marinho, G., Hartford, J., ... & Liu, Y. | JAMA network open | 35 | 30 | 10 | 75 |
| 187 | A survey on deep learning for skin lesion segmentation | Mirikharaji, Z., Abhishek, K., Bissoto, A., Barata, C., Avila, S., Valle, E., ... & Hamarneh, G. | Medical Image Analysis | 40 | 30 | 20 | 90 |
| 188 | Characteristics of publicly available skin cancer image datasets: a systematic review | Wen, D., Khan, S. M., Xu, A. J., Ibrahim, H., Smith, L., Caballero, J., ... & Matin, R. N. | The Lancet Digital Health | 40 | 30 | 10 | 80 |
Literature Review results - Facial Palsy
Screening
For each one of the PICO components we have defined the set of keywords as:
| Keywords | |
|---|---|
| Population, Patient, Problem (P) | facial palsy, facial paralysis, facial nerve palsy |
| Intervention (I) | photo, video, expression, proportion, clinical image, smartphone, monitoring |
| Comparator (C) | artificial intelligence, machine learning, deep learning, computer vision, deep neural networks, convolutional neural networks, regression, neural network |
| Outcome (O) | prediction, assessment, severity assessment, improving |
In the following, we describe how we use these keywords to form queries for each source of information, the retrieved articles, and the outcome of the Data Screening. Since this screening process does not contain as many articles as the previous one, we do not filter out the compiled articles with the impact factor of their publication source.
PubMed
Following the PICO format and using the PubMed advanced search tool, we have defined a global query to retrieve the articles whose title or abstract relate to the query:
(("facial palsy") OR ("facial paralysis") OR ("facial nerve palsy")) AND ("Photo") OR ("video") OR ("expression") OR ("proportion") OR ("clinical image") OR ("smartphone") OR ("monitoring") AND ("artificial intelligence") OR ("machine learning") OR ("deep learning") OR ("computer vision") OR ("deep neural networks") OR ("convolutional neural networks") OR ("regression") OR ("neural network") AND (("prediction") OR ("assessment") OR ("severity assessment") OR ("improving"))
As a result, we have compiled 21 articles of which 20 articles have passed the Data Screening:
The editable table is here: https://docs.google.com/spreadsheets/d/1eIGPeoYd3BqZFDWA8h6jRL36UBbsy0J6nNunvM-g12g/edit?usp=sharing
| ID | Name | Journal/Conference | In scope? Not duplicated? | Appraisal included |
|---|---|---|---|---|
| 001 | Reliability and validity of emotrics in the assessment of facial palsy. | Journal of personalized medicine | Y | Y |
| 002 | Artificial intelligence-driven video analysis for novel outcome measures after smile reanimation surgery. | Facial plastic surgery & aesthetic medicine | Y | Y |
| 003 | Toward an automatic system for computer-aided assessment in facial palsy. | Facial plastic surgery & aesthetic medicine | Y | Y |
| 004 | Automated spontaneity assessment after smile reanimation: a machine learning approach. | Plastic and reconstructive surgery | Y | Y |
| 005 | A smartphone-based automatic diagnosis system for facial nerve palsy. | Sensors (Basel, Switzerland) | Y | Y |
| 006 | Machine learning models for predicting facial nerve palsy in parotid gland surgery for benign tumors. | The Journal of surgical research | Y | Y |
| 007 | Clinician-graded electronic facial paralysis assessment: the eface. | Plastic and reconstructive surgery | Y | Y |
| 008 | The research for the function evaluation of facial nerve and the mechanisms of rehabilitation training. | Medicine | N | N |
| 009 | Artificial neural network as a tool to predict facial nerve palsy in parotid gland surgery for benign tumors. | Medical sciences (Basel, Switzerland) | Y | Y |
| 010 | Prediction of long-term facial nerve outcomes with intraoperative nerve monitoring. | Otology & neurotology : official publication of the American Otological Society, American Neurotology Society [and] European Academy of Otology and Neurotology | Y | Y |
| 011 | The emerging role of artificial intelligence tools for outcome measurement in facial reanimation surgery: a review. | Facial plastic surgery & aesthetic medicine | Y | Y |
| 012 | A survey on computer vision for assistive medical diagnosis from faces. | IEEE journal of biomedical and health informatics | Y | Y |
| 013 | Functional outcome of dual reinnervation with cross-facial nerve graft and masseteric nerve transfer for facial paralysis. | Plastic and reconstructive surgery | Y | Y |
| 014 | Predicting perceived disfigurement from facial function in patients with unilateral paralysis. | Plastic and reconstructive surgery | Y | Y |
| 015 | Reliability between in-person and still photograph assessment of facial function in facial paralysis using the eface facial grading system. | Facial plastic surgery & aesthetic medicine | Y | Y |
| 016 | Objective method of assessing and presenting the house-brackmann and regional grades of facial palsy by production of a facogram. | Otology & neurotology : official publication of the American Otological Society, American Neurotology Society [and] European Academy of Otology and Neurotology | Y | Y |
| 017 | Facial reanimation surgery restores affect display. | Otology & neurotology : official publication of the American Otological Society, American Neurotology Society [and] European Academy of Otology and Neurotology | Y | Y |
| 018 | Deepstroke: an efficient stroke screening framework for emergency rooms with multimodal adversarial deep learning. | Medical image analysis | Y | Y |
| 019 | In the eye of the beholder: changes in perceived emotion expression after smile reanimation. | Plastic and reconstructive surgery | Y | Y |
| 020 | Quantitative analysis of facial paralysis using local binary patterns in biomedical videos. | IEEE transactions on bio-medical engineering | Y | Y |
| 021 | Objective outcomes analysis following microvascular gracilis transfer for facial reanimation: a review of 10 years' experience. | JAMA facial plastic surgery | Y | Y |
Cochrane
Following the PICO format and using the Cochrane advanced search tool, we have defined a global query to retrieve the articles whose title, abstract, or keywords relate to the query:
("facial palsy" OR "facial paralysis" OR "facial nerve palsy") AND ("Photo" OR "video" OR "expression" OR "proportion" OR "clinical image" OR "smartphone" OR "monitoring") AND ("artificial intelligence" OR "machine learning" OR "deep learning" OR "computer vision" OR "deep neural networks" OR "convolutional neural networks" OR "regression" OR "neural network") AND ("prediction" OR "assessment" OR "severity assessment" OR "improving")
As a result, we have compiled 4 articles of which 0 articles have passed the Data Screening:
| ID | Name | Journal/Conference | In scope? Not duplicated? | Appraisal included |
|---|---|---|---|---|
| 042 | Changing perception: facial reanimation surgery improves attractiveness and decreases negative facial perception. | The Laryngoscope | N | N |
| 043 | Changing perception: facial reanimation surgery improves attractiveness and decreases negative facial perception. | The Laryngoscope | N | N |
| 044 | A randomised controlled trial of the use of aciclovir and/or prednisolone for the early treatment of bell's palsy: the bells study. | -- | N | N |
| 045 | Efficacy of nerve blocks for episodic migraine. | -- | N | N |
Google Scholar
Since the Google Scholar search tool does not allow as much detail as PubMed and Cochrane, we have defined a simplified query to retrieve the articles. From the retrieved list of articles, sorted by relevance, we compile the first 20 results (2 pages):
Query: facial (palsy OR paralysis) (prediction OR assessment OR improving) (regression OR machine learning OR computer vision OR neural network OR artificial intelligence)
As a result, 10 articles have passed the Data Screening:
| ID | Name | Journal/Conference | In scope? Not duplicated? | Appraisal included |
|---|---|---|---|---|
| 022 | Classification of facial paralysis based on machine learning techniques. | BioMedical Engineering OnLine | Y | Y |
| 023 | A critical assessment and review of artificial intelligence in facial paralysis analysis: uncovering the truth. | FACE | Y | Y |
| 024 | Deep learning for the assessment of facial nerve palsy: opportunities and challenges. | Facial Plastic Surgery | Y | Y |
| 025 | Using artificial intelligence to measure facial expression following facial reanimation surgery. | Plastic and reconstructive surgery | N | N |
| 026 | Applications of artificial intelligence, machine learning, and deep learning on facial plastic surgeries. | In Cosmetic and reconstructive facial plastic surgery: A review of medical and biomedical engineering and science concepts (pp 281-306) | Y | Y |
| 027 | Advances and future directions in the care of patients with facial paralysis. | Operative Techniques in Otolaryngology-Head and Neck Surgery | N | N |
| 028 | Toward an automatic system for computer-aided assessment in facial palsy. | Facial Plastic Surgery & Aesthetic Medicine | N | N |
| 029 | A privacy preserving diagnostic collaboration framework for facial paralysis using federated learning. | Engineering Applications of Artificial Intelligence | N | N |
| 030 | The emerging role of artificial intelligence tools for outcome measurement in facial reanimation surgery: a review. | Facial Plastic Surgery & Aesthetic Medicine | N | N |
| 031 | Automatic facial palsy, age and gender detection using a raspberry pi. | BioMedInformatics | Y | Y |
| 032 | Artificial intelligence-driven video analysis for novel outcome measures after smile reanimation surgery. | Facial Plastic Surgery & Aesthetic Medicine | N | N |
| 033 | In the eye of the beholder: changes in perceived emotion expression after smile reanimation. | Plastic and Reconstructive Surgery | N | N |
| 034 | Automatic evaluation of facial paralysis with transfer learning and improved resnet34 neural network. | In 2023 15th International Conference on Intelligent Human-Machine Systems and Cybernetics (IHMSC) (pp 218-222) | Y | Y |
| 035 | Computer vision system for facial palsy detection. | Journal of Techniques | Y | Y |
| 036 | Automated spontaneity assessment after smile reanimation: a machine learning approach. | Plastic and Reconstructive Surgery | N | N |
| 037 | A narrative review of artificial intelligence (ai) for objective assessment of aesthetic endpoints in plastic surgery. | Aesthetic Plastic Surgery | N | N |
| 038 | The auto-eface: machine learning-enhanced program yields automated facial palsy assessment tool. | Plastic and Reconstructive Surgery | Y | Y |
| 039 | Artificial intelligence for automatic pain assessment: research methods and perspectives. | Pain Research and Management | N | N |
| 040 | Svm and logistic regression for facial palsy detection utilizing facial landmark features. | In Proceedings of the 2022 Fourteenth International Conference on Contemporary Computing (pp 43-48) | Y | Y |
| 041 | Smart prediction system for facial paralysis. | In 2020 7th International Conference on Electrical and Electronics Engineering (ICEEE) (pp 321-327) | Y | Y |
Cited articles
One article, cited by the article with ID 026, have passed the Data Screening:
| ID | Name | Journal/Conference | In scope? Not duplicated? | Appraisal included |
|---|---|---|---|---|
| 046 | AFLFP: A database with annotated facial landmarks for facial palsy | IEEE Transactions on Computational Social Systems | Y | Y |
Data appraisal
All the articles that passed the Data Screening have been reviewed thoroughly to assess their Quality, Relevance, and Contribution. As a result:
- 15 articles have been evaluated as highly relevant.
- 10 articles have been evaluated as relevant.
- 6 articles have been evaluated as non-relevant.
| ID | Name | Authors | Journal/Conference | Q (<40) | R (<30) | C (<30) | Weighted Value |
|---|---|---|---|---|---|---|---|
| 001 | Reliability and validity of emotrics in the assessment of facial palsy. | Kim, Min Gi; Bae, Cho Rong; Kim, Dae Yul | Journal of personalized medicine | 25 | 25 | 30 | 80 |
| 002 | Artificial intelligence-driven video analysis for novel outcome measures after smile reanimation surgery. | Kollar, Branislav; Schneider, Laura; Eisenhardt, Steffen U | Facial plastic surgery & aesthetic medicine | 30 | 20 | 20 | 70 |
| 003 | Toward an automatic system for computer-aided assessment in facial palsy. | Guarin, Diego L; Yunusova, Yana; Jowett, Nate | Facial plastic surgery & aesthetic medicine | 30 | 25 | 20 | 75 |
| 004 | Automated spontaneity assessment after smile reanimation: a machine learning approach. | Dusseldorp, Joseph R; Guarin, Diego L; Hadlock, Tessa A | Plastic and reconstructive surgery | 10 | 10 | 5 | 25 |
| 005 | A smartphone-based automatic diagnosis system for facial nerve palsy. | Kim, Hyun Seok; Kim, So Young; Park, Kwang Suk | Sensors (Basel, Switzerland) | 15 | 20 | 10 | 45 |
| 006 | Machine learning models for predicting facial nerve palsy in parotid gland surgery for benign tumors. | Chiesa-Estomba, Carlos Miguel; Echaniz, Oier; Graña, Manuel | The Journal of surgical research | 30 | 25 | 20 | 75 |
| 007 | Clinician-graded electronic facial paralysis assessment: the eface. | Banks, Caroline A; Bhama, Prabhat K; Hadlock, Tessa A | Plastic and reconstructive surgery | 35 | 30 | 20 | 85 |
| 009 | Artificial neural network as a tool to predict facial nerve palsy in parotid gland surgery for benign tumors. | Chiesa-Estomba, Carlos M; Sistiaga-Suarez, Jon A; Medela, Alfonso | Medical sciences (Basel, Switzerland) | 30 | 25 | 20 | 75 |
| 010 | Prediction of long-term facial nerve outcomes with intraoperative nerve monitoring. | Isaacson, Brandon; Kileny, Paul R; El-Kashlan, Hussam K | Otology & neurotology : official publication of the American Otological Society, American Neurotology Society [and] European Academy of Otology and Neurotology | 20 | 25 | 10 | 55 |
| 011 | The emerging role of artificial intelligence tools for outcome measurement in facial reanimation surgery: a review. | Fuzi, Jordan; Meller, Catherine; Dusseldorp, Joseph | Facial plastic surgery & aesthetic medicine | 40 | 25 | 10 | 75 |
| 012 | A survey on computer vision for assistive medical diagnosis from faces. | Thevenot, Jerome; Lopez, Miguel Bordallo; Hadid, Abdenour | IEEE journal of biomedical and health informatics | 35 | 15 | 10 | 60 |
| 013 | Functional outcome of dual reinnervation with cross-facial nerve graft and masseteric nerve transfer for facial paralysis. | Kollar, Branislav; Weiss, Jakob B W; Eisenhardt, Steffen U | Plastic and reconstructive surgery | 20 | 10 | 5 | 35 |
| 014 | Predicting perceived disfigurement from facial function in patients with unilateral paralysis. | Lyford-Pike, Sofia; Helwig, Nathaniel E; Hadlock, Tessa A | Plastic and reconstructive surgery | 30 | 10 | 5 | 45 |
| 015 | Reliability between in-person and still photograph assessment of facial function in facial paralysis using the eface facial grading system. | Malka, Ronit; Miller, Matthew; Banks, Caroline | Facial plastic surgery & aesthetic medicine | 30 | 20 | 20 | 70 |
| 016 | Objective method of assessing and presenting the house-brackmann and regional grades of facial palsy by production of a facogram. | O'Reilly, Brian F; Soraghan, John J; He, Shu | Otology & neurotology : official publication of the American Otological Society, American Neurotology Society [and] European Academy of Otology and Neurotology | 20 | 20 | 10 | 50 |
| 017 | Facial reanimation surgery restores affect display. | Dey, Jacob K; Ishii, Masaru; Ishii, Lisa E | Otology & neurotology : official publication of the American Otological Society, American Neurotology Society [and] European Academy of Otology and Neurotology | 30 | 20 | 10 | 60 |
| 018 | Deepstroke: an efficient stroke screening framework for emergency rooms with multimodal adversarial deep learning. | Cai, Tongan; Ni, Haomiao; Wong, Stephen T C | Medical image analysis | 35 | 25 | 15 | 75 |
| 019 | In the eye of the beholder: changes in perceived emotion expression after smile reanimation. | Dusseldorp, Joseph R; Guarin, Diego L; Hadlock, Tessa A | Plastic and reconstructive surgery | 30 | 25 | 20 | 75 |
| 020 | Quantitative analysis of facial paralysis using local binary patterns in biomedical videos. | He, Shu; Soraghan, John J; Xing, Dongshan | IEEE transactions on bio-medical engineering | 35 | 10 | 10 | 55 |
| 021 | Objective outcomes analysis following microvascular gracilis transfer for facial reanimation: a review of 10 years' experience. | Bhama, Prabhat K; Weinberg, Julie S; Hadlock, Tessa A | JAMA facial plastic surgery | 30 | 20 | 10 | 60 |
| 022 | Classification of facial paralysis based on machine learning techniques. | Gaber, A., Taher, M. F., Wahed, M. A., Shalaby, N. M., & Gaber, S. | BioMedical Engineering OnLine | 25 | 30 | 16 | 71 |
| 023 | A critical assessment and review of artificial intelligence in facial paralysis analysis: uncovering the truth. | Mitchell, D. T., Allen, D. Z., Greives, M. R., & Nguyen, P. D. | FACE | 25 | 30 | 25 | 80 |
| 024 | Deep learning for the assessment of facial nerve palsy: opportunities and challenges. | Boochoon, K., Mottaghi, A., Aziz, A., & Pepper, J. P. | Facial Plastic Surgery | 35 | 30 | 10 | 75 |
| 026 | Applications of artificial intelligence, machine learning, and deep learning on facial plastic surgeries. | Tokgöz, E., & Carro, M. A. | In Cosmetic and reconstructive facial plastic surgery: A review of medical and biomedical engineering and science concepts (pp 281-306) | 30 | 10 | 0 | 40 |
| 031 | Automatic facial palsy, age and gender detection using a raspberry pi. | Amsalam, A. S., Al-Naji, A., Daeef, A. Y., & Chahl, J. | BioMedInformatics | 10 | 5 | 10 | 25 |
| 034 | Automatic evaluation of facial paralysis with transfer learning and improved resnet34 neural network. | Fu, R., & Zhou, G. | In 2023 15th International Conference on Intelligent Human-Machine Systems and Cybernetics (IHMSC) (pp 218-222) | 5 | 10 | 5 | 20 |
| 035 | Computer vision system for facial palsy detection. | Amsalam, A. S., Al-Naji, A., Daeef, A. Y., & Chahl, J. | Journal of Techniques | 5 | 10 | 5 | 20 |
| 038 | The auto-eface: machine learning-enhanced program yields automated facial palsy assessment tool. | Miller, M. Q., Hadlock, T. A., Fortier, E., & Guarin, D. L. | Plastic and Reconstructive Surgery | 30 | 30 | 20 | 80 |
| 040 | Svm and logistic regression for facial palsy detection utilizing facial landmark features. | Arora, A., Sinha, A., Bhansali, K., Goel, R., Sharma, I., & Jayal, A. | In Proceedings of the 2022 Fourteenth International Conference on Contemporary Computing (pp 43-48) | 15 | 5 | 5 | 25 |
| 041 | Smart prediction system for facial paralysis. | Ridha, A. M., Shehieb, W., Yacoub, P., Al-Balawneh, K., & Arshad, K. | In 2020 7th International Conference on Electrical and Electronics Engineering (ICEEE) (pp 321-327) | 14 | 5 | 10 | 29 |
| 046 | AFLFP: A database with annotated facial landmarks for facial palsy | Xia, Y., Nduka, C., Kannan, R. Y., Pescarini, E., Berner, J. E., & Yu, H. | IEEE Transactions on Computational Social Systems | 35 | 20 | 30 | 85 |
Responsible evaluators
The qualifications and experience of the responsible evaluators are provided in the T-015-003 Clinical Evaluation Report (CER) to demonstrate that the responsible persons fulfill the requirements to evaluate the product clinical evaluation.
| Name | Position | Company | Role in the evaluation | Declaration of interest | CV |
|---|---|---|---|---|---|
| Alberto Sabater | Medical Data Scientist | AI Labs Group SL | Editor | Yes | Yes |
| María Belén Hirigoity | Dermatologist and Medical Advisor to Legit.Health | Hospital 9 de Octubre and Alta Estética clinic | Reviewer | Yes | Yes |
| Constanza Balboni | Professor of Medicine (Dermatology) and Medical Advisor at Legit.Health | UBA School of Medicine | Reviewer | Yes | Yes |
| María Diez | Quality Manager and PRRC | AI Labs Group SL | Reviewer | Yes | Yes |
| Taig Mac Carthy | Design and development manager | AI Labs Group SL | Reviewer | Yes | Yes |
| Alfonso Medela | Technical Responsible and PRRC | AI Labs Group SL | Approver | Yes | Yes |
Record signature meaning
- Author: JD-009
- Reviewer: JD-004 or JD-003
- Approver: JD-005