A machine learning‐based, decision support, mobile phone application for diagnosis of common dermatological diseases

Pangti, Rashi; Mathur, Jyoti; Chouhan, Vikas; Kumar, Sharad; Rajput, Lavina; Shah, Subhash Chandra; Gupta, Anurag; Dixit, Aparna Banerjee; Dholakia, Dhwani; Gupta, Sanjeev; George, Mino; Sharma, Vaishali

doi:10.1111/jdv.16967

Cited by 34 publications

(25 citation statements)

References 29 publications

(52 reference statements)

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“…Another limitation was that the validation of the algorithm could not be done in actual clinical settings. In the future, this model could be enhanced by combining image analysis with automated patient history analysis to provide a more accurate, integrated inference 18 …”

Section: Discussionmentioning

confidence: 99%

“…To answer the question if skin lesions can inform treatment regimens, Young et al proposed documenting cutaneous abnormalities present at diagnosis and during the course of COVID‐19 using photographs captured by mobile phones 24 . We envision that algorithms like ours could be integrated into smartphone apps to provide a decision support tool for dermatologists to recognize COVID‐19‐related skin lesions, guide patient care and research efforts 18 …”

Section: Discussionmentioning

confidence: 99%

“…In the future, this model could be enhanced by combining image analysis with automated patient history analysis to provide a more accurate, integrated inference. 18 In patients where COVID-19 skin lesions manifest before systemic symptoms, machine-learning (ML) algorithms may help in diagnosing these lesions and quarantine of patients with suspected SARS-CoV-2 infection. In other cases, where COVID-19 skin lesions occur alongside systemic symptoms but before testing results, ML-based detection of skin lesions may serve as an additional diagnostic clue and aid in early intervention and quarantine.…”

Section: Discussionmentioning

confidence: 99%

“…B, shows the disease categories that represent the false-positive and false-negative cases in all 5-folds for COVID-19-rash dermatologists to recognize COVID-19-related skin lesions, guide patient care and research efforts. 18…”

Section: Discussionmentioning

confidence: 99%

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A convolutional neural network architecture for the recognition of cutaneous manifestations of COVID‐19

Chouhan¹,

Pangti

Kumar³

et al. 2021

Dermatologic Therapy

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During the COVID-19 pandemic, dermatologists reported an array of different cutaneous manifestations of the disease. It is challenging to discriminate COVID-19-related cutaneous manifestations from other closely resembling skin lesions. The aim of this study was to generate and evaluate a novel CNN (Convolutional Neural Network) ensemble architecture for detection of COVID-19-associated skin lesions from clinical images. An ensemble model of three different CNN-based algorithms was trained with clinical images of skin lesions from confirmed COVID-19 positive patients, healthy controls as well as 18 other common skin conditions, which included close mimics of COVID-19 skin lesions such as urticaria, varicella, pityriasis rosea, herpes zoster, bullous pemphigoid and psoriasis. The multi-class model demonstrated an overall top-1 accuracy of 86.7% for all 20 diseases. The sensitivity and specificity of COVID-19-rash detection were found to be 84.2 ± 5.1% and 99.5 ± 0.2%, respectively. The positive predictive value, NPV and area under curve values for COVID-19-rash were 88.0 ± 5.6%, 99.4 ± 0.2% and 0.97 ± 0.25, respectively. The binary classifier had a mean sensitivity, specificity and accuracy of 76.81 ± 6.25%, 99.77 ± 0.14% and 98.91 ± 0.17%, respectively for COVID-19 rash. The model was robust in detection of all skin lesions on both white and skin of color, although only a few images of COVID-19-associated skin lesions from skin of color were available. To our best knowledge, this is the first machine learning-based study for automated detection of COVID-19 based on skin images and may provide a useful decision support tool for physicians to optimize contact-free COVID-19 triage, differential diagnosis of skin lesions and patient care.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

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A convolutional neural network architecture for the recognition of cutaneous manifestations of COVID‐19

Chouhan¹,

Pangti

Kumar³

et al. 2021

Dermatologic Therapy

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“…In this study, a mobile phone application (app) was used, which was developed on a convolutional neural network‐based algorithm trained with 15,418 clinical images of 40 different skin conditions and normal skin, out of which 8,227 images (from Caucasian population) were from public databases (http://www.hellenicdermatlas.com/en and http://www.danderm.dk/atlas) after taking permission from them, and 7,501 images were from private data of Indian patients taken by various Indian dermatologists; 310 images were discarded as these were either poor quality pictures or multiple images from the same patient 4 …”

Section: Figurementioning

confidence: 99%

Comparison of performance of a deep learning‐based mobile application with non‐dermatologist physicians in the diagnosis of common skin diseases

et al. 2021

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Lack of Transparency and Potential Bias in Artificial Intelligence Data Sets and Algorithms

et al. 2021

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IMPORTANCE Clinical artificial intelligence (AI) algorithms have the potential to improve clinical care, but fair, generalizable algorithms depend on the clinical data on which they are trained and tested.OBJECTIVE To assess whether data sets used for training diagnostic AI algorithms addressing skin disease are adequately described and to identify potential sources of bias in these data sets.DATA SOURCES In this scoping review, PubMed was used to search for peer-reviewed research articles published between January 1, 2015, and November 1, 2020, with the following paired search terms: deep learning and dermatology, artificial intelligence and dermatology, deep learning and dermatologist, and artificial intelligence and dermatologist.STUDY SELECTION Studies that developed or tested an existing deep learning algorithm for triage, diagnosis, or monitoring using clinical or dermoscopic images of skin disease were selected, and the articles were independently reviewed by 2 investigators to verify that they met selection criteria.CONSENSUS PROCESS Data set audit criteria were determined by consensus of all authors after reviewing existing literature to highlight data set transparency and sources of bias.RESULTS A total of 70 unique studies were included. Among these studies, 1 065 291 images were used to develop or test AI algorithms, of which only 257 372 (24.2%) were publicly available. Only 14 studies (20.0%) included descriptions of patient ethnicity or race in at least 1 data set used. Only 7 studies (10.0%) included any information about skin tone in at least 1 data set used. Thirty-six of the 56 studies developing new AI algorithms for cutaneous malignant neoplasms (64.3%) met the gold standard criteria for disease labeling. Public data sets were cited more often than private data sets, suggesting that public data sets contribute more to new development and benchmarks.CONCLUSIONS AND RELEVANCE This scoping review identified 3 issues in data sets that are used to develop and test clinical AI algorithms for skin disease that should be addressed before clinical translation: (1) sparsity of data set characterization and lack of transparency, (2) nonstandard and unverified disease labels, and (3) inability to fully assess patient diversity used for algorithm development and testing.

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A machine learning‐based, decision support, mobile phone application for diagnosis of common dermatological diseases

Cited by 34 publications

References 29 publications

A convolutional neural network architecture for the recognition of cutaneous manifestations of COVID‐19

A convolutional neural network architecture for the recognition of cutaneous manifestations of COVID‐19

Comparison of performance of a deep learning‐based mobile application with non‐dermatologist physicians in the diagnosis of common skin diseases

Lack of Transparency and Potential Bias in Artificial Intelligence Data Sets and Algorithms

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