Accepted for/Published in: JMIR Research Protocols
Date Submitted: Feb 24, 2022
Open Peer Review Period: Feb 24, 2022 - Apr 21, 2022
Date Accepted: May 12, 2022
(closed for review but you can still tweet)
Using artificial intelligence as a diagnostic decision support tool in skin disease: observational prospective cohort study
ABSTRACT
Background:
Dermatological conditions are a relevant health problem. Machine learning models are increasingly being applied to dermatology as a diagnostic decision support tool using image analysis, especially for skin cancer detection and classification.
Objective:
The objective of this study is to perform a prospective validation of an image analysis Machine Learning (ML) model, which is capable of screening 44 different skin disease types, comparing its diagnostic capacity with that of General Practitioners (GPs) and dermatologists.
Methods:
In these prospective study 100 consecutive patients who visit a participant GP with a skin problem in central Catalonia will be recruited, data collection is planned to last 7 months. Skin diseases anonymized pictures will be taken and introduced in the ML model interface, which will return top 5 accuracy diagnosis. The same image will be also sent as a teledermatology consultation, following the current workflow. GP, ML model and dermatologist/s assessments will be compared to calculate the precision, sensitivity, specificity and accuracy of the ML model.
Results:
Results will be represented globally and individually for each skin disease class using a confusion matrix and One vs All methodology. Time taken to make the diagnosis will also be taken into consideration.
Conclusions:
This study will provide information about ML models effectiveness and limitations. External testing is essential for regulating these diagnostic systems, in order to deploy ML models in a PCP setting. Clinical Trial: The clinical trial has been approved by the IDIAP Jordi Gol i Guirna ethics committee with code 20-159P
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