Mayo Clinic proceedings. Digital health · 2024
An Automated Approach for Diagnosing Allergic Contact Dermatitis Using Deep Learning to Support Democratization of Patch Testing.
СтатьяНазвание и аннотация: Перевод пока не подготовлен
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Hall Matthew R, Weston Alexander D, Wieczorek Mikolaj A, Hobbs Misty M, Caruso Maria A, Siddiqui Habeeba, Pacheco-Spann Laura M, Lopez-Dominguez Johanny L, Escoda-Diaz Coralle, Carter Rickey E, Bruce Charles J
Аннотация
OBJECTIVE: To develop a deep learning algorithm for the analysis of patch testing.
PATIENTS AND METHODS: A retrospective case series between January 1, 2010, and December 31, 2020, was constructed to develop a deep learning model for the classification of patch test results from photographs. The performance of human expert readers reviewing the same photographs blinded to the original clinical physical examination findings was measured to benchmark model performance.
RESULTS: Model performance on the independent test set (n=5070 test site locations from 37 patients) achieved an area under the receiver operating characteristic curve of 0.89 (95% CI, 0.86-0.91) and an F1 score of 37.1. The optimal cutoff had a sensitivity of 70.1% (136/194; 95% CI, 63.1%-76.5%) and a specificity of 91.7% (4472/4876; 95% CI, 90.9%-92.5%).
CONCLUSION: We demonstrated proof-of-concept utility for detecting allergic contact dermatitis using an automated deep learning approach.
PATIENTS AND METHODS: A retrospective case series between January 1, 2010, and December 31, 2020, was constructed to develop a deep learning model for the classification of patch test results from photographs. The performance of human expert readers reviewing the same photographs blinded to the original clinical physical examination findings was measured to benchmark model performance.
RESULTS: Model performance on the independent test set (n=5070 test site locations from 37 patients) achieved an area under the receiver operating characteristic curve of 0.89 (95% CI, 0.86-0.91) and an F1 score of 37.1. The optimal cutoff had a sensitivity of 70.1% (136/194; 95% CI, 63.1%-76.5%) and a specificity of 91.7% (4472/4876; 95% CI, 90.9%-92.5%).
CONCLUSION: We demonstrated proof-of-concept utility for detecting allergic contact dermatitis using an automated deep learning approach.