Evaluation of Artificial Intelligence-Assisted Diagnosis of Skin Erythema in a Patch Test.

Background: The patch test evaluates skin erythema, infiltration, papules and vesicles following exposure to various substances, including metals, cosmetics and medicines. Accurate evaluation of these conditions requires consistent skin score assessments, precise visual grading and minimal inter-expert variability.

Objectives: This study aimed to develop a skin irritation artificial intelligence model based on the YOLOv5x object detection framework to automatically detect skin irritation from the patch test images for multiple test substances.

Methods: Patch test images were collected with test sites marked to enable the YOLOv5x algorithm to locate the samples. An expert assigned a score to each sample (0-4) for training and validation. The model was trained using 83 629 data points. Evaluation and validation were performed with 1312 and 1536 data points, respectively.

Results: The model achieved an overall accuracy of 0.983 at both 24 and 48 h, with an F1 score (harmonic mean of recall and precision) of 0.982. The areas under the curve (AUCs) for scores 0, 1 and 2 were 0.914, 0.838 and 0.865, respectively. The sensitivity for a score of 0 was 0.997.

Conclusion: These findings suggest that this AI model effectively supports and classifies skin irritation, thereby facilitating faster and more accurate dermatological evaluations.