Diagnosing Allergic Contact Dermatitis Using Deep Learning: Single-Arm, Pragmatic Clinical Trial with an Observer Performance Study to Compare Artificial Intelligence Performance with Human Reader Performance.

Background

Allergic contact dermatitis is a common, pruritic, debilitating skin disease, affecting at least 20% of the population.

Objective

To prospectively validate a computer vision algorithm across all Fitzpatrick skin types.

Methods

Each participant was exposed to 10 allergens. The reference criterion was obtained 5 days after initial patch placement by a board-certified dermatologist. The algorithm processed photographs of the test site obtained on Day 5. Human performance in reading the photographs was also evaluated.

Results

A total of 206 evaluable participants [mean age 39 years, 66% (136/206) female, and 47% with Fitzpatrick skin types IV-VI] completed testing. Forty-two percent (87/206) of participants experienced 1 or more allergic reaction resulting in a total of 132 allergic reactions. The model provided high discrimination (AUROC 0.86, 95% CI: 0.82-0.90) and specificity (93%, 95% CI: 92%-94%) but with lower sensitivity (58%, 95% CI: 49%-67%). Human performance interpreting the photographs ranged from providing similar performance to the algorithm to providing superior performance when combined across readers. There were no serious adverse events.

Conclusions

The combination of a smartphone capture of patch testing sites with deep learning yielded high discrimination across a diverse sample.