Demonstration of Convolutional Neural Networks to Determine Patch Test Reactivity

Abstract

Abstract:

Background: Convolutional neural networks (CNNs) have the potential to assist allergists and dermatologists in the analysis of patch tests. Such models can help reduce interprovider variability and improve consistency of patch test interpretations.

Objective: Our aim is to evaluate the performance of a CNN model as a proof of concept in discriminating between patch tests with reactions and patch tests without reactions.

Methods: We performed a retrospective analysis of patch test images from March 2020 to March 2021. The CNN model was trained as a binary classifier to discriminate between reaction and nonreaction patches. Performance of the model was determined using summary statistics and receiver operator characteristics (ROC) curves.

Results:

In total, 13,622 images from 125 patients were recorded for analysis. The majority of patients in the cohort were female (81.6%) with Fitzpatrick skin types I–II (88.0%). The area under curve was 0.940, indicating a high discriminative performance of the model for this data set. This resulted in a total accuracy of 90.1%, sensitivity of 86.0%, and specificity of 90.2%.

Conclusions: CNNs have the capacity to determine the presence of delayed-type reactions in patch tests. Future prospective studies are required to assess the generalizability of such models.