AI
‐Assisted Automated Two‐Stage Patch Test Interpretation System Using Vision Transformer
Jin Ju Lee, Yon Soo Jeong, You Won Choi, Joo Young Roh, Hae Young Choi, So Hyun Ahn, Ji Yeon Byun ABSTRACT
Background
Patch testing, the gold standard for allergic contact dermatitis (ACD) diagnosis, suffers from inter‐observer variability. We developed a Vision Transformer‐based automated patch test interpretation system.
Methods
This retrospective two‐centre study included 424 patients (734 images) using the Korean Standard Series (25 allergens). Four model variants—3‐class and 6‐class classification, each with one‐stage and two‐stage (binary segmentation + classification) processing—were compared. Inter‐rater agreement was assessed using Cohen's κ. Performance metrics included accuracy, balanced accuracy, macro F1‐score, PPV, and NPV, with 95% CIs by bootstrap resampling.
Results
Inter‐rater agreement showed substantial variability (κ: 0.393–0.557). DeiT demonstrated generally favourable performance compared to CNN architectures in binary classification, achieving higher accuracy than Xception (96.1% vs. 92.3%) and substantially higher recall than ResNet‐50 (88.0% vs. 66.9%). For 3‐class classification, one‐stage and two‐stage approaches performed comparably (accuracy 92.9% vs. 92.8%, balanced accuracy 88.0% vs. 86.8%, all p > 0.05). For 6‐class classification, two‐stage processing was superior (accuracy 91.6% vs. 86.0%, balanced accuracy 70.1% vs. 67.5%, p < 0.001). Institution‐stratified analysis confirmed robustness across different test marking conventions.
Conclusion
The 3‐class approach offers robust performance with implementation flexibility, whilst 6‐class classification requires two‐stage processing. Consistent performance across a dataset with heterogeneous imaging conditions supports real‐world applicability in ACD management.