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Related Experiment Video

Updated: Jun 30, 2026

An Automated Method for Assessing Visual Acuity in Infants and Toddlers Using an Eye-Tracking System
05:10

An Automated Method for Assessing Visual Acuity in Infants and Toddlers Using an Eye-Tracking System

Published on: March 17, 2023

AI-Assisted Automated Two-Stage Patch Test Interpretation System Using Vision Transformer.

Jin Ju Lee1, Yon Soo Jeong2, You Won Choi1

  • 1Departments of Dermatology, Ewha Womans University College of Medicine, Seoul, Korea.

Contact Dermatitis
|June 29, 2026
PubMed
Summary

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This summary is machine-generated.

Automated patch testing using a Vision Transformer system improves allergic contact dermatitis diagnosis by reducing inter-observer variability. The 3-class model offers flexible implementation, while the 6-class model requires two-stage processing for optimal results.

Area of Science:

  • Dermatology
  • Medical Imaging
  • Artificial Intelligence

Background:

  • Patch testing is the standard for diagnosing allergic contact dermatitis (ACD).
  • Current patch testing methods suffer from significant inter-observer variability.
  • Automated interpretation systems are needed to improve diagnostic consistency.

Purpose of the Study:

  • To develop and evaluate a Vision Transformer-based automated system for patch test interpretation.
  • To compare the performance of different model variants for ACD classification.
  • To assess the system's robustness across varied imaging conditions.

Main Methods:

  • A retrospective study of 424 patients with 734 patch test images.
  • Four Vision Transformer model variants were tested: 3-class and 6-class classification, each with one-stage and two-stage processing.
Keywords:
allergic contact dermatitisdeep learningdermatologymachine learningmedical image analysispatch testvision transformer

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  • Performance was evaluated using accuracy, balanced accuracy, F1-score, PPV, NPV, and inter-rater agreement (Cohen's κ).

    • Main Results:

    • Inter-rater agreement in manual interpretation showed substantial variability (κ: 0.393-0.557).
    • The Vision Transformer system achieved high accuracy, outperforming CNNs in binary classification.
    • For 6-class classification, a two-stage approach significantly outperformed a one-stage approach (accuracy 91.6% vs. 86.0%).

    Conclusions:

    • The 3-class automated patch test interpretation system demonstrates robust performance and implementation flexibility.
    • A two-stage processing approach is recommended for 6-class classification.
    • The system's consistent performance across diverse imaging conditions suggests strong real-world applicability for ACD management.