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Quantitative Evaluation of Patch Test Reactions Using a 3D Camera-Derived Features and Machine Learning: The Role of
Sotiria C Gilou1, Anna Tagka2, George I Lambrou3,4,5
1Lab of Medical Physics & Digital Innovation, Faculty of Medicine, School of Health Sciences, Aristotle University of Thessaloniki, AHEPA University General Hospital of Thessaloniki, 54636 Thessaloniki, Greece.
Bioengineering (Basel, Switzerland)
|July 28, 2026
Summary
This study introduces a machine learning approach using Antera 3D imaging to objectively assess allergic contact dermatitis (ACD) patch tests. The model achieved high accuracy, improving diagnosis beyond subjective scoring.
Area of Science:
- Dermatology
- Bioengineering
- Artificial Intelligence
Background:
- Epicutaneous patch testing is the standard for diagnosing allergic contact dermatitis (ACD).
- Current interpretation relies on subjective scoring, leading to variability.
- Objective assessment methods are needed to improve diagnostic accuracy.
Purpose of the Study:
- To develop a machine learning pipeline for objective patch test assessment.
- To integrate objective bioengineering measurements from Antera 3D imaging with AI.
- To enhance the diagnostic reliability of allergic contact dermatitis testing.
Main Methods:
- Utilized a dataset of skin reactions from patch tests.
- Employed patient-level data splitting techniques (GroupShuffleSplit, GroupKFold) to prevent information leakage.
- Developed and evaluated machine learning models, including Random Forest and Multi-Layer Perceptron.
Main Results:
- The Random Forest classifier achieved an AUC of 0.861 on unseen patient data.
- Incorporating temporal features (48-72h changes) improved performance, reaching an AUC of 0.902.
- Achieved a high sensitivity of 96.8% with the enhanced model.
Conclusions:
- Objective biophysical measurements from Antera 3D imaging can be effectively combined with machine learning for patch test assessment.
- Temporal changes in skin reactions provide valuable information for improving diagnostic models.
- This approach offers a more objective and accurate method for diagnosing allergic contact dermatitis.
