Quantitative Evaluation of Patch Test Reactions Using a 3D Camera-Derived Features and Machine Learning: The Role of Temporal Dynamics

Background: Epicutaneous patch testing is the gold standard for diagnosing allergic contact dermatitis (ACD), yet its interpretation relies on subjective scoring and remains prone to inter-observer variability. Aim: In this study, we present a machine learning pipeline that complements subjective scoring with objective bioengineering measurements derived from the Antera 3D imaging system. Methods: A dataset of skin reactions was analyzed, with particular attention to how the data was split to avoid information leakage between patients. For this reason, methods such as GroupShuffleSplit and GroupKFold, which account for patient-level clustering,, were used. Results: Of the models tested, the Random Forest classifier showed the best overall performance, with an AUC of 0.861 (95% CI: 0.830–0.888) on patient data that had not been used during training, outperforming the Multi-Layer Perceptron model. The incorporation of additional features that capture changes between 48 and 72 h improved the results even further, raising the AUC to 0.902 and achieving a very high sensitivity of 96.8%. Conclusions: Overall, the results show that objective biophysical measurements derived from the Antera 3D imaging system can be combined with machine-learning techniques for objective patch-test assessment. Incorporating temporal changes between the 48- and 72-h readings further improved model performance, suggesting that temporal changes provide additional information beyond single-time-point measurements.