ABSTRACT
BACKGROUND: Inflammatory skin diseases, such as psoriasis, atopic eczema, and contact dermatitis pose diagnostic challenges due to their diverse clinical presentations and the need for rapid and precise diagnostic assessment. OBJECTIVE: While recent studies described non-invasive imaging devices such as Optical coherence tomography and Line-field confocal OCT (LC-OCT) as possible techniques to enable real-time visualization of pathological features, a standardized analysis and validation has not yet been performed. METHODS: One hundred forty lesions from patients diagnosed with atopic eczema (57), psoriasis (50), and contact dermatitis (33) were imaged using OCT and LC-OCT. Statistical analysis was employed to assess the significance of their characteristic morphologic features. Additionally, a decision tree algorithm based on Gini's coefficient calculations was developed to identify key attributes and criteria for accurately classifying the disease groups. RESULTS: Descriptive statistics revealed distinct morphologic features in eczema, psoriasis, and contact dermatitis lesions. Multivariate logistic regression demonstrated the significance of these features, providing a robust differentiation between the three inflammatory conditions. The decision tree algorithm further enhanced classification accuracy by identifying optimal attributes for disease discrimination, highlighting specific morphologic criteria as crucial for rapid diagnosis in the clinical setting. CONCLUSION: The combined approach of descriptive statistics, multivariate logistic regression, and a decision tree algorithm provides a thorough understanding of the unique aspects associated with each inflammatory skin disease. This research offers a practical framework for lesion classification, enhancing the interpretability of imaging results for clinicians.