SYsteMatical Trained learnIng aLgorithms for Oral carcInogenesiS Interpretation by Optical Coherence Tomography

Plain-language summary

This clinical trial aims to assess the efficacy of Optical Coherence Tomography (OCT) in the early diagnosis of oral cancer. It focuses on Oral Potentially Malignant Disorders (OPMDs) as precursors to Oral Squamous Cell Carcinoma (OSCC). Despite the availability of oral screening, diagnostic delays persist, underscoring the importance of exploring non-invasive methodologies. The OCT technology provides cross-sectional analysis of biological tissues, enabling a detailed evaluation of ultrastructural oral mucosal features. The trial aims to compare OCT preliminary evaluation with traditional histology, considered the gold standard in oral lesion diagnosing. It seeks to create a database of pathological OCT data, facilitating the non invasive identification of carcinogenic processes. The goal is to develop a diagnostic algorithm based on OCT, enhancing its ability to detect characteristic patterns such as the keratinized layer, squamous epithelium, basement membrane, and lamina propria in oral tissues affected by OPMDs and OSCC. Furthermore, the trial aims to implement Artificial Intelligence (AI) in OCT image analysis. The use of machine learning algorithms could contribute to a faster and more accurate assessment of images, aiding in early diagnosis. The trial aims to standardize the comparison between in vivo OCT images and histological analysis, adopting a site-specific approach in biopsies to improve correspondence between data collected by both methods. In summary, the trial not only evaluates OCT as a diagnostic tool but also aims to integrate AI to develop a standardized approach that enhances the accuracy of oral cancer diagnosis, providing a significant contribution to clinical practice.

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