Evaluating Optical Coherence Tomography and AI for Early Detection of Oral Cancer and Precancerous Lesions

What this Trial Is About

Researchers are evaluating the use of Optical Coherence Tomography OCT for early diagnosis of oral cancer, focusing on oral potentially malignant disorders OPMDs and oral squamous cell carcinoma OSCC. Despite existing oral screening methods, delays in diagnosis remain common, so this study aims to explore OCT as a non-invasive imaging tool that provides detailed cross-sectional views of oral tissues. The trial also integrates Artificial Intelligence AI to improve image analysis and speed up diagnosis, seeking to establish standardized OCT patterns correlated with histology, the current gold standard for diagnosis. Participants will undergo OCT scans alongside traditional biopsy and histological analysis using a standardized procedure to ensure accurate comparison between imaging and tissue analysis. The study is organized in phases the first year focuses on standardizing OCT and biopsy techniques the second year develops standardized OCT patterns and builds a comprehensive image dataset and the third year involves developing and validating diagnostic software using machine learning algorithms. This approach aims to create reliable diagnostic tools that enhance early detection of oral cancer. During the study, participants will receive OCT imaging and biopsies following the standardized protocol, with clinical data and medical records collected. Researchers will compare OCT images with histological findings to identify characteristic tissue features and train AI algorithms. The primary outcomes include standardizing techniques, creating an image repository, and validating diagnostic software. The study is observational and will monitor participants with suspected oral lesions to improve non-invasive diagnostic methods over approximately three years.

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