P487 Automated Scoring of Patient Endoscopy Videos Using Deep Learning Techniques: A Promising Approach for Clinical Trials in Inflammatory Bowel Disease

Abstract

Abstract Background Accurate assessment of patient endoscopy videos is crucial for various clinical trials, including those related to inflammatory bowel diseases (IBD). However, manual scoring by trained local and central readers can lead to discrepancies, inconsistencies, and logistical challenges. To address these issues, we devised a novel approach utilizing deep learning techniques to automate the analysis, parsing, and scoring of patient endoscopy videos. Our objectives were to reduce human error/variability, accelerate endoscopic scoring by enhancing read accuracy, and lower clinical trial cost. Methods We developed and trained deep learning models using a large dataset of labeled patient endoscopy images. The models were designed to predict the Mayo endoscopic score (MES), which serves as a standardized measure of disease severity in IBD. Our approach developed convolutional neural networks (CNNs) to extract features from raw video data and processed them into meaningful patterns. The models were optimized using transfer learning and validated on external endoscopy video datasets through rigorous testing protocols. Results Using an iterative process, we developed two sets (total 4) of proof-of-concept (POC) Artificial Intelligence (AI) models using frame level datasets from endoscopy videos. First, quality was assessed by the blur detection model, currently performing at > 90% accuracy, and the bowel preparation model, performing at > 95% accuracy compared to the labels provided by human annotators. Second, MES sub-score prediction currently performs with >75% accuracy for 4-level Mayo sub-score prediction and >90% accuracy in predicting advanced MES scores (2 or higher compared with consensus score derived from 3 annotators). Conclusion The presented study highlights the potential of deep learning techniques in revolutionizing the assessment of patient endoscopy videos in clinical trials for IBD. Automating the endoscopic scoring process enables faster, more accurate, and cost-efficient evaluations, ultimately leading to better detection of patient outcomes. Future research directions involve further refining the models to enhance their performance and expand their application to other gastrointestinal diseases.