Detecting Patient Readiness for Colonoscopy Through Bowel Image Analysis: A Machine Learning Approach

Bowel preparation is a crucial step in ensuring the success and accuracy of colonoscopy procedures. Adequate bowel cleansing allows for better visualization and detection of abnormalities within the colon. In this study, we present an AI tool developed to assess the quality of bowel preparation in colonoscopy procedures. The dataset used in this study consists of 350 images of toilet bowls obtained from patients at the hospital “Hôtel Dieu de France” in Beirut, Lebanon. Their images are labeled by the professionals using the Boston scores. Our methodology involves a comprehensive pre-processing phase, encompassing detection, cropping, color adjustment, and Principal Component Analysis (PCA) on the image dataset. Subsequently, we applied different machine learning (ML) models for classification, achieving a high accuracy of 92% with Gradient Boosting. This AI-based approach exhibits great potential in enhancing the efficiency and reliability of colonoscopy evaluations, ultimately leading to improved patient outcomes and early detection of gastrointestinal disorders.