AI-driven prediction of the optimal number of bowel preparation sachets for adequate preparation before colonoscopy.

Background: High-quality colonoscopy is critical for colorectal cancer (CRC) prevention. Suboptimal bowel preparation impairs adenoma detection and prolongs cecal intubation time (CIT). This study aimed to identify behavioral and clinical predictors of CIT and bowel preparation quality, to support development of an AI-based model for personalized purgative dosing. Method: In a prospective study of 280 outpatients undergoing screening colonoscopy, variables such as age, sex, BMI, physical activity, sleep duration, and bowel preparation quality (Boston Bowel Preparation Scale (BBPS)) were recorded. CIT, total colonoscopy time (TCT), and polyp/adenoma detection were measured. Regression models and machine learning algorithms (Random Forest, XGBoost) were applied to develop predictive models. Result: More sedentary time 6-9 hours was significantly associated with poor preparation and longer CIT and TCT (p<0.01). Daily activity Time between 9-12 hours correlated with better preparation. Chronic constipation, opioid use, and obesity independently predicted poor bowel prep (BBPS ≤6). Female sex, age ≥60, and BMI ≥30 was also associated with lower BBPS scores. The SVR model achieved a mean absolute error (MAE) of 1.3 and RMSE of 1.6 in predicting optimal powder dosage. Conclusion: Simple behavioral factors such as sleep and physical activity significantly affect colonoscopy performance. Personalized purgative dosing based on individual risk factors may enhance preparation quality and reduce procedural delays. Machine learning models can support clinical decision-making and improve colorectal cancer screening efficacy.