The purpose of this study is to develop and validate the performance of an artificial intelligence(AI) assisted Boston Bowel preparation Scoring(BBPS) system for evaluation of bowel cleanness, then testify whether this new scoring system can help physicians to improve the quality control parameters of colonoscopy in clinic practice.

Colonoscopy is recommended as a routine examination for colorectal cancer screening. Adequate bowel preparation is indispensable to ensure a clear vision of colonic mucosa，complete inspection of all colon segments, and furthermore improves the detection rates of small adenomas. Thus, the adequacy of bowel preparation should be accurately evaluated and documented. However, the accuracy of current bowel preparation quality scales greatly relies on intra-observer and inter-observer consistency for lack of objective measurements. Recently, deep learning based on central neural networks (CNN) has shown multiple potential in computer-aided detection and computer-aided diagnose of gastrointestinal lesions. While, no studies have been conducted to evaluate the performance of deep learning algorithm in bowel preparation quality scoring. This study aims to train an algorithm to assess bowel preparation quality using the BBPS, and testify whether the engagement of AI can improve the quality control parameters of colonoscopy.

Inclusion Criteria:

• Patients aged 18-70 years undergoing afternoon colonoscopy

Exclusion Criteria:

• Known or suspected bowel obstruction, stricture or perforation
• Compromised swallowing reflex or mental status
• Severe chronic renal failure(creatinine clearance < 30 ml/min)
• Severe congestive heart failure (New York Heart Association class III or IV)
• Uncontrolled hypertension (systolic blood pressure > 170 mm Hg, diastolic blood pressure > 100 mm Hg)
• Dehydration
• Disturbance of electrolytes
• Pregnancy or lactation
• Hemodynamically unstable
• Unable to give informed consent