Automated assessment of small bowel and colon cleansing in enteroscopy using a convolutional neural network

Background and study aims: Device-assisted enteroscopy (DAE) offers a comprehensive examination of the gastrointestinal tract, yet its diagnostic and therapeutic success is dependent on adequate bowel preparation. Current methods for assessing preparation quality are subjective and limited to specific gastrointestinal segments. Although prior research explored artificial intelligence models for colon preparation classification, this study aimed to develop a convolutional neural network (CNN) for automatic evaluation of bowel cleanliness in DAE, addressing both small bowel and colon cleansing. Patients and methods: We retrospectively analyzed 28 procedures (single balloon, double-balloon, and motorized spiral enteroscopy from January 2023 to May 2024). Bowel preparation was graded as excellent (≥ 90% visible mucosa), satisfactory (50%-90%), or unsatisfactory (< 50%). A dataset of 88,623 images (training: 90%, testing: 10%) was used, covering both small bowel and colon areas. CNN performance was evaluated against expert consensus using sensitivity, specificity, accuracy, and area under a receiver operating characteristic (AUC-ROC). Results: The CNN demonstrated the following performance metrics: excellent cleansing (sensitivity: 97.8%, specificity: 80.3%, accuracy: 90.6%, AUC-ROC: 0.95), satisfactory cleansing (sensitivity: 81.8%, specificity: 97.9%, accuracy: 92.7%, AUC-ROC: 0.95), and unsatisfactory cleansing (sensitivity: 68.7%, specificity: 99.5%, accuracy: 96.8%, AUC-ROC: 0.96). Conclusions: Current bowel cleanliness assessment methods are subjective and region-specific. This study presents the first CNN capable of panendoscopic bowel cleanliness evaluation during DAE, achieving high accuracy and demonstrating potential for real-time clinical application. This study marks a key step toward standardizing cleanliness assessment and endoscopy quality improvement.