A Novel Convolutional Neural Network Model as an Alternative Approach to Bowel Preparation Evaluation Before Colonoscopy in the COVID-19 Era: A Multicenter, Single-Blinded, Randomized Study

An adequate bowel preparation is the key to a successful colonoscopy for detecting adenomas and preventing colorectal cancer. We developed an artificial intelligence (AI) platform using a convolutional neural network (CNN) model to evaluate the quality of bowel preparation before colonoscopy.
This was a colonoscopist-blinded, randomized study. A total of 1,434 patients were enrolled and randomized into groups that used AI-CNN model to evaluate the quality of bowel preparation (AI-CNN, n = 730) or performed self-evaluation per routine practice (Control, n = 704). The primary outcome was the consistency between two methods. Secondary outcomes included quality of bowel preparation according to Boston Bowel Preparation Scale (BBPS), polyp detection rate (PDR), and adenoma detection rate (ADR).
There was no significant difference in evaluation results (pass or not pass) in respect of the adequacy of bowel preparation per BBPS score between groups, suggesting that the AI-CNN model and routine practice were generally consistent in the evaluation of bowel preparation quality. The mean BBPS score, PDR, and ADR were also similar in both groups. Additionally, it's worth noting that the mean BBPS score of patients with pass results was significantly higher for the AI-CNN group than for the Control group, indicating that the AI-CNN model may improve the quality of bowel preparation further in patients who showed adequate bowel preparation.
The novel AI-CNN model, demonstrating comparable outcomes to the routine practice, may potentially be an alternative approach for evaluating the bowel preparation quality before colonoscopy.