A prospective study evaluating an artificial intelligence-based system for withdrawal time measurement

Abstract Withdrawal time has emerged as a critical quality measure in colonoscopy for colorectal cancer screening. Owing to the high variability in calculating withdrawal time, recent research has explored the use of artificial intelligence (AI) to standardize this process, but prospective validation is lacking. This prospective, superiority trial compared the accuracy of AI-assisted withdrawal time calculation with that of physicians during routine colonoscopy from December 2023 to March 2024. The gold standard was obtained via manual, frame-by-frame annotation of the examination video recordings. The AI also automatically generated an image report, which was qualitatively assessed by four endoscopists. 126 patients were analyzed. The proposed AI system demonstrated a significantly lower mean absolute error (MAE) in estimating withdrawal time compared with physicians (2.2 vs. 4.2 minutes; P < 0.001). This was attributed to examinations containing endoscopic interventions, where the AI had significantly lower MAE compared with physicians (2.1 vs. 5.2; P < 0.001). The MAE was comparable in the absence of interventions (2.3 vs. 2.3; P = 0.52). High-quality image reports were generated by the AI system; 97% were assessed as showing satisfactory timeline representation and 81% achieved overall satisfaction. Our study demonstrated the superiority of an AI system in calculating withdrawal time during colonoscopy compared with physicians, providing significant improvements, especially in examinations involving interventions. This work demonstrates the promise of AI in streamlining clinical workflows.