Artificial intelligence-based assessments of colonoscopic withdrawal technique: a new method for measuring and enhancing the quality of fold examination.

BACKGROUND

This study aimed to develop an artificial intelligence (AI)-based system for measuring fold examination quality (FEQ) of colonoscopic withdrawal technique. We also examined the relationship between the system's evaluation of FEQ and FEQ scores from experts, and adenoma detection rate (ADR) and withdrawal time of colonoscopists, and evaluated the system's ability to improve FEQ during colonoscopy.

METHODS

First, we developed an AI-based system for measuring FEQ. Next, 103 consecutive colonoscopies performed by 11 colonoscopists were collected for evaluation. Three experts graded FEQ of each colonoscopy, after which the recorded colonoscopies were evaluated by the system. We further assessed the system by correlating its evaluation of FEQ against expert scoring, historical ADR, and withdrawal time of each colonoscopist. We also conducted a prospective observational study to evaluate the system's performance in enhancing fold examination.

RESULTS

The system's evaluations of FEQ of each endoscopist were significantly correlated with experts' scores (r = 0.871, P < 0.001), historical ADR (r = 0.852, P = 0.001), and withdrawal time (r = 0.727, P = 0.01). For colonoscopies performed by colonoscopists with previously low ADRs (< 25 %), AI assistance significantly improved the FEQ, evaluated by both the AI system (0.29 [interquartile range (IQR) 0.27-0.30] vs. 0.23 [0.17-0.26]) and experts (14.00 [14.00-15.00] vs. 11.67 [10.00-13.33]) (both P < 0.001).

CONCLUSION

The system's evaluation of FEQ was strongly correlated with FEQ scores from experts, historical ADR, and withdrawal time of each colonoscopist. The system has the potential to enhance FEQ.