Introduction Withdrawal time during colonoscopy is a key quality indicator directly linked to adenoma detection and colorectal cancer prevention. However, accurate measurement in routine practice is often inconsistent, especially during procedures involving interventions.
Introduction
Withdrawal time during colonoscopy is a key quality indicator directly linked to adenoma detection and colorectal cancer prevention. However, accurate measurement in routine practice is often inconsistent, especially during procedures involving interventions. This variability limits standardisation and quality benchmarking, prompting interest in artificial intelligence (AI)-based systems to objectively measure withdrawal time.
Problem Statement
Despite the importance of withdrawal time, current measurement relies on manual estimation by endoscopists, which is prone to error and inconsistency, particularly when procedures involve therapeutic interventions. There is a lack of prospective validation of AI systems to determine whether they can reliably and accurately standardise this critical quality metric.
Summary
In this prospective study of 126 patients, AI demonstrated superior accuracy in measuring withdrawal time compared to physicians, with significantly lower error, especially during interventional procedures. In non-interventional cases, AI and physicians performed similarly. Additionally, the AI system generated high-quality procedural image reports with strong endoscopist satisfaction. Overall, this study highlights the potential of AI to improve standardization, enhance quality metrics, and streamline workflow in colonoscopy practice.
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