Artificial intelligence technology based on deep learning is being applied in various medical fields, and research is being actively conducted to develop computer-aided detection (CADe) systems for colonoscopies to overcome the limitation of the variance of human skills. These well-trained CADe systems demonstrated high performance for neoplastic polyp detection and reported a 44% increase in adenoma detection rate (ADR) for endoscopists. However, the level of performance in the CADe system is not clear for expert endoscopists to be useful for ADR increase.~Furthermore, operator fatigue may negatively influence ADR during a screening colonoscopy, and many studies reported a decrease in ADR according to the increase in working hours and procedure numbers. Accordingly, the investigators sought to identify the effect of the colonoscopy CADe system according to working hours and procedure numbers in endoscopists with various levels. The investigators hypothesized that the CADe system would be useful to prevent the decrease in ADR in case of a high endoscopy workload according to the performance of CADe systems.
Computer-aided detection (CADe) systems have been actively researched for polyp detection in colonoscopy. The investigators aim to identify the effect of two CADe systems according to the workload of endoscopy units