LAUSR

copy are components of cancer screening programs worldwide, the aim being to reduce colorectal cancer (CRC) incidence and mortality. However, FIT-based screening schemes do not prevent all cancers: some patients who have undergone FIT-based colonoscopies develop post-colonoscopy colorectal cancer (PCCRC). One of the main causes of PCCRC is poor-quality colonoscopy. The adenoma detection rate (ADR) is an established quality indicator for primary screening colonoscopy [1], this rate being inversely associated with the incidence of PCCRC. Whether ADR is an equivalent indicator of quality in post-FIT colonoscopies and primary screening colonoscopies has not yet been established. However, it is generally believed that ADR serves as a reliable quality indicator in post-FIT colonoscopies [2]. With recent advances in artificial intelligence (AI) technology, computer-aided detection (CADe) systems for polyp detection during real-time colonoscopy have been attracting attention both in the academic field and marketplace. Generally, such CADe systems are reported to increase the ADR [3] and reduce the adenoma miss rate [4]. However, because most of these studies did not involve cancer screening programs, the clinical impact of CADe in such programs remains unknown.