Long-term impact of computer-aided adenoma detection: a prospective observational study

Computer-aided detection (CADe) systems have improved the adenoma detection rate (ADR); however, concerns about its long-term effect on endoscopists' performance without CADe and potential deskilling remain unaddressed. This study evaluated the impact of CADe on the learning curve of endoscopists for adenoma detection.This propensity score-matching, prospective, single-center, observational study was conducted from January 2021 to December 2023. CADe systems were installed in half of the endoscopy units, and patients were equally distributed between the rooms. Patients aged ≥20 years scheduled for colonoscopy were included, excluding those with polyposis, inflammatory bowel disease, known polyps, incomplete colonoscopy, emergency cases, or previous colorectal surgery, and those examined by novices. Endoscopists were classified as high detectors (ADR ≥25%) or low detectors (ADR <25%) based on the ADR recorded before CADe implementation. To assess skill acquisition and transfer, the primary outcome was the change in ADR over time, as measured by cumulative summation (CUSUM) analysis, in both CADe and non-CADe procedures.Of 18 962 patients who underwent colonoscopy, 13 245 patients were excluded, and of the 5717 patients initially enrolled, 4712 (CADe group, n = 2356; non-CADe group, n = 2356) were analyzed after propensity score matching. CUSUM analysis showed that both high and low detectors achieved enhanced detection performance for CADe procedures. Among non-CADe procedures, high detectors had accelerated learning curves, indicating they maintained a higher ADR, whereas low detectors showed no significant change in their learning trajectory.After CADe implementation, the detection rate in procedures performed without CADe was maintained and did not decline over time.

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Publication Details

Journal
Endoscopy
Published
2025-07-19
DOI
https://doi.org/10.1055/a-2661-2624
Citations
4
Primary Topic
AI in cancer detection
Type
article
Field-Weighted Citation Impact
9.92
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Long-term impact of computer-aided adenoma detection: a prospective observational study

Kazuki Kato, Noriyuki Ogata, Kensaku Mori, Tatsuya Sakurai et al.
4 citations
Endoscopy
AI in cancer detection
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article

Long-term impact of computer-aided adenoma detection: a prospective observational study

Kazuki Kato, Noriyuki Ogata, Kensaku Mori, Tatsuya Sakurai, Yuki Takashina, Yuki Miyata, Yukihide Koyama, Kunihiko Wakamura, Taishi Okumura, Masashi Misawa, Takemasa Hayashi, Toshiyuki Baba, Katsuro Ichimasa, Yosuke Minegishi, Masahiro Oda, Hideyuki Miyachi, Toyoki Kudo, Hayato Itoh, Yasuharu Maeda, Yutaro Ide, Shun Kato, Yushi Ogawa, Masahiro Abe, Tetsuo Nemoto, Kazumi Takisima
article en
4 citations

Abstract

Computer-aided detection (CADe) systems have improved the adenoma detection rate (ADR); however, concerns about its long-term effect on endoscopists' performance without CADe and potential deskilling remain unaddressed. This study evaluated the impact of CADe on the learning curve of endoscopists for adenoma detection.This propensity score-matching, prospective, single-center, observational study was conducted from January 2021 to December 2023. CADe systems were installed in half of the endoscopy units, and patients were equally distributed between the rooms. Patients aged ≥20 years scheduled for colonoscopy were included, excluding those with polyposis, inflammatory bowel disease, known polyps, incomplete colonoscopy, emergency cases, or previous colorectal surgery, and those examined by novices. Endoscopists were classified as high detectors (ADR ≥25%) or low detectors (ADR <25%) based on the ADR recorded before CADe implementation. To assess skill acquisition and transfer, the primary outcome was the change in ADR over time, as measured by cumulative summation (CUSUM) analysis, in both CADe and non-CADe procedures.Of 18 962 patients who underwent colonoscopy, 13 245 patients were excluded, and of the 5717 patients initially enrolled, 4712 (CADe group, n = 2356; non-CADe group, n = 2356) were analyzed after propensity score matching. CUSUM analysis showed that both high and low detectors achieved enhanced detection performance for CADe procedures. Among non-CADe procedures, high detectors had accelerated learning curves, indicating they maintained a higher ADR, whereas low detectors showed no significant change in their learning trajectory.After CADe implementation, the detection rate in procedures performed without CADe was maintained and did not decline over time.

EndoscopyVol. 58(02)
University of Kochi (JP), Fukuoka University (JP), SHOWA Medical University Northern Yokohama Hospital (JP), Nagoya University (JP), Kōchi University (JP)
Openalex Percentile: Top 2%
AI in cancer detection
9.92
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