Old Dogs Can Learn New Tricks: Artificial Intelligence Improves Adenoma Detection Rates in Screening Colonoscopies in Experienced Endoscopists

BACKGROUND: A high adenoma detection rate in screening colonoscopy is associated with decreased rates of post-colonoscopy colorectal cancer. The use of artificial intelligence has emerged to assist with the identification of adenomas. OBJECTIVE: Evaluating if adopting artificial intelligence-powered computer-aided detection systems in colonoscopy will increase the adenoma detection rate, with the greatest increase seen among low-volume and junior endoscopists. DESIGN: A retrospective review compared the adenoma detection rate before and after computer-aided detection in a single-center high-volume endoscopy practice, including both colorectal surgeons and gastroenterologists with a wide variety of experience. SETTING: Single tertiary referral institution between April 2023 and August 2023, and April 2024 and August 2024. PATIENTS: Endoscopists board-certified in colorectal surgery or gastroenterology. INTERVENTION: Incorporation of computer-aided detection software to colonoscopy equipment. MAIN OUTCOME MEASURE: Adenoma detection rate. RESULTS: Data from 9 colorectal surgeons and 15 gastroenterologists were included. A total of 2,327 colonoscopies were performed over the 10 months - 1,285 pre-computer-aided detection and 1,042 post-computer-aided detection. The overall mean adenoma detection rate significantly increased from 53.9% to 61.3% ( p = 0.02). Colorectal surgeons had a statistically significant increase in adenoma detection rate from 49% to 62% ( p = 0.02), but gastroenterologists did not (56.9% to 60.8%, p = 0.15). Of 12 senior and 12 junior endoscopists, the seniors had a statistically significant increase ( p = 0.02) from 51.7% to 59.6%, while juniors did not (56.2% to 62.9%, p = 0.12). From 12 high-volume and 12 low-volume endoscopists, the high-volume group had a statistically significant increase (51.3% to 59.4%, p = 0.01), but the low-volume group did not (56.5% to 63.1%, p = 0.13). LIMITATIONS: Single institution, limited time of study to show if increased adenoma detection rate will impact overall colorectal cancer incidence. CONCLUSIONS: Computer-aided detection in colonoscopy correlated with increased adenoma detection rate for experienced endoscopists. See Video Abstract .

Authors

Institutions

Publication Details

Journal
Diseases of the Colon & Rectum
Published
2026-04-01
DOI
https://doi.org/10.1097/dcr.0000000000004235
Primary Topic
Colorectal Cancer Screening and Detection
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Old Dogs Can Learn New Tricks: Artificial Intelligence Improves Adenoma Detection Rates in Screening Colonoscopies in Experienced Endoscopists

Brian R. Kann, William F. Johnston, Jennifer L. Paruch, William Kethman et al.
Diseases of the Colon & Rectum
Colorectal Cancer Screening and Detection
article

Old Dogs Can Learn New Tricks: Artificial Intelligence Improves Adenoma Detection Rates in Screening Colonoscopies in Experienced Endoscopists

Brian R. Kann, William F. Johnston, Jennifer L. Paruch, William Kethman, Koby Herman, Renee Cholyway, Danielle Kay, Alexander J. Rossi
article en

Abstract

BACKGROUND: A high adenoma detection rate in screening colonoscopy is associated with decreased rates of post-colonoscopy colorectal cancer. The use of artificial intelligence has emerged to assist with the identification of adenomas. OBJECTIVE: Evaluating if adopting artificial intelligence-powered computer-aided detection systems in colonoscopy will increase the adenoma detection rate, with the greatest increase seen among low-volume and junior endoscopists. DESIGN: A retrospective review compared the adenoma detection rate before and after computer-aided detection in a single-center high-volume endoscopy practice, including both colorectal surgeons and gastroenterologists with a wide variety of experience. SETTING: Single tertiary referral institution between April 2023 and August 2023, and April 2024 and August 2024. PATIENTS: Endoscopists board-certified in colorectal surgery or gastroenterology. INTERVENTION: Incorporation of computer-aided detection software to colonoscopy equipment. MAIN OUTCOME MEASURE: Adenoma detection rate. RESULTS: Data from 9 colorectal surgeons and 15 gastroenterologists were included. A total of 2,327 colonoscopies were performed over the 10 months - 1,285 pre-computer-aided detection and 1,042 post-computer-aided detection. The overall mean adenoma detection rate significantly increased from 53.9% to 61.3% ( p = 0.02). Colorectal surgeons had a statistically significant increase in adenoma detection rate from 49% to 62% ( p = 0.02), but gastroenterologists did not (56.9% to 60.8%, p = 0.15). Of 12 senior and 12 junior endoscopists, the seniors had a statistically significant increase ( p = 0.02) from 51.7% to 59.6%, while juniors did not (56.2% to 62.9%, p = 0.12). From 12 high-volume and 12 low-volume endoscopists, the high-volume group had a statistically significant increase (51.3% to 59.4%, p = 0.01), but the low-volume group did not (56.5% to 63.1%, p = 0.13). LIMITATIONS: Single institution, limited time of study to show if increased adenoma detection rate will impact overall colorectal cancer incidence. CONCLUSIONS: Computer-aided detection in colonoscopy correlated with increased adenoma detection rate for experienced endoscopists. See Video Abstract .

Diseases of the Colon & Rectum
Ochsner Medical Center (US)
Openalex Percentile: Top 30%
Colorectal Cancer Screening and Detection
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.