Related Experiment Video
Updated: Jun 13, 2025
E-Patient Counseling Trial E-PACO: Computer Based Education versus Nurse Counseling for Patients to Prepare for Colonoscopy
Published on: August 1, 2019
Performance of Computer-Aided Detection and Quality of Bowel Preparation: A Comprehensive Analysis of Colonoscopy
Dalton A Norwood1, Shyam Thakkar2, Amanda Cartee3
1Division of Preventive Medicine, School of Medicine, The University of Alabama at Birmingham, Birmingham, AL, 35205, USA.
Background:
Artificial intelligence (AI) has emerged as a promising tool for detecting and characterizing colorectal polyps during colonoscopy, offering potential enhancements in traditional colonoscopy procedures to improve outcomes in patients with inadequate bowel preparation.
Aims:
This study aimed to assess the impact of an AI tool on computer-aided detection (CADe) assistance during colonoscopy in this population.
Methods:
This case-control study utilized propensity score matching (PSM) for age, sex, race, and colonoscopy indication to analyze a database of patients who underwent colonoscopy at a single tertiary referral center between 2017 and 2023. Patients were excluded if the procedure was incomplete or aborted owing to poor preparation. The patients were categorized based on the use of AI during colonoscopy. Data on patient demographics and colonoscopy performance metrics were collected. Univariate and multivariate logistic regression models were used to compare the groups.
Results:
After PSM patients with adequately prepped colonoscopies (n = 1466), the likelihood of detecting hyperplastic polyps (OR = 2.0, 95%CI 1.7-2.5, p < 0.001), adenomas (OR = 1.47, 95%CI 1.19-1.81, p < 0.001), and sessile serrated polyps (OR = 1.90, 95%CI 1.20-3.03, p = 0.007) significantly increased with the inclusion of CADe. In inadequately prepped patients (n = 160), CADe exhibited a more pronounced impact on the polyp detection rate (OR = 4.34, 95%CI 1.6-6.16, p = 0.049) and adenomas (OR = 2.9, 95%CI 2.20-8.57, p < 0.001), with a marginal increase in withdrawal and procedure times.
Conclusion:
This study highlights the significant improvement in detecting diminutive polyps (< 5 mm) and sessile polyps using CADe, although notably, this benefit was only seen in patients with adequate bowel preparation. In conclusion, the integration of AI in colonoscopy, driven by artificial intelligence, promises to significantly enhance lesion detection and diagnosis, revolutionize the procedure's effectiveness, and improve patient outcomes.
More Related Videos
Related Concept Videos
01:26Imaging Studies III: Gastrointestinal Motility Studies and Virtual Colonoscopy
Radionuclide Testing
Radionuclide testing is a sophisticated medical technique for assessing gastrointestinal motility. It focuses on gastric emptying and colonic transit time. Radioactive markers track the movement of food through the digestive system, providing insights into gastrointestinal disorders.
In gastric emptying studies, a meal's liquid and...
01:25Endoscopic Procedures II: Colonoscopy
01:26Endoscopic Procedures IV: Sigmoidoscopy and Laproscopy
Sigmoidoscopy
Sigmoidoscopy is a diagnostic procedure that uses a flexible sigmoidoscope equipped with a light source and camera to examine the rectum and sigmoid colon. The procedure involves inserting the tube through the anus...
01:23Lower GI Series: Barium Enema
Procedure Details
The examination begins by inserting a lubricated rectal tube into the patient's rectum to administer a radiopaque barium solution. The barium flow is carefully...
01:25Assessment of the Rectum and Anus
Rectal Inspection
Begin by inspecting the perianal and anal areas for color, texture, rashes,...
01:28Endoscopic Procedures III: Video Capsule Endoscopy