Performance of Computer-Aided Detection and Quality of Bowel Preparation: A Comprehensive Analysis of Colonoscopy Outcomes

Sep 16, 2024Digestive diseases and sciences

How Computer-Aided Detection and Bowel Cleaning Quality Affect Colonoscopy Results

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Abstract

The inclusion of (CADe) during colonoscopy significantly improved the detection of polyps, with odds ratios of 2.0 for hyperplastic polyps and 4.34 for inadequately prepped patients.

Patients with adequate bowel preparation showed a 2.0 times greater likelihood of detecting hyperplastic polyps with CADe assistance.
Detection of adenomas increased by 47% (OR = 1.47) with the use of CADe in adequately prepared patients.
In inadequately prepped patients, the likelihood of detecting polyps with CADe was 4.34 times higher.
CADe also demonstrated a significant increase in the detection of adenomas among inadequately prepared patients (OR = 2.9).
The use of CADe was associated with a marginal increase in both withdrawal and procedure times.

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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 (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.

Key numbers

4.34

Increase in Polyp Detection Rate

Odds ratio for polyp detection in inadequately prepped patients

1.6

Average Number of Polyps Retrieved

Average number of polyps per procedure with

2.7

Increase in Withdrawal Time

Mean increase in withdrawal time with assistance

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Performance of Computer-Aided Detection and Quality of Bowel Preparation: A Comprehensive Analysis of Colonoscopy Outcomes

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