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Effect of a deep-learning computer-aided detection system on adenoma detection during colonoscopy (CADe-DB trial): a
Pu Wang1, Xiaogang Liu1, Tyler M Berzin2
1Department of Gastroenterology, Sichuan Academy of Medical Sciences and Sichuan Provincial People's Hospital, Chengdu, China.
The Lancet. Gastroenterology & Hepatology
|January 26, 2020
Summary
Computer-aided detection (CADe) significantly improved adenoma detection rates during colonoscopy in a blinded trial. This technology helps identify subtle polyps, potentially enhancing colorectal cancer screening effectiveness.
Area of Science:
- Gastroenterology
- Medical Imaging
- Oncology
Background:
- Colonoscopy is a key tool for colorectal cancer screening and diagnosis.
- Computer-aided detection (CADe) systems offer visual alerts to aid endoscopists.
- Previous studies used non-blinded designs, risking operational bias.
Purpose of the Study:
- To evaluate the effectiveness of a computer-aided detection (CADe) system for colonoscopy.
- To assess CADe performance in a double-blind, randomized trial to minimize bias.
- To determine if CADe improves the adenoma detection rate (ADR).
Main Methods:
- A double-blind randomized trial was conducted with 962 patients undergoing colonoscopy.
- Patients were allocated 1:1 to either a CADe system or a sham system.
- The primary outcome was the adenoma detection rate (ADR), analyzed per protocol.
Main Results:
- The adenoma detection rate (ADR) was significantly higher in the CADe group (34%) compared to the sham group (28%).
- The odds ratio for ADR with CADe was 1.36 (95% CI 1.03-1.79; p=0.030).
- CADe identified polyps that were small, flat, and difficult to discern, often obscured by folds or visual field limitations.
Conclusions:
- A high-performance CADe system can significantly increase adenoma detection rates during colonoscopy.
- CADe is effective in identifying subtle polyps missed by endoscopists.
- Further research should investigate the impact of CADe on interval colorectal cancer incidence.
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