ArticleNPJ digital medicine2025
An artificial intelligence system for qualified mucosal observation time during colonoscopic withdrawal.
Article in NPJ digital medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
1 citing paper in PubMed.
- Advances in Artificial Intelligence for Gastrointestinal Endoscopy: 2026 Update.Diagnostics (Basel, Switzerland) · 2026Review
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Authors and funding
17 authors.
Funding
Abstract
Colonoscopic withdrawal time is crucial for achieving a high adenoma detection rate (ADR) and reducing post-colonoscopy colorectal cancer risk. Enhanced qualified mucosal observation improves ADR, but manual quantification of qualified mucosal observation time (QMOT) in routine is challenging. We developed an artificial intelligence (AI) system, QAMaster, for automatic QMOT calculation during colonoscopy withdrawal. QAMaster comprises two models: Model I for image quality analysis (trained with 57,235 images from 64 patients) and Model II for anatomical landmark identification (trained with 7712 images from 3013 patients). Patients were stratified by QMOT, and ADR was compared. The areas under the curve (AUC) of Model I were 0.980-0.991, and Model II were 0.977-0.997. Among 482 patients, ADR was 36.54% (57/156) vs. 19.94% (65/326) in high-QMOT group (≥90 s) vs. low-QMOT group (<90 s) (adjusted OR 2.02; 95% CI 1.23-3.33). QAMaster provides a promising tool for assessing colonoscopy withdrawal quality.
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Registered trials
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