An artificial intelligence system for qualified mucosal observation time during colonoscopic withdrawal
AI-Powered QMOT: Revolutionizing Colonoscopy Quality
Our QAMaster AI system automatically quantifies Qualified Mucosal Observation Time (QMOT) during colonoscopy withdrawal, significantly improving adenoma detection rates (ADR) and reducing post-colonoscopy colorectal cancer risk. This innovation streamlines quality control, reduces human variability, and enhances patient outcomes.
Executive Impact at a Glance
Increased ADR with High QMOT
QMOT/Withdrawal Time Correlation
Model I Accuracy (Prospective)
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
Technological Innovation
QAMaster leverages advanced Vision Transformer (ViT) models for superior image quality assessment and anatomical landmark identification. This ensures real-time, precise calculation of QMOT, addressing limitations of manual quantification and traditional CNNs.
The system comprises two core models: Model I for Image Quality Analysis, trained on 57,235 images across 6 categories (in vitro, non-informative, foreign body, intervention, defective, qualified), achieving AUCs between 0.980-0.991; and Model II for Anatomical Landmark Identification, trained on 7,712 images, with AUCs between 0.977–0.997 for identifying critical landmarks like the cecum.
Clinical Impact
The study demonstrated a significant increase in ADR (36.54% vs. 19.94%) in patients with high QMOT (≥90s) compared to low QMOT (<90s), with an adjusted OR of 2.02 (95% CI 1.23–3.33). This highlights QMOT as a more effective quality indicator than total withdrawal time alone.
High QMOT was significantly correlated with the detection of diminutive (OR 3.93) and small adenomas (OR 1.76), crucial for early CRC prevention.
Deployment Strategy
QAMaster offers a robust and generalizable solution for colonoscopy quality control across diverse clinical settings. Future work involves refining annotation protocols, exploring more flexible QMOT criteria (e.g., continuous quality scores), and conducting randomized controlled trials.
The system’s compatibility with Olympus endoscopes, dominant in the market, facilitates broad adoption, potentially extendable to other endoscope types via transfer learning. Its real-time capabilities empower endoscopists to maintain optimal mucosal observation quality during withdrawal.
2.02x
Increased ADR with High QMOT (≥90s)
Enterprise Process Flow
Data Collection
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Annotations
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Model Development
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Model Evaluation
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Quantitative Analyses
| Feature | Traditional Manual Method | QAMaster AI System |
|---|---|---|
| QMOT Quantification | Manual, prone to variability | Automatic, precise, real-time |
| ADR Improvement Focus | Total withdrawal time | Qualified mucosal observation time |
| Interobserver Variability | High | Minimal, standardized |
| Resource Demands | Substantial human resources | Automated, scalable |
| Diagnostic Accuracy | Variable | Enhanced and standardized |
Enhanced ADR in a Prospective Cohort
In a prospective cohort of 482 patients, QAMaster identified a High-QMOT group (≥90s) with an ADR of 36.54%, significantly higher than the Low-QMOT group (<90s) with an ADR of 19.94%. This translates to a 2.02 times higher odds ratio for adenoma detection, confirming QAMaster's clinical utility in improving colonoscopy outcomes and reducing PCCRC risk.
Calculate Your Potential ROI
Estimate the impact of QAMaster on your organization's colonoscopy efficiency and patient outcomes.
Estimated Annual Savings
$0
Annual Hours Reclaimed
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Implementation Roadmap
A phased approach to integrating QAMaster into your clinical workflow.
Phase 1: Pilot & Customization
Integrate QAMaster into a pilot program, customize to existing endoscope systems, and refine annotation protocols.
Phase 2: Validation & Training
Conduct internal validation, train endoscopists on QMOT criteria, and roll out in a controlled environment.
Phase 3: Full-Scale Deployment
Expand QAMaster across all relevant clinical sites, establish continuous monitoring, and assess long-term outcomes.
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