Evaluation efficacy and accuracy of a real-time computer-aided polyp detection system during colonoscopy: a prospective, multicentric, randomized, parallel-controlled study trial
Bibliographic record
Abstract
INTRODUCTION: Colorectal cancer (CRC) ranks as the second deadliest cancer globally, impacting patients' quality of life. Colonoscopy is the primary screening method for detecting adenomas and polyps, crucial for reducing long-term CRC risk, but it misses about 30% of cases. Efforts to improve detection rates include using AI to enhance colonoscopy. This study assesses the effectiveness and accuracy of a real-time AI-assisted polyp detection system during colonoscopy. MATERIALS AND METHODS: The study included 390 patients aged 40 to 75 undergoing colonoscopies for either colorectal cancer screening (risk score ≥ 4) or clinical diagnosis. Participants were randomly assigned to an experimental group using software-assisted diagnosis or a control group with physician diagnosis. The software, a medical image processing tool with B/S and MVC architecture, operates on Windows 10 (64-bit) and supports real-time image handling and lesion identification via HDMI, SDI, AV, and DVI outputs from endoscopy devices. Expert evaluations of retrospective video lesions served as the gold standard. Efficacy was assessed by polyp per colonoscopy (PPC), adenoma per colonoscopy (APC), adenoma detection rate (ADR), and polyp detection rate (PDR), while accuracy was measured using sensitivity and specificity against the gold standard. RESULTS: In this multicenter, randomized controlled trial, computer-aided detection (CADe) significantly improved polyp detection rates (PDR), achieving 67.18% in the CADe group versus 56.92% in the control group. The CADe group identified more polyps, especially those 5 mm or smaller (61.03% vs. 56.92%). In addition, the CADe group demonstrated higher specificity (98.44%) and sensitivity (95.19%) in the FAS dataset, and improved sensitivity (95.82% vs. 77.53%) in the PPS dataset, with both groups maintaining 100% specificity. These results suggest that the AI-assisted system enhances PDR accuracy. CONCLUSION: This real-time computer-aided polyp detection system enhances efficacy by boosting adenoma and polyp detection rates, while also achieving high accuracy with excellent sensitivity and specificity.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.011 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".