Computer-assisted optical diagnosis of colorectal polyps up to 10 mm
Bibliographic record
Abstract
Background: Computer-aided diagnosis (CADx) of colorectal polyps during colonoscopy could replace pathology for certain polyps. This study aimed to evaluate CADx-assisted optical diagnosis for polyps of ≤10 mm in the context of established quality benchmarks. Methods: We performed a post-hoc analysis of a randomized controlled trial evaluating assistive versus autonomous computer-aided optical diagnosis. Our primary outcome was achievement of the American Society for Gastrointestinal Endoscopy PIVI1 threshold for resect-and-discard implementation when CADx was used for polyps ≤3 mm. Secondary outcomes included PIVI1 threshold achievement when using CADx with a size cutoff of ≤5 mm and ≤10 mm, as well as diagnostic performance and prevalence of advanced histology across the polyp size groups. Results: We included 313 patients with a total of 463 polyps of ≤10 mm undergoing optical diagnosis with CADx assistance. Compared with pathology-based intervals, surveillance interval agreement was 94.6% (95%CI 91.3%–96.7%), 89.5% (95%CI 85.4%–92.5%), and 85.9% (95%CI 81.5%–89.5%) when CADx was used with size cutoffs ≤3 mm, ≤5 mm, and ≤10 mm, respectively. The diagnostic accuracy of CADx-assisted optical diagnosis was 76.2%, 76.6%, and 72.5% for polyps sized ≤3 mm, >3 to ≤5 mm, and >5 to ≤10 mm, respectively. The negative predictive value for rectosigmoid adenomas was >90% for all size groups (PIVI2). The prevalence of advanced or serrated pathology was higher in polyps >3 mm, which resulted in a higher number of incorrectly assigned surveillances intervals. Conclusions: In our study, CADx-assisted optical diagnosis met the resect-and-discard PIVI1 threshold only with a size cutoff of ≤3 mm, and the diagnose-and-leave PIVI2 threshold for polyps ≤10 mm.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".