Computer‐aided diagnosis for colorectal polyp in comparison with endoscopists: Systematic review and meta‐analysis
Bibliographic record
Abstract
OBJECTIVES: Computer-aided diagnosis (CADx) is anticipated to enhance the prediction of colorectal polyp histology. This study aims to compare the diagnostic accuracy of CADx in the optical diagnosis of colorectal polyps, evaluating its performance against that of both experienced and inexperienced endoscopists. METHODS: The protocol of this study was registered in the International Prospective Register of Systematic Reviews (PROSPERO) (ID: CRD42024585097). Three electronic databases including MEDLINE, Embase, and the Cochrane Central Register of Controlled Trials (CENTRAL) were searched in September 2024. A bivariate random effects model was employed. The primary outcome was the comparison of sensitivity and specificity between CADx and experienced endoscopists; the secondary outcome was the comparison between CADx and inexperienced endoscopists. RESULTS: Twenty-one studies involving 5477 polyps were included. The pooled sensitivities of CADx and experienced endoscopists were 0.87 (95% confidence interval [CI] 0.82-0.91) and 0.88 (95% CI 0.83-0.91), respectively (P = 0.93). The pooled specificities of CADx and experienced endoscopists were 0.85 (95% CI 0.78-0.90) and 0.87 (95% CI 0.82-0.92), respectively (P = 0.53). In nine studies comparing CADx with inexperienced endoscopists, the pooled sensitivities were 0.88 (95% CI 0.82-0.92) for CADx and 0.85 (95% CI 0.78-0.90) for inexperienced endoscopists (P = 0.46). The pooled specificities were 0.84 (95% CI 0.78-0.88) for CADx and 0.77 (95% CI 0.70-0.83) for inexperienced endoscopists (P = 0.16). CONCLUSION: Computer-aided diagnosis does not demonstrate superior diagnostic accuracy in optical diagnosis of colorectal polyps compared to endoscopists, regardless of their experience level.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.012 | 0.037 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.017 | 0.028 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".