Artificial intelligence and colorectal neoplasia detection performances in patients with positive fecal immunochemical test: Meta‐analysis and systematic review
Bibliographic record
Abstract
OBJECTIVES: The combination of fecal immunochemical test (FIT) followed by colonoscopy has established itself as one of the preferred population-based screening strategies. Despite extensive exploration of various techniques and technologies, their impact on adenoma detection rate has shown inconsistency across studies in this specific setting "FIT+ population." We aimed to assess the impact of the computer-aided detection (CADe) system in all randomized trials focused on this subpopulation. METHODS: We searched MEDLINE, EMBASE, and Scopus databases until September 2023 for randomized controlled trials reporting diagnostic accuracy of CADe systems for detection of colorectal neoplasia. The primary outcome was pooled adenoma detection rate, and secondary outcomes were adenoma per colonoscopy, advanced adenoma per colonoscopy, serrated lesions, and nonneoplastic per colonoscopy. RESULTS: Ten randomized trials on 5421 patients were included. Adenoma detection rate was higher in the CADe group than in the standard colonoscopy group (0.62 vs. 0.52; relative risk 1.19; 95% confidence interval 1.08-1.31). CADe also resulted in higher detection performances of both adenomas (incidence rate ratio 1.16; 95% confidence interval 1.09-1.24) and serrated lesions (incidence rate ratio, 1.20; 95% confidence interval 1.05-1.38) at per-polyp analysis. No differences were found for advanced adenomas between the groups. On the other hand, more nonneoplastic polyps were removed in the CADe than the standard group (0.45 vs. 0.34; mean difference 0.06; P = 0.026) in a comparable inspection time. CONCLUSIONS: The use of CADe during colonoscopy results in an increased detection of adenomas, and serrated lesions, in a FIT+ setting. The impact on advanced adenomas was not significant. Higher rates of unnecessary removal of nonneoplastic polyps were also reported.
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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 | 0.007 | 0.029 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.012 | 0.022 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".