Diagnosis of knee meniscal injuries using artificial intelligence: A systematic review and meta-analysis of diagnostic performance
Bibliographic record
Abstract
AIM OF THE STUDY: The aim was to systematically review the literature and perform a meta-analysis to estimate the performance of artificial intelligence (AI) algorithms in detecting meniscal injuries. MATERIALS AND METHODS: A systematic search was performed in the Scopus, PubMed, EBSCO, Cinahl, Web of Science, IEEE Xplore, and Cochrane Central databases on July, 2024. The included studies' reporting quality and risk of bias were evaluated using the Transparent Reporting of a multivariable prediction model for Individual Prognosis or Diagnosis (TRIPOD) and the Prediction Model Study Risk of Bias Assessment Tool (PROBAST), respectively. Also, a meta-analysis was done using contingency tables to estimate diagnostic performance metrics (sensitivity and specificity), and a meta-regression analysis was performed to investigate the effect of the following variables on the main outcome: imaging view, data augmentation and transfer learning usage, and presence of meniscal tear in the injury, with a corresponding 95% confidence interval (CI) and a P-value of 0.05 as a threshold for significance. RESULTS: Among 28 included studies, 92 contingency tables were extracted from 15 studies. The reference standard of the studies were mostly expert radiologists, orthopedics, or surgical reports. The pooled sensitivity and specificity for AI algorithms on internal validation were 81% (95% CI: 78, 85), and 78% (95% CI: 72, 83), and for clinicians on internal validation were 85% (95% CI: 76, 91), and 88% (95% CI: 83, 92), respectively. The pooled sensitivity and specificity for studies validating algorithms with an external test set were 82% (95% CI: 74, 88), and 88% (95% CI: 84, 91), respectively. CONCLUSION: The results of this study imply the lower diagnostic performance of AI-based algorithms in knee meniscal injuries compared with clinicians.
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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 | 0.023 | 0.066 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.021 | 0.037 |
| Bibliometrics | 0.011 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".