Use of artificial intelligence for detection of MB2 canals in maxillary first molars on CBCT: a systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: Detecting the second mesiobuccal (MB2) canal in maxillary first molars is challenging, even with cone-beam computed tomography (CBCT). Artificial intelligence (AI), especially deep learning, has been explored as a tool to aid detection. OBJECTIVES: This systematic review and meta-analysis evaluated the diagnostic accuracy of AI in identifying MB2 canals on CBCT. METHODS: Following PRISMA guidelines, a comprehensive electronic search across five databases (PubMed, Scopus, Web of Science, Embase, and Scopus Secondary) retrieved 52 articles. After removing duplicates and screening titles/abstracts, 7 full texts were assessed, of which 4 met the inclusion criteria. Studies were eligible if they applied AI algorithms for MB2 detection in CBCT images and reported diagnostic performance outcomes. Data extraction included study design, dataset size, AI model architecture, and diagnostic metrics. Pooled estimates of sensitivity, specificity, accuracy, positive predictive value (PPV), and negative predictive value (NPV) were calculated using a random-effects model. Heterogeneity was assessed with the I² statistic, and publication bias was evaluated with Egger's test. RESULTS: Four studies were included. AI models achieved a pooled sensitivity of 0.82 and specificity of 0.74. Deep learning models outperformed traditional machine learning, with higher sensitivity (0.87 vs. 0.80), specificity (0.90 vs. 0.68), and accuracy (0.84 vs. 0.75). Considerable heterogeneity and small sample sizes limited generalizability. CONCLUSION: AI, particularly deep learning, shows promise in detecting MB2 canals on CBCT. While current evidence is preliminary, standardised AI training and reporting protocols, together with larger multicenter studies, are needed to validate these tools. Clinically, AI could serve as a supplementary aid to improve diagnostic consistency and reduce missed canals during endodontic treatment.
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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 | 0.015 | 0.048 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.029 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 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".