SynthesisBMC oral health2025
Use of artificial intelligence for detection of MB2 canals in maxillary first molars on CBCT: a systematic review and meta-analysis.
Synthesis in BMC oral health, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
3 citing papers in PubMed.
- Clinical Readiness of Artificial Intelligence Models for MB2 Canal Detection in Maxillary Molars: A Scoping Review.International dental journal · 2026Article
- Review
- Application of machine learning in predicting of MB2 canal in permanent maxillary first and second molars: A CBCT study.PloS one · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundDetecting 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.
objectivesThis systematic review and meta-analysis evaluated the diagnostic accuracy of AI in identifying MB2 canals on CBCT.
methodsFollowing 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.
resultsFour 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.
conclusionAI, 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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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.