SynthesisOral radiology2026
Artificial intelligence-based segmentation of mandibular canal on cone-beam computed tomography: a systematic review and meta-analysis.
Synthesis in Oral radiology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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.
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Authors and funding
11 authors.
Funding
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Abstract
objectivesTo systematically evaluate the accuracy and generalizability of artificial intelligence (AI)-based segmentation of mandibular canal and related anatomical structures on cone-beam computed tomography (CBCT) and to examine methodological factors influencing reported performance.
methodsA systematic search of PubMed/MEDLINE, Scopus, Web of Science, Cochrane Library, and Wiley Online Library was conducted through April 10, 2026. Studies evaluating AI-based segmentation of the mandibular canal or related anatomical structures on CBCT were included. Risk of bias was assessed using QUADAS-2 tool. Random-effects meta-analysis using restricted maximum likelihood estimation was performed to pool segmentation performance, with the Dice Similarity Coefficient (DSC) as the primary outcome. Subgroup analyses, meta-regression, publication bias assessment, and GRADE certainty evaluation were conducted.
resultsFifty-nine studies were included qualitatively, and 46 studies were eligible for quantitative synthesis. Across 40 effect sizes, the pooled DSC was 0.806 (95% CI: 0.773-0.839), indicating high average segmentation performance, although heterogeneity was substantial (I² = 99.68%). Pooled secondary outcomes were 0.757 for Intersection-over-Union, 2.085 mm for HD95, and 0.498 mm for mean distance error. No significant differences were observed according to validation strategy or AI architecture. Publication year was the only signific.
conclusionsAI-based segmentation of mandibular canals on CBCT demonstrates promising performance; however, substantial heterogeneity, limited external validation, and low certainty of evidence restrict confidence in its generalizability. AI systems should be considered adjunctive tools, and further high-quality, externally validated studies with standardized methodologies are required to support reliable clinical implementation. CLINICAL RELEVANCE: AI-assisted mandibular canal segmentation may improve workflow efficiency and support treatment planning in implant dentistry and oral surgery. Nevertheless, clinician oversight remains essential because current evidence does not support fully autonomous clinical implementation.
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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.