SynthesisOral radiology2025
The diagnostic accuracy of cone-beam computed tomography for assessing in vitro osseous alterations of the mandibular condyle: a systematic review and meta-analysis.
Synthesis in Oral radiology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
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Who cites it
2 citing papers in PubMed.
- Prevalence and demographic associations of degenerative mandibular condylar changes in a Bangladeshi cohort: a CBCT study.Oral radiology · 2026Article
- Prevalence of degenerative alterations of osseous temporomandibular joint structures in cone beam computed tomograms in a consecutive sample of young adults. A retrospective study.Clinical oral investigations · 2025Article
Corrections and comments
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Authors and funding
7 authors.
Funding
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Abstract
objectiveTo determine the diagnostic accuracy of cone-beam computed tomography (CBCT) in detecting simulated bony changes in the mandibular condyle by assessing the sensitivity and specificity.
methodsThis review adhered to PRISMA guidelines. Following predefined eligibility criteria, a search was conducted in four electronic databases in June 2024. The study-level risk of bias was assessed using a diagnostic test accuracy checklist provided by the Joanna Briggs Institute. Pooled estimates of sensitivity and specificity were calculated using a bivariate random-effects model.
resultsAmong 1,803 potentially eligible references, six met the inclusion criteria for qualitative synthesis, and three for meta-analysis. The meta-analysis revealed that the index test, CBCT, had a low pooled sensitivity of 0.54 and a high specificity of 0.93 for detecting simulated defects of the mandibular condyle. Computed tomography exhibited a lower sensitivity of 0.37, but similar specificity of 0.93 like CBCT. Out of the six studies, five were found to have a low risk of bias.
conclusionsCone-beam computed tomography is found to be more accurate than other modalities for detecting condylar bony changes, effectively ruling out false positives, but with a risk of missing true positives. A smaller field of view and voxel size may provide more accurate detection.
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