ReviewJournal of oral biology and craniofacial research
Artificial intelligence and machine learning in diagnosing and managing temporomandibular disorders: A systematic review and meta-analysis.
Review in Journal of oral biology and craniofacial research. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed.
- Editorial: Special issue on application of artificial intelligence in oral and craniofacial care.Journal of oral biology and craniofacial researchArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Artificial intelligence (AI) and machine learning (ML) models have recently emerged as promising tools for enhancing diagnostic accuracy. Objective: To evaluate the diagnostic accuracy of AI/ML models in detecting TMDs through a systematic review and meta-analysis of existing literature. Methods: A comprehensive search of electronic databases was conducted to identify studies assessing the diagnostic performance of AI/ML models in TMD diagnosis (PROSPERO-CRD420251035080). Data extraction and quality assessment were conducted independently by two reviewers using the AXIS tool for cross-sectional and Newcastle-Ottawa Scale for cohort studies. Meta-analysis of diagnostic accuracy was performed using pooled sensitivity, specificity, diagnostic odds ratio, and area under the curve. Statistical heterogeneity was assessed with the I Results: The systematic search identified 368 articles, of which 12 studies met inclusion criteria after screening. Risk of bias assessment showed most observational studies had low to unclear bias, while cross-sectional studies varied from moderate to high quality. Five studies were eligible for meta-analysis and they revealed that AI and machine learning models achieved a pooled sensitivity of 87.1 %(95 %CI:84.9 %-89.2 %) and specificity of 87.0 %(95 %CI:84.8 %-89.2 %) for TMD diagnosis. The diagnostic odds ratio was 45.1(95 %CI:30.5-66.8), with an area under the ROC curve of 0.96, indicating excellent diagnostic accuracy. Moderate heterogeneity I Conclusion: AI/ML models demonstrate excellent accuracy in differentiating patients with and without TMDs, reinforcing their potential as reliable diagnostic aids in clinical and screening settings. However, variability in input features and lack of standardized model development protocols highlight the need for future research focusing on validation across diverse populations and harmonization of diagnostic criteria.
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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.