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Comparing Traditional Versus AI-Assisted TMJ Disorder Management Approaches: A Systematic Review and Meta-Analysis.

Vini Mehta1,2, Annie Vathani3, Praveen Kumar Gonuguntla Kamma4

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Summary
This summary is machine-generated.

Artificial intelligence (AI) shows promise in improving temporomandibular joint disorder (TMD) diagnosis, offering higher accuracy than traditional methods. However, current evidence is limited by methodological issues, necessitating further research for validation.

Keywords:
artificial intelligencediagnostic accuracymachine learningmeta‐analysisradiomicssystematic reviewtemporomandibular joint disorder

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence in Medicine
  • Diagnostic Accuracy

Background:

  • Temporomandibular joint disorders (TMDs) pose diagnostic challenges.
  • Traditional diagnostic methods for TMDs include clinical examination, imaging, and standardized criteria.
  • There is a growing interest in leveraging artificial intelligence (AI) for improved diagnostic precision.

Purpose of the Study:

  • To systematically review and meta-analyze the diagnostic accuracy of AI-based techniques compared to traditional methods for TMDs.
  • To evaluate the performance metrics (sensitivity, specificity, accuracy) of AI algorithms in TMD diagnosis.

Main Methods:

  • Systematic literature search of multiple databases (PubMed, Scopus, Embase, Cochrane, Google Scholar) from 2010 to 2025.
  • Inclusion of studies utilizing AI (deep learning, machine learning) for TMD diagnosis, comparing them against traditional approaches.
  • Meta-analysis of pooled sensitivity and specificity; risk of bias assessed using QUADAS-2.

Main Results:

  • AI methods demonstrated moderate-to-high diagnostic accuracy (sensitivity: 0.66–0.88, specificity: 0.72–0.86).
  • AI models integrating radiomic and semantic features achieved higher accuracy (sensitivity: 0.82–0.93, specificity: 0.76–0.90).
  • Evidence certainty was low due to high bias risk, small sample sizes, and lack of external validation.

Conclusions:

  • AI techniques show significant potential to enhance diagnostic precision for TMDs.
  • Methodological limitations necessitate high-quality prospective studies for AI validation in TMD management.
  • Standardized reporting is crucial for future AI research in this field.