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Minimally Invasive Approach for Diagnosing TMJ Osteoarthritis.

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Summary
This summary is machine-generated.

This study links inflammatory biomarkers and clinical symptoms to changes in temporomandibular joint osteoarthritis (TMJOA) condylar morphology. Artificial intelligence accurately stages TMJOA degeneration, correlating with biomarker levels and morphology.

Keywords:
artificial intelligencebioinformaticsbiomarkersdigital imaging/radiologyjoint diseasetemporomandibular disorders (TMDs)

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Area of Science:

  • Biomarkers and Artificial Intelligence in Osteoarthritis Research
  • Temporomandibular Joint (TMJ) Disorders and Morphology
  • Inflammatory Pathways in Degenerative Joint Disease

Background:

  • Temporomandibular joint osteoarthritis (TMJOA) is a degenerative condition affecting condylar morphology.
  • Understanding the relationship between biomarkers, clinical symptoms, and morphological changes is crucial for TMJOA management.
  • Current methods for staging TMJOA may benefit from objective, data-driven approaches.

Purpose of the Study:

  • To investigate correlations between specific biomarkers and condylar morphology in TMJOA patients.
  • To apply artificial intelligence (AI), specifically a neural network (NN), for staging TMJOA condylar morphology.
  • To compare AI-based staging accuracy against clinical expert consensus.

Main Methods:

  • Collected clinical data, serum/saliva biomarkers (17 inflammatory), and cone beam computed tomography (CBCT) scans from TMJOA patients and controls.
  • Quantified biomarker levels using protein microarrays and analyzed condylar morphology from CBCT scans.
  • Trained a NN with 259 condyles to classify TMJOA stages and compared its performance to repeated clinical expert classifications.

Main Results:

  • Salivary biomarkers (MMP-3, VE-cadherin, 6Ckine, PAI-1) correlated with each other and posterior condylar surface morphology.
  • Serum biomarkers (VE-cadherin, VEGF, MMP-3, CXCL16) showed significant associations with anterior, lateral, and superior-posterior condylar surfaces.
  • Mouth opening range was linked to medial and lateral condylar pole morphology; NN achieved 73.5%-91.2% accuracy in staging TMJOA.

Conclusions:

  • Significant correlations exist between protein expression, clinical symptoms, and condylar morphology in TMJOA.
  • AI-powered analysis of 3D condylar morphology shows promise for comprehensive TMJOA phenotyping.
  • Biomarker profiles and AI staging offer objective insights into TMJOA progression and morphology.