Minimally Invasive Approach for Diagnosing TMJ Osteoarthritis

Abstract

This study’s objectives were to test correlations among groups of biomarkers that are associated with condylar morphology and to apply artificial intelligence to test shape analysis features in a neural network (NN) to stage condylar morphology in temporomandibular joint osteoarthritis (TMJOA). Seventeen TMJOA patients (39.9 ± 11.7 y) experiencing signs and symptoms of the disease for less than 10 y and 17 age- and sex-matched control subjects (39.4 ± 15.2 y) completed a questionnaire, had a temporomandibular joint clinical exam, had blood and saliva samples drawn, and had high-resolution cone beam computed tomography scans taken. Serum and salivary levels of 17 inflammatory biomarkers were quantified using protein microarrays. A NN was trained with 259 other condyles to detect and classify the stage of TMJOA and then compared to repeated clinical experts’ classifications. Levels of the salivary biomarkers MMP-3, VE-cadherin, 6Ckine, and PAI-1 were correlated to each other in TMJOA patients and were significantly correlated with condylar morphological variability on the posterior surface of the condyle. In serum, VE-cadherin and VEGF were correlated with one another and with significant morphological variability on the anterior surface of the condyle, while MMP-3 and CXCL16 presented statistically significant associations with variability on the anterior surface, lateral pole, and superior-posterior surface of the condyle. The range of mouth opening variables were the clinical markers with the most significant associations with morphological variability at the medial and lateral condylar poles. The repeated clinician consensus classification had 97.8% agreement on degree of degeneration within 1 group difference. Predictive analytics of the NN’s staging of TMJOA compared to the repeated clinicians’ consensus revealed 73.5% and 91.2% accuracy. This study demonstrated significant correlations among variations in protein expression levels, clinical symptoms, and condylar surface morphology. The results suggest that 3-dimensional variability in TMJOA condylar morphology can be comprehensively phenotyped by the NN.

Application of Artificial Intelligence in Dentistry

“…A natural language processing–based model was successful in differentiating TMD-mimicking conditions from genuine TMDs, according to the frequency of word usage in the patient’s chief complaint and mouth-opening size (Nam et al 2018). ANNs based on CBCT revealed a 91.2% degree of conformity as compared with clinician consensus in classifying condylar morphology (Shoukri et al 2019). By integrating patients’ chief complaints, clinical and biochemical indicators, and objective radiomic features into training data sets and collecting larger samples of these data sets, a computer-assisted diagnosis system is warranted to improve TMD diagnostic accuracy.…”

The Modern and Digital Transformation of Oral Health Care: A Mini Review

“…Theoretically, the collective clinical data of TMDs examinations, if they can be translated into a well-structured and organized computer language, are able to differentiate between absolute TMDs diagnosis and other clinical conditions mimicking TMDs [ 65 ]. A study by Shoukri et al (2019) showed that the neural networks are able to program and classify the TMDs based on the combination of condylar radiographic imaging utilizing CBCT, biological markers such as saliva, and a variable range of clinical indicators including detailed facial and muscle pain and soreness history, range of mouth opening, and other signs such as headaches [ 66 ].…”

Strontium Ranelate Promotes Chondrogenesis Through Inhibition of the Wnt/β-catenin Pathway

“…Histologically, abnormal synthesis and degradation of articular chondrocytes, extracellular matrix and subchondral bone are detected; as a result, the cartilage becomes thinner and stripped, and subchondral bone is exposed and scleroid [2]. The pathogenesis of TMJ-OA involves in ammation, excessive mechanical stress, abnormal remodeling of subchondral bone, chondrocyte apoptosis, catabolic disturbances and genetic factors, in which accelerated subchondral bone turnover plays a role in the initiation of TMJ-OA [3].…”