1 Department of Orthodontics and Pediatric Dentistry, School of Dentistry, University of Michigan, Ann Arbor, MI, USA.
2 Department of Psychiatry, School of Medicine, University of North Carolina, Chapel Hill, NC, USA.
J Dent Res. 2019 Sep;98(10):1103-1111. doi: 10.1177/0022034519865187. Epub 2019 Jul 24.
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.
本研究的目的是检验与髁突形态相关的生物标志物组之间的相关性，并应用人工智能在神经网络（NN）中测试形态分析特征，以分期颞下颌关节骨关节炎（TMJOA）的髁突形态。17 名 TMJOA 患者（39.9±11.7 岁）患有疾病的症状和体征不到 10 年，17 名年龄和性别匹配的对照组患者（39.4±15.2 岁）完成了一份问卷，进行了颞下颌关节临床检查，抽取了血液和唾液样本，并进行了高分辨率锥形束 CT 扫描。使用蛋白质微阵列定量了 17 种炎症生物标志物的血清和唾液水平。NN 用 259 个其他髁突进行了训练，以检测和分类 TMJOA 的阶段，然后与重复的临床专家分类进行了比较。TMJOA 患者的唾液生物标志物 MMP-3、VE-钙黏蛋白、6Ckine 和 PAI-1 之间存在相关性，并且与髁突后表面的形态变异性显著相关。在血清中，VE-钙黏蛋白和 VEGF 彼此相关，与髁突前表面的形态变异性显著相关，而 MMP-3 和 CXCL16 与髁突前表面、外侧极和后上表面的变异性呈统计学显著关联。开口变量范围是与内侧和外侧髁突极形态变异性关联最显著的临床标志物。重复临床医生共识分类在 1 个组内差异内对退变程度有 97.8%的一致性。NN 分期与重复临床医生共识的 TMJOA 预测分析显示，准确性分别为 73.5%和 91.2%。本研究证明了蛋白质表达水平、临床症状和髁突表面形态之间的显著相关性。结果表明，NN 可以全面表型 TMJOA 髁突形态的 3 维变异性。
