Sui Huachao, Xiao Mo, Jiang Xueqing, Li Jiaye, Qiao Feng, Yin Bin, Wang Yuanyuan, Wu Ligeng
Department of Endodontics, Tianjin Medical University School and Hospital of Stomatology & Tianjin Key Laboratory of Oral Soft and Hard Tissues Restoration and Regeneration, No. 12 Qixiangtai Road, Heping District, Tianjin, 300070, China.
Department of Endodontics, Tianjin Stomatological Hospital, School of Medicine, Nankai University, Tianjin, 300041, China.
BMC Oral Health. 2025 Jun 5;25(1):916. doi: 10.1186/s12903-025-06098-9.
Temporomandibular disorders (TMDs) are frequently associated with posterior condylar displacement; however, early prediction of this displacement remains a significant challenge. Therefore, in this study, we aimed to develop and evaluate a predictive model for bilateral posterior condylar displacement.
In this retrospective observational study, 166 cone-beam computed tomography images were examined and categorized into two groups based on condyle positions as observed in the sagittal images of the joint space: those with bilateral posterior condylar displacement and those without. Three machine-learning algorithms-Random Forest, Least Absolute Shrinkage and Selection Operator (LASSO) regression, and Extreme Gradient Boosting (XGBoost)-were used to identify risk factors and establish a risk assessment model. Calibration curves, receiver operating characteristic curves, and decision curve analyses were employed to evaluate the accuracy of the predictions, differentiation, and clinical usefulness of the models, respectively.
Articular eminence inclination (AEI) and age were identified as significant risk factors for bilateral posterior condylar displacement. The area under the curve values for the LASSO and Random Forest models were both > 0.7, indicating satisfactory discriminative ability of the nomogram. No significant differences were observed in the differentiation and calibration performance of the three models. Clinical utility analysis revealed that the LASSO regression model, which incorporated age, AEI, A point-nasion-B point (ANB) angle, and facial height ratio (S-Go/N-Me), demonstrated superior net benefit compared to the other models when the probability threshold exceeded 45%.
Patients with a steeper AEI, insufficient posterior vertical distance (S-Go/N-Me), an ANB angle ≥ 4.7°, and older age are more likely to experience bilateral posterior condylar displacement. The prognostic nomogram developed and validated in this study may assist clinicians in assessing the risk of bilateral posterior condylar displacement.
颞下颌关节紊乱病(TMDs)常与髁突后移位相关;然而,对这种移位的早期预测仍然是一项重大挑战。因此,在本研究中,我们旨在开发并评估一种双侧髁突后移位的预测模型。
在这项回顾性观察研究中,检查了166张锥形束计算机断层扫描图像,并根据关节间隙矢状图像中观察到的髁突位置将其分为两组:双侧髁突后移位组和无移位组。使用三种机器学习算法——随机森林、最小绝对收缩和选择算子(LASSO)回归以及极端梯度提升(XGBoost)——来识别风险因素并建立风险评估模型。分别采用校准曲线、受试者工作特征曲线和决策曲线分析来评估预测的准确性、模型的区分度和临床实用性。
关节结节倾斜度(AEI)和年龄被确定为双侧髁突后移位的重要风险因素。LASSO和随机森林模型的曲线下面积值均>0.7,表明列线图具有令人满意的区分能力。三种模型在区分度和校准性能方面未观察到显著差异。临床效用分析显示,当概率阈值超过45%时,纳入年龄、AEI、A点-鼻根点-B点(ANB)角和面部高度比(S-Go/N-Me)的LASSO回归模型显示出比其他模型更好的净效益。
AEI较陡、后垂直距离(S-Go/N-Me)不足、ANB角≥4.7°且年龄较大的患者更有可能发生双侧髁突后移位。本研究开发并验证的预后列线图可能有助于临床医生评估双侧髁突后移位的风险。