Development and validation of a predictive nomogram for bilateral posterior condylar displacement using cone-beam computed tomography and machine-learning algorithms: a retrospective observational study.

IF 3.1 2区 医学 Q1 DENTISTRY, ORAL SURGERY & MEDICINE
Huachao Sui, Mo Xiao, Xueqing Jiang, Jiaye Li, Feng Qiao, Bin Yin, Yuanyuan Wang, Ligeng Wu
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引用次数: 0

Abstract

Background: 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.

Methods: 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.

Results: 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%.

Conclusion: 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.

利用锥束计算机断层扫描和机器学习算法开发和验证双侧后髁移位的预测图:一项回顾性观察研究。
背景:颞下颌紊乱(TMDs)常与后髁移位相关;然而,对这种位移的早期预测仍然是一个重大挑战。因此,在本研究中,我们旨在建立并评估双侧后髁移位的预测模型。方法:回顾性观察166张锥形束ct图像,根据关节间隙矢状位图像中髁的位置将其分为两组:双侧后髁移位组和无后髁移位组。三种机器学习算法-随机森林,最小绝对收缩和选择算子(LASSO)回归和极端梯度增强(XGBoost)-用于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模型的预测准确性、差异性和临床实用性。结果:关节隆起倾斜(AEI)和年龄是双侧后髁移位的重要危险因素。LASSO和Random Forest模型的曲线下面积均为> 0.7,说明nomogram具有较好的判别能力。三种模型的分化和校准性能均无显著差异。临床效用分析显示,当概率阈值超过45%时,纳入年龄、AEI、A点- b点(ANB)角度和面部高度比(S-Go/N-Me)的LASSO回归模型的净效益优于其他模型。结论:AEI较陡、后侧垂直距离(S-Go/N-Me)不足、ANB角≥4.7°、年龄较大的患者更容易发生双侧后髁移位。本研究开发并验证的预后图可以帮助临床医生评估双侧后髁移位的风险。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
BMC Oral Health
BMC Oral Health DENTISTRY, ORAL SURGERY & MEDICINE-
CiteScore
3.90
自引率
6.90%
发文量
481
审稿时长
6-12 weeks
期刊介绍: BMC Oral Health is an open access, peer-reviewed journal that considers articles on all aspects of the prevention, diagnosis and management of disorders of the mouth, teeth and gums, as well as related molecular genetics, pathophysiology, and epidemiology.
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