Fang Xinyi, Xiong Xin, Lin Jiu, Wu Yange, Xiang Jie, Wang Jun
State Key Laboratory of Oral Diseases, National Clinical Research Center for Oral Diseases, West China Hospital of Stomatology, Sichuan University, Chengdu, Sichuan, China; Department of Orthodontics, Hospital of Stomatology, Key Laboratory of Oral Biomedical Research of Zhejiang Province, School of Stomatology, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China.
State Key Laboratory of Oral Diseases, National Clinical Research Center for Oral Diseases, West China Hospital of Stomatology, Sichuan University, Chengdu, Sichuan, China.
Am J Orthod Dentofacial Orthop. 2023 Feb;163(2):260-271.e5. doi: 10.1016/j.ajodo.2022.10.015.
Degenerative temporomandibular joint diseases (DJDs) are common diseases in dental practice, characterized by a series of degenerative processes in the temporomandibular joint. Early clinical detection of DJD by dental practitioners can be beneficial to prevent or alleviate the further progression of the disease. This study aimed to develop a cephalogram-based multidimensional nomogram to screen DJD.
A total of 502 patients (170 normal and 332 with DJD) were randomly assigned to a training set (n = 351) or a validation set (n = 151). Thirty-six cephalometric parameters were extracted from the cephalograms to be used as input for a predictive machine-learning algorithm. Multivariable logistic regression was used to construct a combined model for visualization in the form of a nomogram. Receiver operating characteristic curve, calibration testing, and decision curve analyses were conducted to evaluate the performance of the combined model.
A Ceph score consisting of 22 cephalometric parameters were significantly associated with DJD (P <0.01). A combined model that consisted of Ceph scores and clinical features (including age, gender, limited mouth opening, crepitus, etc.) performed well in the receiver operating characteristic curve (area under the curve, 0.893), calibration test, and decision curve analyses, indicating its potential clinical value.
This study constructed and verified a multidimensional nomogram consisting of Ceph scores and clinical features, which may contribute to the clinical screening of DJD in dental practice. Future studies are needed to test the reliability of the model with similar parameters.
退行性颞下颌关节疾病（DJD）是牙科临床常见疾病，其特征为颞下颌关节出现一系列退行性病变。牙科医生对DJD进行早期临床检测有助于预防或减轻疾病的进一步发展。本研究旨在开发一种基于头影测量的多维列线图以筛查DJD。
总共502例患者（170例正常，332例患有DJD）被随机分配至训练集（n = 351）或验证集（n = 151）。从头影测量图中提取36个头影测量参数，用作预测性机器学习算法的输入。采用多变量逻辑回归构建一个以列线图形式呈现的可视化组合模型。进行受试者工作特征曲线分析、校准测试和决策曲线分析以评估该组合模型的性能。
由22个头影测量参数组成的Ceph评分与DJD显著相关（P <0.01）。由Ceph评分和临床特征（包括年龄、性别、张口受限、关节弹响等）组成的组合模型在受试者工作特征曲线分析（曲线下面积为0.893）、校准测试和决策曲线分析中表现良好，表明其具有潜在的临床价值。
本研究构建并验证了一个由Ceph评分和临床特征组成的多维列线图，这可能有助于牙科临床对DJD进行筛查。未来需要开展研究以用类似参数测试该模型的可靠性。
