Fang Yuanyuan, Xu Fan, Lei Jie, Zhang Hao, Zhang Wenyu, Sun Yu, Wu Hongxin, Fu Kaiyuan, Mao Weiyu
Department of Oral and Maxillofacial Radiology, Center for TMD & Orofacial Pain, Peking University School and Hospital of Stomatology & National Center for Stomatology & National Clinical Research Center for Oral Diseases & National Engineering Research Center of Oral Biomaterials and Digital Medical Devices & Beijing Key Laboratory of Digital Stomato-logy, Beijing 100081, China.
LargeV Instrument Corp. Ltd., Beijing 100084, China.
Beijing Da Xue Xue Bao Yi Xue Ban. 2025 Feb 18;57(1):192-201. doi: 10.19723/j.issn.1671-167X.2025.01.029.
To develop a clinical automated diagnostic system for temporomandibular disorders (TMD) based on the diagnostic criteria for TMD (DC/TMD) to assist dentists in making rapid and accurate clinical diagnosis of TMD.
Clinical and imaging data of 354 patients, who visited the Center for TMD & Orofacial Pain at Peking University Hospital of Stomatology from September 2023 to January 2024, were retrospectively collected. The study developed a clinical automated diagnostic system for TMD using the DC/TMD, built on the. NET Framework platform with branching statements as its internal structure. Further validation of the system on consistency and diagnostic efficacy compared with DC/TMD were also explored. Diagnostic efficacy of the TMD clinical automated diagnostic system for degenerative joint diseases, disc displacement with reduction, disc displacements without reduction with limited mouth opening and disc displacement without reduction without limited mouth opening was evaluated and compared with a specialist in the field of TMD. Accuracy, precision, specificity and the Kappa value were assessed between the TMD clinical automated diagnostic system and the specialist.
Diagnoses for various TMD subtypes, including pain-related TMD (arthralgia, myalgia, headache attributed to TMD) and intra-articular TMD (disc displacement with reduction, disc displacement with reduction with intermittent locking, disc displacement without reduction with limited opening, disc displacement without reduction without limited opening, degenerative joint disease and subluxation), using the TMD clinical automated diagnostic system were completely identical to those obtained by the TMD specialist based on DC/TMD. Both the system and the expert showed low sensitivity for diagnosing degenerative joint disease (0.24 and 0.37, respectively), but high specificity (0.96). Both methods achieved high accuracy (> 0.9) for diagnosing disc displacements with reduction and disc displacements without reduction with limited mouth opening. The sensitivity for diagnosing disc displacement without reduction without limited mouth opening was only 0.59 using the automated system, lower than the expert (0.87), while both had high specificity (0.92). The Kappa values for most TMD subtypes were close to 1, except the disc displacement without reduction without limited mouth opening, which had a Kappa value of 0.68.
This study developed and validated a reliable clinical automated diagnostic system for TMD based on DC/TMD. The system is designed to facilitate the rapid and accurate diagnosis and classification of TMD, and is expected to be an important tool in clinical scenarios.
基于颞下颌关节紊乱病（TMD）的诊断标准（DC/TMD）开发一种临床自动诊断系统，以协助牙医对TMD进行快速准确的临床诊断。
回顾性收集2023年9月至2024年1月在北京口腔医院颞下颌关节病及口颌面疼痛中心就诊的354例患者的临床和影像数据。本研究使用DC/TMD开发了一种用于TMD的临床自动诊断系统，该系统基于.NET框架平台构建，内部结构采用分支语句。还探讨了该系统与DC/TMD在一致性和诊断效能方面的进一步验证。评估了TMD临床自动诊断系统对退行性关节疾病、可复性盘移位、不可复性盘移位伴张口受限和不可复性盘移位不伴张口受限的诊断效能，并与TMD领域的专家进行比较。评估了TMD临床自动诊断系统与专家之间的准确性、精确性、特异性和Kappa值。
使用TMD临床自动诊断系统对各种TMD亚型的诊断，包括疼痛相关的TMD（关节痛、肌痛、归因于TMD的头痛）和关节内TMD（可复性盘移位、可复性盘移位伴间歇性绞锁、不可复性盘移位伴张口受限、不可复性盘移位不伴张口受限、退行性关节疾病和半脱位），与TMD专家基于DC/TMD得出的诊断完全相同。该系统和专家对退行性关节疾病的诊断敏感性均较低（分别为0.24和0.37），但特异性较高（0.96）。两种方法对可复性盘移位和不可复性盘移位伴张口受限的诊断准确性均较高（>0.9）。使用自动系统对不可复性盘移位不伴张口受限的诊断敏感性仅为0.59，低于专家（0.87），而两者的特异性均较高（0.92）。除不可复性盘移位不伴张口受限的Kappa值为0.68外，大多数TMD亚型的Kappa值接近1。
本研究开发并验证了一种基于DC/TMD的可靠的TMD临床自动诊断系统。该系统旨在促进TMD的快速准确诊断和分类，有望成为临床场景中的重要工具。
