目的: 拟建立基于颞下颌关节紊乱病诊断标准(diagnostic criteria for temporomandibular disorders，DC/TMD)的颞下颌关节紊乱病(temporomandibular disorders，TMD)临床自动诊断系统，以帮助口腔医师快速且准确地做出TMD的临床诊断。方法: 回顾性收集2023年9月至2024年1月就诊于北京大学口腔医院颞下颌关节病口颌面疼痛诊治中心的354例患者临床及影像学资料。基于DC/TMD，采用. NET Framework平台开发并以分支语句作为内部构架搭建TMD临床自动诊断系统，并验证该系统与DC/TMD的符合率。对于退行性关节病、可复性关节盘移位、不可复性关节盘移位伴开口受限、不可复性关节盘移位无开口受限，以影像学检查并结合临床最大主动开口度作为金标准，评估该系统对这4种疾病的诊断效能并与专家诊断结果相比较。结果: TMD临床自动诊断系统诊断TMD各亚型疾病的结果(包括疼痛类疾病和关节类疾病)与专科医师采用DC/TMD所得诊断结果符合率均为100%。TMD临床自动诊断系统及专家对于退行性关节病的诊断灵敏度较低，分别为0.24和0.37，而特异度均高达0.96。两种方法对于可复性关节盘移位和不可复性关节盘移位伴开口受限的诊断准确度均达到0.9以上；TMD临床自动诊断系统对于不可复性关节盘移位无开口受限的诊断灵敏度为0.59, 相对专家(0.87)较低，但特异度两者均较高(0.92)。TMD临床自动诊断系统对于大部分TMD亚型的诊断结果与专家诊断结果的Kappa值接近1，仅不可复性关节盘移位无开口受限的Kappa值为0.68。结论: 本研究开发并验证评估了一种基于DC/TMD的TMD临床自动诊断系统，该系统可以帮助口腔医师快速、准确诊断并分类TMD，有望成为辅助TMD诊断的重要工具。

Objective: 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. Methods: 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. Results: 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. Conclusion: 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.