北京大学学报(医学版) ›› 2025, Vol. 57 ›› Issue (1): 192-201. doi: 10.19723/j.issn.1671-167X.2025.01.029

• 技术方法 • 上一篇    下一篇

基于颞下颌关节紊乱病诊断标准的临床自动诊断系统的建立及验证

方媛媛1, 徐帆2, 雷杰1, 张昊2, 张文宇2, 孙宇2, 吴宏新2, 傅开元1,*(), 毛伟玉1,*()   

  1. 1. 北京大学口腔医学院 · 口腔医院医学影像科,颞下颌关节病口颌面疼痛诊治中心,国家口腔医学中心,国家口腔疾病临床医学研究中心,口腔生物材料和数字诊疗装备国家工程研究中心,口腔数字医学北京市重点实验室,北京 100081
    2. 北京朗视仪器股份有限公司,北京 100084
  • 收稿日期:2024-09-23 出版日期:2025-02-18 发布日期:2025-01-25
  • 通讯作者: 傅开元,毛伟玉 E-mail:kqkyfu@bjmu.edu.cn;maoweiyupumch@163.com
  • 基金资助:
    国家重点研发计划(2023YFC2509200);北京大学口腔医院临床新技术新疗法项目(PKUSSNCT-22B13);北京市自然科学基金-海淀原始创新联合基金(L232112);北京大学口腔医(学)院临床研究基金(PKUSS-2023CRF206)

Development and validation of a clinical automatic diagnosis system based on diagnostic criteria for temporomandibular disorders

Yuanyuan FANG1, Fan XU2, Jie LEI1, Hao ZHANG2, Wenyu ZHANG2, Yu SUN2, Hongxin WU2, Kaiyuan FU1,*(), Weiyu MAO1,*()   

  1. 1. 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
    2. LargeV Instrument Corp. Ltd., Beijing 100084, China
  • Received:2024-09-23 Online:2025-02-18 Published:2025-01-25
  • Contact: Kaiyuan FU, Weiyu MAO E-mail:kqkyfu@bjmu.edu.cn;maoweiyupumch@163.com
  • Supported by:
    the National Key Research and Development Program of China(2023YFC2509200);the Program for New Clinical Techniques and Therapies of Peking University School and Hospital of Stomatology(PKUSSNCT-22B13);Beijing Natural Science Foundation(L232112);the Clinical Research Foundation of Peking University School and Hospital of Stomatology(PKUSS-2023CRF206)

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摘要:

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。

关键词: 颞下颌关节紊乱病, 计算机辅助诊断, 自动化, 敏感性与特异性

Abstract:

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.

Key words: Temporomandibular joint disorders, Computer-assisted diagnosis, Automation, Sensitivity and specificity

中图分类号: 

  • R782.6

表1

DC/TMD诊断用病史和临床检查记录表"

Chief complaint
Left side (1) Right side (2)        
 
History
1. Have you ever had pain in your jaw, temple, in the ear, or in front of the ear on either side? Yes (3) No
2. In the last 30 days, have you had any headaches that included the temple areas of your head? Yes (4) No
3. In the last 30 days, have you had any jaw joint noise(s) when you moved or used your jaw? Yes (5) No
4. Have you ever had your jaw catch, even for a moment, so that it would not open all the way? Yes (6) No
5. Have you ever had your jaw lock, which affects eating? Yes (7) No
6. In the last 30 days, when you opened your mouth wide, did your jaw lock or catch even for a moment such that you could not close it from the wide-open position? Yes (8) No
 
Clinical examination
1. Palpation
Right side: Left side:
Temporalis No Pain Familiar pain (9) Headache (10) Temporalis No Pain Familiar pain (11) Headache (10)
Masseter No Pain Familiar pain (12) Headache (10) Masseter No Pain Familiar Pain (13) Headache (10)
Joint No Pain Familiar pain (14) Headache (10) Joint No Pain Familiar pain (15) Headache (10)
2. Joint noises
Left side No Open & close movement (16) Open or close movement (17) Lateral movement (18) Protrusive movement (19)
Right side No Open & close movement (20) Open or close movement (21) Lateral movement (22) Protrusive movement (23)
Left side No Crepitus (24) Joint locking (25)    
Right side No Crepitus (26) Joint locking (27)    
3. Mouth opening
>Maximum unassisted opening __ <35 mm (28) ≥35 mm (29) Pain on right joint (30) Pain on right muscles (31) Headache on right side (32)  
Pain on left joint (33) Pain on left muscles (34) Headache on left side (35)  
Maximum assisted opening __ Pain on right joint (30) Pain on right muscles (31) Headache on right side (32) Pain on left joint (33) Pain on left muscles (34) Headache on left side (35)
Protrusive movement __ Pain on right joint (30) Pain on right muscles (31) Headache on right side (32) Pain on left joint (33) Pain on left muscles (34) Headache on left side (35)
Left Lateral movement __ Pain on right joint (30) Pain on right muscles (31) Headache on right side (32) Pain on left joint (33) Pain on left muscles (34) Headache on left side (35)
Right Lateral movement __ Pain on right joint (30) Pain on right muscles (31) Headache on right side (32) Pain on left joint (33) Pain on left muscles (34) Headache on left side (35)
4. Opening pattern
Straight Intermittent locking while opening Deviation to left (36) Deviation to right (37) Corrected deviation

图1

颞下颌关节紊乱病疼痛类疾病的临床诊断流程"

表2

颞下颌关节紊乱病疼痛类疾病的临床诊断方法"

Disease Side Number of diagnosis*
Arthralgia Left 3+15 or/and 33
Right 3+14 or/and 30
Myalgia   3+9 or/and 11 or/and 12 or/and 13 or/and 31 or/and 34
Headache attributed to temporomandibular disorders   4+10 or/and 32 or/and 35

图2

颞下颌关节紊乱病关节类疾病的临床诊断流程"

表3

颞下颌关节紊乱病关节类疾病的临床诊断方法"

Disease Side Number of diagnosis*
Disc displacement with reductionLeft 5+16 or 5+17+18 or 5+17+19
Right 5+20 or 5+21+22 or 5+21+23
Disc displacement with reduction with intermittent lockingLeft 6+16 or 6+16+25 or 6+17+18 or 6+17+18+25 or 6+17+19 or 6+17+19+25
Right 6+20 or 6+20+27 or 6+21+22 or 6+21+22+27 or 6+21+23 or 6+21+23+27
Disc displacement without reduction with limited openingLeft 7+28+1 or/and 36
Right 7+28+2 or/and 37
Disc displacement without reduction without limited openingLeft 7+29+1 or/and 36
Right 7+29+2 or/and 37
Degenerative joint diseaseLeft 5+24
Right 5+26
SubluxationLeft 1+8
Right 2+8

图3

颞下颌关节紊乱病临床自动诊断系统的操作流程图"

图4

颞下颌关节紊乱病临床自动诊断系统的架构设计图"

表4

基于DC/TMD的TMD临床自动诊断系统的诊断符合率"

Disease DC/TMD, n Clinical automatic diagnosis system for TMD, n Consistency
Arthralgia 44 44 100%
Myalgia 196 196 100%
Headache attributed to TMD 4 4 100%
Disc displacement with reduction 43 43 100%
Disc displacement with reduction with intermittent 35 35 100%
Disc displacement without reduction with limited opening 76 76 100%
Disc displacement without reduction without limited opening 61 61 100%
Degenerative joint disease 50 50 100%
Subluxation 3 3 100%
Total 512 512 100%

表5

TMD临床自动诊断系统与专家对于退行性关节病的诊断效能"

Items DC/TMD* Clinical automatic diagnosis system for TMD Expert
TP/P, n/n 31/128 47/128
TN/N, n/n   123/128 123/128
Sensitivity (95%CI) 0.55 0.24 (0.17, 0.32) 0.37 (0.28, 0.45)
Specificity (95%CI) 0.61 0.96 (0.93, 0.99) 0.96 (0.93, 0.99)
Accuracy (95%CI)   0.60 (0.54, 0.66) 0.66 (0.61, 0.72)
Kappa value   0.79  

表6

TMD临床自动诊断系统与专家对于可复性关节盘移位的诊断效能"

Items DC/TMD* Clinical automatic diagnosis system for TMD Expert
TP/P, n/n 18/27 19/27
TN/N, n/n   122/127 119/127
Sensitivity (95%CI) 0.34 0.67 (0.49, 0.84) 0.70 (0.53, 0.88)
Specificity (95%CI) 0.92 0.96 (0.93, 1.00) 0.94 (0.89, 0.93)
Accuracy (95%CI)   0.91 (0.87, 0.95) 0.90 (0.85, 0.94)
Kappa value   0.92  

表7

TMD临床自动诊断系统与专家对于不可复性关节盘移位伴开口受限的诊断效能"

Items DC/TMD* Clinical automatic diagnosis system for TMD Expert
TP/P, n/n   48/53 52/53
TN/N, n/n   93/101 93/101
Sensitivity (95%CI) 0.80 0.90 (0.83, 0.98) 0.98 (0.97, 1.00)
Specificity (95%CI) 0.97 0.92 (0.87, 0.97) 0.92 (0.87, 0.97)
Accuracy (95%CI)   0.92 (0.87, 0.96) 0.95 (0.90, 0.98)
Kappa value   0.93  

表8

TMD临床自动诊断系统与专家对于不可复性关节盘移位无开口受限的诊断效能"

Items DC/TMD* Clinical automatic diagnosis system for TMD Expert
TP/P, n/n   31/53 46/53
TN/N, n/n   93/101 93/101
Sensitivity (95%CI) 0.54 0.59 (0.45, 0.72) 0.87 (0.78, 0.96)
Specificity (95%CI) 0.79 0.92 (0.87, 0.97) 0.92 (0.87, 0.97)
Accuracy (95%CI)   0.81 (0.74, 0.87) 0.90 (0.86, 0.95)
Kappa value   0.68  
1 Iturriaga V , Bornhardt T , Velasquez N . Temporomandibular joint: Review of anatomy and clinical implications[J]. Dent Clin North Am, 2023, 67 (2): 199- 209.
doi: 10.1016/j.cden.2022.11.003
2 Valesan LF , Da-Cas CD , Réus JC , et al. Prevalence of temporomandibular joint disorders: A systematic review and meta-analysis[J]. Clin Oral Investig, 2021, 25 (2): 441- 453.
doi: 10.1007/s00784-020-03710-w
3 Cao Y , Yap AU , Lei J , et al. Oral health-related quality of life of patients with acute and chronic temporomandibular disorder diagnostic subtypes[J]. J Am Dent Assoc, 2022, 153 (1): 50- 58.
doi: 10.1016/j.adaj.2021.07.011
4 Jung HD , Kim SY , Park HS , et al. Orthognathic surgery and temporomandibular joint symptoms[J]. Maxillofac Plast Reconstr Surg, 2015, 37 (1): 14.
doi: 10.1186/s40902-015-0014-4
5 Michelotti A , Rongo R , D'antò V , et al. Occlusion, orthodontics, and temporomandibular disorders: Cutting edge of the current evidence[J]. J World Fed Orthod, 2020, 9 (Suppl 3): S15- S18.
6 Din NMU , Dar RA , Rasool M , et al. Breast cancer detection using deep learning: Datasets, methods, and challenges ahead[J]. Comput Biol Med, 2022, 149, 106073.
doi: 10.1016/j.compbiomed.2022.106073
7 Chan HP , Hadjiiski LM , Samala RK . Computer-aided diagnosis in the era of deep learning[J]. Med Phys, 2020, 47 (5): e218- e227.
8 Wu S , Roberts K , Datta S , et al. Deep learning in clinical natural language processing: A methodical review[J]. J Am Med Inform Assoc, 2020, 27 (3): 457- 470.
doi: 10.1093/jamia/ocz200
9 邵毅, 张铭志, 许言午, 等. 人工智能在视网膜图像自动分割和疾病诊断中的应用指南(2024)[J]. 眼科新进展, 2024, 44 (8): 592- 601.
10 魏世成, 王翰章. 颞颌关节紊乱综合征专家系统的设计与实现[J]. 华西口腔医学杂志, 1990, (3): 216- 220.
11 Schiffman E , Ohrbach R , Truelove E , et al. Diagnostic criteria for temporomandibular disorders (DC/TMD) for clinical and research applications: Recommendations of the International RDC/TMD Consortium Network* and Orofacial Pain Special Interest Group[J]. J Oral Facial Pain Headache, 2014, 28 (1): 6- 27.
doi: 10.11607/jop.1151
12 傅开元. 2014年新版国际颞下颌关节紊乱病分类及诊断标准解读[J]. 中华口腔医学杂志, 2017, 52 (6): 374- 376.
doi: 10.3760/cma.j.issn.1002-0098.2017.06.010
13 Minervini G , Marrapodi MM , Siurkel Y , et al. Accuracy of temporomandibular disorders diagnosis evaluated through the diagnostic criteria for temporomandibular disorder (DC/TDM) axis Ⅱ compared to the axis Ⅰ evaluations: A systematic review and meta-analysis[J]. BMC Oral Health, 2024, 24 (1): 299.
doi: 10.1186/s12903-024-03983-7
14 傅开元, 雷杰. 颞下颌关节紊乱病的分类、诊断及治疗进展[J]. 口腔医学, 2024, 44 (1): 6- 10.
15 Zhang XL , Sun JL , He DM . Review of the studies on the relationship and treatment of anterior disk displacement and dentofacial deformity in adolescents[J]. Oral Surg Oral Med Oral Pathol Oral Radiol, 2023, 135 (4): 470- 474.
doi: 10.1016/j.oooo.2022.07.018
16 Bas B , Ozgonenel O , Ozden B , et al. Use of artificial neural network in differentiation of subgroups of temporomandibular internal derangements: A preliminary study[J]. J Oral Maxillofac Surg, 2012, 70 (1): 51- 59.
doi: 10.1016/j.joms.2011.03.069
17 Tașkıran U , Çunkaș M . A deep learning based decision support system for diagnosis of temporomandibular joint disorder[J]. Applied Acoustics, 2021, 182, 108292.
doi: 10.1016/j.apacoust.2021.108292
18 Sharma N, Dar IG, Kumar J, et al. Temporomandibular joint syndrome prediction using neural network[C]/ /Ray K, Sharan SN, Rawat S, et al. Proceedings of the Engineering Vibration, Communication and Information Processing: ICoEVCI 2018, India. Singapore: Springer, 2019: 1-8.
19 Yap AU , Lei J , Zhang XH , et al. TMJ degenerative joint disease: Relationships between CBCT findings, clinical symptoms, and signs[J]. Acta Odontol Scand, 2023, 81 (7): 562- 568.
doi: 10.1080/00016357.2023.2215317
20 Patel A , Tee BC , Fields H , et al. Evaluation of cone-beam computed tomography in the diagnosis of simulated small osseous defects in the mandibular condyle[J]. Am J Orthod Dentofacial Orthop, 2014, 145 (2): 143- 156.
doi: 10.1016/j.ajodo.2013.10.014
21 Yadav S , Palo L , Mahdian M , et al. Diagnostic accuracy of 2 cone-beam computed tomography protocols for detecting arthritic changes in temporomandibular joints[J]. Am J Orthod Dentofacial Orthop, 2015, 147 (3): 339- 344.
doi: 10.1016/j.ajodo.2014.11.017
22 Lee KS , Kwak HJ , Oh JM , et al. Automated detection of TMJ osteoarthritis based on artificial intelligence[J]. J Dent Res, 2020, 99 (12): 1363- 1367.
doi: 10.1177/0022034520936950
23 Choi E , Kim D , Lee JY , et al. Artificial intelligence in detecting temporomandibular joint osteoarthritis on orthopantomogram[J]. Sci Rep, 2021, 11 (1): 10246.
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