[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-124373-en":3,"doc-seo-124373-105":29,"detail-sidebar-cat-0-en-105":90},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":11,"language":21,"language_code":22,"site_id":23,"html_lang":22,"table_of_contents":24,"faqs":25,"seo_title":26,"seo_description":14,"update_tm":27,"read_time":28},124373,13056703019662,"Evangeline","https://ap-avatar.wpscdn.com/avatar/be000253a8e92610077?_k=1778726343310543188",7,"Healthcare","Using machine learning to classify temporomandibular disorders - a proof of concept","Escalating demand for care in temporomandibular disorders (TMD) and frequent diagnostic difficulties for non-specialist dental practitioners highlight the need for automated decision support. This study evaluates a machine-learning model to classify TMDs using structured patient data and the International Classification of Orofacial Pain (ICOP-1). Two decision-tree experiments classify muscular versus articular conditions with shared evaluation metrics. Results show 84% accuracy (f1-score 0.85) for joint pain and 78% accuracy (f1-score 0.76) for myofascial pain, using 2–5 clinical variables. Decision-tree ML supports TMD classification in general practice.","Original Article  \n[http://dx.doi.org/10.1590/1678-7757-2024-0282](http://dx.doi.org/10.1590/1678-7757-2024-0282)  \nUsing machine learning to classify temporomandibular disorders: a proof of concept*  \nFernanda Pretto ZATT¹ | João Victor Cunha CORDEIRO¹ | Lauren BOHNER¹ | Beatriz Dulcineia Mendes de SOUZA¹ | Victor Emanoel Armini CALDAS² | Ricardo Armini CALDAS¹  \n¹Universidade Federal de Santa Catarina (UFSC), Departamento de Odontologia, Florianópolis, Brasil.  \n²Independent Researcher, Amsterdam, Netherlands.  \nAbstract  \nBackground: the escalating influx of patients with temporomandibular disorders and the challenges associated with accurate diagnosis by non-specialized dental practitioners underscore the integration of artificial intelligence into the diagnostic process of temporomandibular disorders (TMD) as a potential solution to mitigate diagnostic disparities associated with this condition. Objectives: In this study, we evaluated a machine-learning model for classifying TMDs based on the International Classification of Orofacial Pain, using structured data. Methodology: Model construction was performed by the exploration of a dataset comprising patient records from the repository of the Multidisciplinary Orofacial Pain Center (CEMDOR) affiliated with the Federal University of Santa Catarina . Diagnoses of TMD were categorized following the principles established by the International Classification of Orofacial Pain (ICOP-1) . Two independent experiments were conducted using the decision tree technique to classify muscular or articular conditions. Both experiments uniformly adopted identical metrics to assess the developed model’s performance and efficacy. Results: The classification model for joint pain showed a relevant potential for general practitioners, presenting 84% accuracy and f1-score of 0.85. Thus, myofascial pain was classified with 78% accuracy and an f1-score of 0.76. Both models used from 2 to 5 clinical variables to classify orofacial pain. Conclusion: The use of decision tree-based machine learning holds significant support potential for TMD classification.  \n Keywords: Artificial intelligence. Machine learning. Facial pain. Diagnosis.  \n*The article is a study produced from a master dissertation available from:[https://repositorio.ufsc.br/xmlui/handle/123456789/251470](https://repositorio.ufsc.br/xmlui/handle/123456789/251470)  \nCorresponding address:  \nFernanda Pretto Zatt-Universidade Federal de Santa CatarinaDepartamento de Odontologia-Rua Delfino Conti, 1240-88040-535 -Florianópolis-SC-Brasil.  \nPhone: +55 48 3721 4952  \ne-mail: [ferdpretto@gmail.com](ferdpretto@gmail.com)  \nReceived: July 8, 2024  \nRevised: September 14, 2024  \nAccepted: September 18, 2024  \nEditor: Linda Wang  \nAssociate Editor: Paulo César Conti  \nISSN 1678-7765 J Appl Oral Sci. 1/7 2024;32:e20240282  \nIntroduction  \nTemporomandibular disorders (TMD) encompass a diverse array of conditions that affect the masticatory muscles, the temporomandibular joint (TMJ), and their associated structures.1 These conditions may include abroad spectrum of signs and symptoms, ranging from an isolated condition to the involvement of multiple systems . This complexity renders the diagnosis of TMDs a challenging task with a significant potential for diagnostic errors, particularly for non-specialist professionals in the field.2–6 The classification of TMDs is typically grounded in international consensus among experts7,8 and entails a comprehensive patient assessment that takes many factors into account.  \nThe International Classification of Orofacial Pain ( ICOP-1) is the first comprehensive classification system solely dedicated to orofacial pain. This classification was modeled after the structure of the International Classification of Headache Disorders (ICHD), which is widely accepted and globally used by medical practitioners and researchers.7,9 However, these conditions are often underdiagnosed or receive limited attention in oral healthc","cbCaiuUD9pqGl9Kk","https://ap.wps.com/l/cbCaiuUD9pqGl9Kk","pdf",1962079,1,"English","en",105,"# Abstract\n## Background and objectives\n## Methodology\n## Results and conclusion\n# Introduction\n## TMD complexity and diagnostic challenges\n## International Classification of Orofacial Pain (ICOP-1)\n## Educational gaps and emerging AI tools","[{\"question\":\"What problem does the study address in temporomandibular disorder diagnosis?\",\"answer\":\"The study targets diagnostic disparities caused by difficulties in accurately diagnosing TMDs, especially by non-specialist dental practitioners.\"},{\"question\":\"How were TMD diagnoses categorized in the machine-learning model?\",\"answer\":\"Diagnoses were categorized according to ICOP-1 (International Classification of Orofacial Pain), using structured patient records from CEMDOR.\"},{\"question\":\"What were the main performance results of the decision-tree models?\",\"answer\":\"The joint pain model reached 84% accuracy with an f1-score of 0.85, while myofascial pain classification reached 78% accuracy with an f1-score of 0.76.\"}]","Using machine learning to classify temporomandibular disorders - a proof of concept | PDF",1785821874,18,{"code":4,"msg":30,"data":31},"ok",{"site_id":23,"language":22,"slug":32,"title":13,"keywords":33,"description":14,"schema_data":34,"social_meta":85,"head_meta":87,"extra_data":89,"updated_unix":27},"using-machine-learning-to-classify-temporomandibular-disorders-a-proof-of-concept","",{"@graph":35,"@context":84},[36,53,67],{"@type":37,"itemListElement":38},"BreadcrumbList",[39,43,47,50],{"item":40,"name":41,"@type":42,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":44,"name":45,"@type":42,"position":46},"https://docshare.wps.com/document/","Document",2,{"item":48,"name":12,"@type":42,"position":49},"https://docshare.wps.com/document/healthcare/",3,{"item":51,"name":13,"@type":42,"position":52},"https://docshare.wps.com/document/using-machine-learning-to-classify-temporomandibular-disorders-a-proof-of-concept/124373/",4,{"url":51,"name":13,"@type":54,"author":55,"headline":13,"publisher":57,"fileFormat":60,"inLanguage":22,"description":14,"dateModified":61,"datePublished":61,"encodingFormat":60,"isAccessibleForFree":62,"interactionStatistic":63},"DigitalDocument",{"name":9,"@type":56},"Person",{"url":40,"name":58,"@type":59},"DocShare","Organization","application/pdf","2026-08-04",true,{"@type":64,"interactionType":65,"userInteractionCount":4},"InteractionCounter",{"@type":66},"ViewAction",{"@type":68,"mainEntity":69},"FAQPage",[70,76,80],{"name":71,"@type":72,"acceptedAnswer":73},"What problem does the study address in temporomandibular disorder diagnosis?","Question",{"text":74,"@type":75},"The study targets diagnostic disparities caused by difficulties in accurately diagnosing TMDs, especially by non-specialist dental practitioners.","Answer",{"name":77,"@type":72,"acceptedAnswer":78},"How were TMD diagnoses categorized in the machine-learning model?",{"text":79,"@type":75},"Diagnoses were categorized according to ICOP-1 (International Classification of Orofacial Pain), using structured patient records from CEMDOR.",{"name":81,"@type":72,"acceptedAnswer":82},"What were the main performance results of the decision-tree models?",{"text":83,"@type":75},"The joint pain model reached 84% accuracy with an f1-score of 0.85, while myofascial pain classification reached 78% accuracy with an f1-score of 0.76.","https://schema.org",{"og:url":51,"og:type":86,"og:title":13,"og:site_name":58,"og:description":14},"article",{"robots":88,"canonical":51},"index,follow",{"doc_id":7,"site_id":23},{"code":4,"msg":5,"data":91},[92,96,100,104,109,114,117,122,127,130,134],{"id":20,"doc_module":4,"doc_module_name":45,"category_name":93,"show_sort_weight":94,"slug":95},"Story & Novel",90,"story-novel",{"id":46,"doc_module":4,"doc_module_name":45,"category_name":97,"show_sort_weight":98,"slug":99},"Literature",80,"literature",{"id":52,"doc_module":4,"doc_module_name":45,"category_name":101,"show_sort_weight":102,"slug":103},"Exam",70,"exam",{"id":105,"doc_module":4,"doc_module_name":45,"category_name":106,"show_sort_weight":107,"slug":108},5,"Comic",60,"comic",{"id":110,"doc_module":4,"doc_module_name":45,"category_name":111,"show_sort_weight":112,"slug":113},6,"Technology",50,"technology",{"id":11,"doc_module":4,"doc_module_name":45,"category_name":12,"show_sort_weight":115,"slug":116},40,"healthcare",{"id":118,"doc_module":4,"doc_module_name":45,"category_name":119,"show_sort_weight":120,"slug":121},8,"Research & Report",30,"research-report",{"id":123,"doc_module":4,"doc_module_name":45,"category_name":124,"show_sort_weight":125,"slug":126},9,"Religion & Spirituality",20,"religion-spirituality",{"id":125,"doc_module":4,"doc_module_name":45,"category_name":128,"show_sort_weight":125,"slug":129},"World Cup","world-cup",{"id":131,"doc_module":4,"doc_module_name":45,"category_name":132,"show_sort_weight":131,"slug":133},10,"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":45,"category_name":136,"show_sort_weight":105,"slug":137},19,"General","general"]