{"title":"人工智能在颞下颌关节疾病中的应用综述","authors":"Vini Mehta, Snehasish Tripathy, Toufiq Noor, Ankita Mathur","doi":"10.1002/cre2.70115","DOIUrl":null,"url":null,"abstract":"<div>\n \n \n <section>\n \n <h3> Objectives</h3>\n \n <p>Given the complexity of temporomandibular joint disorders (TMDs) and their overlapping symptoms with other conditions, an accurate diagnosis necessitates a thorough examination, which can be time-consuming and resource-intensive. Consequently, innovative diagnostic tools are required to increase TMD diagnosis efficiency and precision. Therefore, the purpose of this umbrella review was to examine the existing evidence about the usefulness of artificial intelligence (AI) in TMD diagnosis.</p>\n </section>\n \n <section>\n \n <h3> Material and Methods</h3>\n \n <p>A comprehensive search of the literature was performed from inception to November 30, 2024, in PubMed-MEDLINE, Embase, and Scopus databases. This review evaluated systematic reviews (SRs) and meta-analyses (MAs) that reported TMD patients/datasets, any AI model as intervention, no treatment, placebo as comparator and accuracy, sensitivity, specificity, or predictive value of AI models as outcome. The extracted data were complemented with narrative synthesis.</p>\n </section>\n \n <section>\n \n <h3> Results</h3>\n \n <p>Out of 1497 search results, this umbrella review included five studies. One of the five articles was an SR while the other four were SRMAs. Three studies focused on patients with temporomandibular joint (TMJ) problems as a group, whereas two were specific to temporomandibular joint osteoarthritis (TMJOA). The included studies reported the use of imaging datasets as samples, including cone-beam computed tomography (CBCT), magnetic resonance imaging (MRI), and panoramic radiography. The studies reported an accuracy level ranging from 0.59 to 1. Four studies reported sensitivity levels ranging from 0.76 to 0.80. Four studies reported specificity values ranging from 0.63 to 0.95 for TMJ conditions. However, only one study provided the area under the curve (AUC) in the diagnosis of TMDs.</p>\n </section>\n \n <section>\n \n <h3> Conclusions</h3>\n \n <p>AI has the ability to provide faster, more accurate, sensitive, and objective diagnosis of TMJ condition. However, the performance is determined on the AI models and datasets used. Therefore, before implementing AI models in clinical practice, it is essential for researchers to extensively refine and evaluate the AI application.</p>\n </section>\n </div>","PeriodicalId":10203,"journal":{"name":"Clinical and Experimental Dental Research","volume":"11 1","pages":""},"PeriodicalIF":1.7000,"publicationDate":"2025-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/cre2.70115","citationCount":"0","resultStr":"{\"title\":\"Artificial Intelligence in Temporomandibular Joint Disorders: An Umbrella Review\",\"authors\":\"Vini Mehta, Snehasish Tripathy, Toufiq Noor, Ankita Mathur\",\"doi\":\"10.1002/cre2.70115\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div>\\n \\n \\n <section>\\n \\n <h3> Objectives</h3>\\n \\n <p>Given the complexity of temporomandibular joint disorders (TMDs) and their overlapping symptoms with other conditions, an accurate diagnosis necessitates a thorough examination, which can be time-consuming and resource-intensive. Consequently, innovative diagnostic tools are required to increase TMD diagnosis efficiency and precision. Therefore, the purpose of this umbrella review was to examine the existing evidence about the usefulness of artificial intelligence (AI) in TMD diagnosis.</p>\\n </section>\\n \\n <section>\\n \\n <h3> Material and Methods</h3>\\n \\n <p>A comprehensive search of the literature was performed from inception to November 30, 2024, in PubMed-MEDLINE, Embase, and Scopus databases. This review evaluated systematic reviews (SRs) and meta-analyses (MAs) that reported TMD patients/datasets, any AI model as intervention, no treatment, placebo as comparator and accuracy, sensitivity, specificity, or predictive value of AI models as outcome. The extracted data were complemented with narrative synthesis.</p>\\n </section>\\n \\n <section>\\n \\n <h3> Results</h3>\\n \\n <p>Out of 1497 search results, this umbrella review included five studies. One of the five articles was an SR while the other four were SRMAs. Three studies focused on patients with temporomandibular joint (TMJ) problems as a group, whereas two were specific to temporomandibular joint osteoarthritis (TMJOA). The included studies reported the use of imaging datasets as samples, including cone-beam computed tomography (CBCT), magnetic resonance imaging (MRI), and panoramic radiography. The studies reported an accuracy level ranging from 0.59 to 1. Four studies reported sensitivity levels ranging from 0.76 to 0.80. Four studies reported specificity values ranging from 0.63 to 0.95 for TMJ conditions. However, only one study provided the area under the curve (AUC) in the diagnosis of TMDs.</p>\\n </section>\\n \\n <section>\\n \\n <h3> Conclusions</h3>\\n \\n <p>AI has the ability to provide faster, more accurate, sensitive, and objective diagnosis of TMJ condition. However, the performance is determined on the AI models and datasets used. 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Artificial Intelligence in Temporomandibular Joint Disorders: An Umbrella Review
Objectives
Given the complexity of temporomandibular joint disorders (TMDs) and their overlapping symptoms with other conditions, an accurate diagnosis necessitates a thorough examination, which can be time-consuming and resource-intensive. Consequently, innovative diagnostic tools are required to increase TMD diagnosis efficiency and precision. Therefore, the purpose of this umbrella review was to examine the existing evidence about the usefulness of artificial intelligence (AI) in TMD diagnosis.
Material and Methods
A comprehensive search of the literature was performed from inception to November 30, 2024, in PubMed-MEDLINE, Embase, and Scopus databases. This review evaluated systematic reviews (SRs) and meta-analyses (MAs) that reported TMD patients/datasets, any AI model as intervention, no treatment, placebo as comparator and accuracy, sensitivity, specificity, or predictive value of AI models as outcome. The extracted data were complemented with narrative synthesis.
Results
Out of 1497 search results, this umbrella review included five studies. One of the five articles was an SR while the other four were SRMAs. Three studies focused on patients with temporomandibular joint (TMJ) problems as a group, whereas two were specific to temporomandibular joint osteoarthritis (TMJOA). The included studies reported the use of imaging datasets as samples, including cone-beam computed tomography (CBCT), magnetic resonance imaging (MRI), and panoramic radiography. The studies reported an accuracy level ranging from 0.59 to 1. Four studies reported sensitivity levels ranging from 0.76 to 0.80. Four studies reported specificity values ranging from 0.63 to 0.95 for TMJ conditions. However, only one study provided the area under the curve (AUC) in the diagnosis of TMDs.
Conclusions
AI has the ability to provide faster, more accurate, sensitive, and objective diagnosis of TMJ condition. However, the performance is determined on the AI models and datasets used. Therefore, before implementing AI models in clinical practice, it is essential for researchers to extensively refine and evaluate the AI application.
期刊介绍：
Clinical and Experimental Dental Research aims to provide open access peer-reviewed publications of high scientific quality representing original clinical, diagnostic or experimental work within all disciplines and fields of oral medicine and dentistry. The scope of Clinical and Experimental Dental Research comprises original research material on the anatomy, physiology and pathology of oro-facial, oro-pharyngeal and maxillofacial tissues, and functions and dysfunctions within the stomatognathic system, and the epidemiology, aetiology, prevention, diagnosis, prognosis and therapy of diseases and conditions that have an effect on the homeostasis of the mouth, jaws, and closely associated structures, as well as the healing and regeneration and the clinical aspects of replacement of hard and soft tissues with biomaterials, and the rehabilitation of stomatognathic functions. Studies that bring new knowledge on how to advance health on the individual or public health levels, including interactions between oral and general health and ill-health are welcome.
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