{"title":"人工智能和机器学习在颞下颌疾病诊断和管理中的应用:系统综述和荟萃分析","authors":"Vaishnavi Rajaraman, Deepak Nallaswamy, Amrutha Shenoy","doi":"10.1016/j.jobcr.2025.09.013","DOIUrl":null,"url":null,"abstract":"<div><h3>Background</h3><div>Artificial intelligence (AI) and machine learning (ML) models have recently emerged as promising tools for enhancing diagnostic accuracy.</div></div><div><h3>Objective</h3><div>To evaluate the diagnostic accuracy of AI/ML models in detecting TMDs through a systematic review and meta-analysis of existing literature.</div></div><div><h3>Methods</h3><div>A comprehensive search of electronic databases was conducted to identify studies assessing the diagnostic performance of AI/ML models in TMD diagnosis (PROSPERO-CRD420251035080). Data extraction and quality assessment were conducted independently by two reviewers using the AXIS tool for cross-sectional and Newcastle–Ottawa Scale for cohort studies. Meta-analysis of diagnostic accuracy was performed using pooled sensitivity, specificity, diagnostic odds ratio, and area under the curve. Statistical heterogeneity was assessed with the I<sup>2</sup> statistic.</div></div><div><h3>Results</h3><div>The systematic search identified 368 articles, of which 12 studies met inclusion criteria after screening. Risk of bias assessment showed most observational studies had low to unclear bias, while cross-sectional studies varied from moderate to high quality. Five studies were eligible for meta-analysis and they revealed that AI and machine learning models achieved a pooled sensitivity of 87.1 %(95 %CI:84.9 %–89.2 %) and specificity of 87.0 %(95 %CI:84.8 %–89.2 %) for TMD diagnosis. The diagnostic odds ratio was 45.1(95 %CI:30.5–66.8), with an area under the ROC curve of 0.96, indicating excellent diagnostic accuracy. Moderate heterogeneity I<sup>2</sup> = 38.7 %.</div></div><div><h3>Conclusion</h3><div>AI/ML models demonstrate excellent accuracy in differentiating patients with and without TMDs, reinforcing their potential as reliable diagnostic aids in clinical and screening settings. However, variability in input features and lack of standardized model development protocols highlight the need for future research focusing on validation across diverse populations and harmonization of diagnostic criteria.</div></div>","PeriodicalId":16609,"journal":{"name":"Journal of oral biology and craniofacial research","volume":"15 6","pages":"Pages 1591-1600"},"PeriodicalIF":0.0000,"publicationDate":"2025-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Artificial intelligence and machine learning in diagnosing and managing temporomandibular disorders: A systematic review and meta-analysis\",\"authors\":\"Vaishnavi Rajaraman, Deepak Nallaswamy, Amrutha Shenoy\",\"doi\":\"10.1016/j.jobcr.2025.09.013\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><h3>Background</h3><div>Artificial intelligence (AI) and machine learning (ML) models have recently emerged as promising tools for enhancing diagnostic accuracy.</div></div><div><h3>Objective</h3><div>To evaluate the diagnostic accuracy of AI/ML models in detecting TMDs through a systematic review and meta-analysis of existing literature.</div></div><div><h3>Methods</h3><div>A comprehensive search of electronic databases was conducted to identify studies assessing the diagnostic performance of AI/ML models in TMD diagnosis (PROSPERO-CRD420251035080). Data extraction and quality assessment were conducted independently by two reviewers using the AXIS tool for cross-sectional and Newcastle–Ottawa Scale for cohort studies. Meta-analysis of diagnostic accuracy was performed using pooled sensitivity, specificity, diagnostic odds ratio, and area under the curve. Statistical heterogeneity was assessed with the I<sup>2</sup> statistic.</div></div><div><h3>Results</h3><div>The systematic search identified 368 articles, of which 12 studies met inclusion criteria after screening. Risk of bias assessment showed most observational studies had low to unclear bias, while cross-sectional studies varied from moderate to high quality. Five studies were eligible for meta-analysis and they revealed that AI and machine learning models achieved a pooled sensitivity of 87.1 %(95 %CI:84.9 %–89.2 %) and specificity of 87.0 %(95 %CI:84.8 %–89.2 %) for TMD diagnosis. The diagnostic odds ratio was 45.1(95 %CI:30.5–66.8), with an area under the ROC curve of 0.96, indicating excellent diagnostic accuracy. Moderate heterogeneity I<sup>2</sup> = 38.7 %.</div></div><div><h3>Conclusion</h3><div>AI/ML models demonstrate excellent accuracy in differentiating patients with and without TMDs, reinforcing their potential as reliable diagnostic aids in clinical and screening settings. However, variability in input features and lack of standardized model development protocols highlight the need for future research focusing on validation across diverse populations and harmonization of diagnostic criteria.</div></div>\",\"PeriodicalId\":16609,\"journal\":{\"name\":\"Journal of oral biology and craniofacial research\",\"volume\":\"15 6\",\"pages\":\"Pages 1591-1600\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2025-09-24\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of oral biology and craniofacial research\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S2212426825002271\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"Medicine\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of oral biology and craniofacial research","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2212426825002271","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"Medicine","Score":null,"Total":0}
Artificial intelligence and machine learning in diagnosing and managing temporomandibular disorders: A systematic review and meta-analysis
Background
Artificial intelligence (AI) and machine learning (ML) models have recently emerged as promising tools for enhancing diagnostic accuracy.
Objective
To evaluate the diagnostic accuracy of AI/ML models in detecting TMDs through a systematic review and meta-analysis of existing literature.
Methods
A comprehensive search of electronic databases was conducted to identify studies assessing the diagnostic performance of AI/ML models in TMD diagnosis (PROSPERO-CRD420251035080). Data extraction and quality assessment were conducted independently by two reviewers using the AXIS tool for cross-sectional and Newcastle–Ottawa Scale for cohort studies. Meta-analysis of diagnostic accuracy was performed using pooled sensitivity, specificity, diagnostic odds ratio, and area under the curve. Statistical heterogeneity was assessed with the I2 statistic.
Results
The systematic search identified 368 articles, of which 12 studies met inclusion criteria after screening. Risk of bias assessment showed most observational studies had low to unclear bias, while cross-sectional studies varied from moderate to high quality. Five studies were eligible for meta-analysis and they revealed that AI and machine learning models achieved a pooled sensitivity of 87.1 %(95 %CI:84.9 %–89.2 %) and specificity of 87.0 %(95 %CI:84.8 %–89.2 %) for TMD diagnosis. The diagnostic odds ratio was 45.1(95 %CI:30.5–66.8), with an area under the ROC curve of 0.96, indicating excellent diagnostic accuracy. Moderate heterogeneity I2 = 38.7 %.
Conclusion
AI/ML models demonstrate excellent accuracy in differentiating patients with and without TMDs, reinforcing their potential as reliable diagnostic aids in clinical and screening settings. However, variability in input features and lack of standardized model development protocols highlight the need for future research focusing on validation across diverse populations and harmonization of diagnostic criteria.
期刊介绍:
Journal of Oral Biology and Craniofacial Research (JOBCR)is the official journal of the Craniofacial Research Foundation (CRF). The journal aims to provide a common platform for both clinical and translational research and to promote interdisciplinary sciences in craniofacial region. JOBCR publishes content that includes diseases, injuries and defects in the head, neck, face, jaws and the hard and soft tissues of the mouth and jaws and face region; diagnosis and medical management of diseases specific to the orofacial tissues and of oral manifestations of systemic diseases; studies on identifying populations at risk of oral disease or in need of specific care, and comparing regional, environmental, social, and access similarities and differences in dental care between populations; diseases of the mouth and related structures like salivary glands, temporomandibular joints, facial muscles and perioral skin; biomedical engineering, tissue engineering and stem cells. The journal publishes reviews, commentaries, peer-reviewed original research articles, short communication, and case reports.