Artificial intelligence and machine learning in diagnosing and managing temporomandibular disorders: A systematic review and meta-analysis

Q1 Medicine
Vaishnavi Rajaraman, Deepak Nallaswamy, Amrutha Shenoy
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引用次数: 0

Abstract

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.
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
人工智能(AI)和机器学习(ML)模型最近成为提高诊断准确性的有前途的工具。目的通过对现有文献的系统回顾和荟萃分析,评价AI/ML模型对tmd的诊断准确性。方法综合检索电子数据库,筛选评估AI/ML模型在TMD诊断中的诊断性能的研究(PROSPERO-CRD420251035080)。数据提取和质量评估由两名审稿人独立进行,使用AXIS工具进行横断面研究,使用纽卡斯尔-渥太华量表进行队列研究。采用合并敏感性、特异性、诊断优势比和曲线下面积对诊断准确性进行meta分析。采用I2统计量评估统计异质性。结果系统检索到368篇文献,经筛选符合纳入标准的文献有12篇。偏倚风险评估显示,大多数观察性研究具有低至不明确的偏倚,而横断面研究的质量从中等到高不等。五项研究符合荟萃分析的条件,它们显示人工智能和机器学习模型对TMD诊断的总灵敏度为87.1% (95% CI: 84.9% - 89.2%),特异性为87.0% (95% CI: 84.8% - 89.2%)。诊断优势比为45.1(95% CI: 30.5-66.8), ROC曲线下面积为0.96,诊断准确性极佳。中度异质性I2 = 38.7%。结论ai /ML模型在鉴别tmd患者和非tmd患者方面具有出色的准确性,增强了其作为临床和筛查环境中可靠诊断辅助工具的潜力。然而,输入特征的可变性和缺乏标准化的模型开发协议突出了未来研究的需要,重点是跨不同人群的验证和诊断标准的协调。
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来源期刊
CiteScore
4.90
自引率
0.00%
发文量
133
审稿时长
167 days
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.
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