人工智能和机器学习在诊断和管理关部疾病:一个系统的审查和元分析
Vaishnavi Rajaraman1, Deepak Nallaswamy1, Amrutha Shenoy1
1Department of Prosthodontics, Saveetha Dental College and Hospitals, Saveetha Institute of Medical and Technical Sciences, Saveetha University, Chennai, Tamil Nadu, India.
Journal of oral biology and craniofacial research
|October 6, 2025
概括
人工智能 (AI) 和机器学习 (ML) 模型在诊断关节疾病 (TMD) 中显示出高准确性. 这些AI/ML工具为诊断TMD提供了可靠的临床和查应用的有希望的潜力.
科学领域:
- 医疗信息学 医疗信息学
- 人工智能在医学中的应用
- 诊断准确性研究 诊断准确性研究
背景情况:
- 人工智能 (AI) 和机器学习 (ML) 正在成为提高诊断准确性的强大工具.
- 部疾病 (TMDs) 是需要增强诊断方法的重要领域.
研究的目的:
- 系统地审查和元分析AI/ML模型用于检测TMDs的诊断准确性.
- 根据现有文献,评估AI/ML在TMD诊断中的整体性能.
主要方法:
- 在电子数据库中进行了全面的文献搜索.
- 数据提取和质量评估是独立使用标准化工具 (AXIS,纽卡斯尔-太华尺度) 进行的.
- 超分析计算了聚合的灵敏度,特异性,诊断几率比率和曲线下的面积,使用I2统计数据评估异质性.
主要成果:
- 十二项研究符合纳入标准,其中五项符合元分析的条件.
- AI/ML模型显示TMD诊断的综合灵敏度为87.1%,特异性为87.0%.
- 诊断几率比为45.1,ROC曲线下的面积为0.96,表明具有中等异质性的出色诊断性能 (I2 = 38.7%).
结论:
- AI/ML模型在区分患有TMD和没有TMD的患者方面表现出卓越的准确性.
- 这些模型显示出在临床和查环境中作为可靠的诊断辅助工具的潜力.
- 需要进一步的研究来解决输入特征的变异性,标准化模型开发,并在多样化的群体中验证.
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