牙科人工智能:系统审查和元分析的概述
Ankita Saikia1, Therese Kvist2, Amr Fawzy2
1UWA Dental School, The University of Western Australia, 17 Monash Ave, Nedlands, Perth, WA, Australia. ankita.saikia@research.uwa.edu.au.
Evidence-based dentistry
|December 9, 2025
概括
人工智能 (AI) 在牙科中表现出高的诊断准确性,其聚合灵敏度和特异性分别为0.85和0.93. 先进的AI模型,特别是卷积神经网络 (CNN),显示出改善牙科诊断和患者结果的重大前景.
科学领域:
- 牙科信息学 牙科信息学
- 医疗保健中的人工智能
- 系统审查方法论 系统审查方法论
背景情况:
- 人工智能 (AI) 在牙科的整合正在迅速发展,需要对其诊断能力和证据质量进行全面评估.
- 系统性审查对于合成各种牙科专业的AI应用研究至关重要.
研究的目的:
- 系统地审查和评估现有的牙科人工智能系统审查的质量和结果.
- 在不同的牙科专业中对人工智能进步进行分类,并确定研究缺口.
主要方法:
- 对七个数据库进行了系统搜索,以确定相关的系统审查.
- 系统性审查中的偏见风险 (ROBIS) 工具被用于评估所包含的审查的质量.
- 计算了人工智能算法的聚合性能估计,并使用了证据差距地图来识别研究差距.
主要成果:
- 对116项系统性审查的分析显示,人工智能诊断准确度在牙科专业中从82%到95%不等.
- 对低偏见审查的元分析显示,AI牙科诊断的综合灵敏度为0.85,特异性为0.93.
- 先进的AI模型,特别是卷积神经网络 (CNN),实现了93.1%的聚合精度,在性能指标中观察到显著的异质性.
结论:
- 人工智能在虫检测和口腔病变诊断等专业领域表现出强大的诊断准确性,具有高的聚合灵敏度,特异性和AUC值.
- 先进的AI模型和更大的数据集提高了诊断性能,特别是在图像分类任务中.
- 解决研究缺口和标准化方法对于最大限度地提高AI在牙科中的临床影响和改善患者的治疗结果至关重要.
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