人工智能用于使用口腔内图像和牙科放射图的二进制牙损伤诊断:系统性审查和元分析
Jing Lai1, Shanshan Guo1, Ke Wang1
1Chongqing Dental Hospital, No. 345 Minsheng Road, Yuzhong District, Chongqing, 400010, China.
BMC oral health
|February 3, 2026
概括
人工智能 (AI) 使用各种成像方法在检测牙损伤方面表现出很好的准确性. 虽然有希望,但需要进一步的研究,因为在广泛临床使用之前需要研究异质性和质量限制.
科学领域:
- 牙科 牙科是指牙科的专业.
- 医疗成像医学成像
- 人工智能的人工智能
背景情况:
- 人工智能 (AI) 在牙科诊断方面表现有前途.
- 人工智能在不同成像方式中对牙的二进制分类的准确性需要系统的评估.
研究的目的:
- 系统地评估人工智能模型的诊断性能,以检测牙虫.
- 用临床口内图像和牙科放射图片来评估AI的准确性.
主要方法:
- 从2015年1月到2025年6月,按照PRISMA-DTA指南在主要数据库 (PubMed,Embase,Scopus,Web of Science,IEEE Xplore) 中进行系统的文献搜索.
- 包含应用人工智能用于病诊断的研究,具有可提取的灵敏度和特异性数据.
- 使用双变的随机效应模型进行元分析,根据图像类型和分析单元进行子组分析,并评估报告质量和偏差风险.
主要成果:
- 在分析中包括了13项研究,其聚合灵敏度,特异性和AUC分别为0.86,0.91和0.94.
- 口腔内图像显示出更高的灵敏度 (0.88) 和AUC (0.95),而X射线图则具有更高的特异性 (0.92).
- 观察到高异质性 (I2>90%),受图像类型,模型架构和数据集特征的影响.
结论:
- 人工智能模型在各种成像模式和分析单元中表现出良好的牙损伤检测诊断准确度.
- 实质性的异质性和研究质量的局限性要求对结果进行谨慎的解释.
- 人工智能显示出作为辅助决策支持工具的潜力,但标准化,外部验证和高质量的多中心研究对于临床实施至关重要.
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