胸部X射线和CT使用人工智能用于骨质疏松症的诊断准确性:系统性审查和元分析
Norio Yamamoto1,2,3, Akihiro Shiroshita4,5, Ryota Kimura4,6
1Department of Epidemiology, Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama University, Okayama, Japan. norio-yamamoto@umin.ac.jp.
Journal of bone and mineral metabolism
|August 21, 2024
概括
人工智能 (AI) 在使用胸部X射线诊断骨质疏松症方面表现有前途,为机会性查提供高灵敏度. 需要进一步的研究,因为在研究中患者选择的偏差很高.
科学领域:
- 放射学 放射学是一门学科.
- 人工智能的人工智能
- 医学诊断 医学诊断 医学诊断
背景情况:
- 分析胸部图像的人工智能 (AI) 系统显示了可靠骨质疏松症诊断的潜力.
- 骨质疏松症诊断传统上依赖于诸如骨矿物密度扫描等方法.
研究的目的:
- 系统地审查和元分析人工智能辅助胸部X射线和CT扫描骨质疏松症的诊断准确性.
- 评估AI的敏感性,特异性和AUC,用于诊断骨质疏松症和骨质疏松症.
主要方法:
- 对人工智能用于骨质疏松症诊断的研究进行系统审查和元分析.
- 在 MEDLINE,EMBASE,Cochrane 和 IEEE Xplore 搜索到 2023 年 11 月 8 日.
- 两变随机效应模型用于估计聚合的灵敏度,特异性和AUC.
主要成果:
- 包括9项研究 (11,369名参与者).
- 在骨质疏松症诊断中,人工智能辅助的胸部X射线的聚合灵敏度和特异性分别为0.83和0.76,AUC为0.86 (低确定性证据).
- 在骨质疏松症/骨质疏松症诊断中,人工智能辅助胸部CT的综合灵敏度和特异性为0.83和0.70 (低至非常低的确定性证据).
结论:
- 人工智能辅助的胸部X射线显示了对骨质疏松症诊断的高度敏感性,这表明机会性查的潜力.
- 在大多数纳入研究中,注意到患者选择偏差的高风险.
- 未来的研究需要对人工智能选工具的 robust 参与者选择标准.
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