深度学习用于预测骨质疏松症使用普通X射线的诊断准确性:系统性审查和元分析
Tzu-Yun Yen1,2, Chan-Shien Ho1,2, Yueh-Peng Chen3,4
1Department of Physical Medicine and Rehabilitation, Chang Gung Memorial Hospital, Linkou No. 5, Fuxing Street, Guishan District, Taoyuan City 333, Taiwan.
Diagnostics (Basel, Switzerland)
|January 22, 2024
概括
深度学习模型显示了从X射线预测骨质疏松症的前景,实现了高诊断准确度. 需要进一步的研究来证实它们在机会性查中的广泛临床应用.
科学领域:
- 放射学 放射学是一门学科.
- 人工智能的人工智能
- 医疗成像医学成像
背景情况:
- 骨质疏松症的诊断通常依赖于骨矿物密度扫描.
- 平面X射线图像广泛可用,但在骨质疏松症查中未得到充分利用.
- 深度学习提供了一种新的方法来分析骨质疏松症检测的放射数据.
研究的目的:
- 评估深度学习模型的诊断准确性,以使用普通X射线图像来预测骨质疏松症.
- 综合现有关于人工智能驱动的骨质疏松症检测从放射图表的性能证据.
主要方法:
- 在主要数据库 (PubMed,科学网,SCOPUS,谷歌学者) 进行了系统的文献搜索,截至2023年2月.
- 包括六项使用深度学习用于从X射线诊断骨质疏松症的研究.
- 使用接收器操作特征曲线 (AUROC) 下的面积,灵敏度和特异性来量化诊断性能.
主要成果:
- 分析显示,AUROC总值为0.88,表明预测性能良好.
- 聚合的灵敏度和特异性分别为0.81和0.87.
- 在包括的研究中观察到适度的异质性.
结论:
- 深度学习模型有效地从普通放射图中提取骨密度信息.
- 这些人工智能技术显示出机会性骨质疏松症查的潜力.
- 建议对多种人群进行进一步的前性多中心研究,以验证这种方法.
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