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与医疗保健专家相比,深度学习的表现在检测手腕骨折的X线图:一个系统的审查和元分析
V Hansen1, J Jensen2, M W Kusk3
1Department of Radiology and Nuclear Medicine, Hospital of South West Jutland, University Hospital of Southern Denmark, Esbjerg, Denmark.
European journal of radiology
|March 1, 2024
概括
深度学习 (DL) 算法显示的诊断准确性与医疗保健专家对手腕骨折的X光照相相似. 需要进一步的研究来验证这些发现,并探索人工智能辅助诊断.
科学领域:
- 放射学和医学成像学 医学成像学
- 医疗保健中的人工智能
- 诊断准确性研究 诊断准确性研究
背景情况:
- 手腕骨折 (WF) 是常见的伤害,需要准确的诊断.
- 传统的诊断依赖于医疗保健专家的放射图解.
- 深度学习 (DL) 算法,特别是卷积神经网络 (CNN),在医学图像分析中表现有前途.
研究的目的:
- 系统地审查和元分析DL算法的诊断准确性,用于使用平面放射图的手腕骨折.
- 将DL算法的性能与医疗保健专家的共识作为参考标准进行比较.
主要方法:
- 系统地搜索Embase,Medline,PubMed,Scopus和科学网络 (2012年1月至2023年3月) 的数据.
- 纳入了关于 Radial 和 ulnar 骨折手腕放射图的研究,使用基于 CNN 的 DL 算法和专家共识作为参考.
- 使用修改的QUADAS-2工具评估研究偏差,并使用双变随机效应模型对诊断测试准确性的元分析.
主要成果:
- 六项研究包括33026张X射线图,被纳入了分析.
- 总结CNN的敏感度为92% (95%CI:80%-97%) 和特异性为93% (95%CI:76%-98%).
- 观察到显著的异质性 (I-平方:81.90%),其中四项研究至少在一个领域具有高偏差风险.
结论:
- 在X光照上识别手腕骨折时,CNN的诊断准确度与医疗保健专家的诊断准确度相当.
- 需要研究采用可靠的参考标准和外部数据集验证.
- 未来的研究应该调查医疗保健专家在CNN的帮助下诊断的表现.
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