深度学习在科布角度自动测量X射线:一个系统的审查和元分析
Yuanpeng Zhu1,2,3, Xiangjie Yin1,2,3, Zefu Chen1,2,3
1Department of Orthopedic Surgery, Peking Union Medical College Hospital, Peking Union Medical College and Chinese Academy of Medical Sciences, Beijing, 100730, China.
Spine deformity
|September 25, 2024
概括
深度学习算法 (DLA) 显示了高精度的自动科布角测量. 基于细分的DLA优于基于基准的方法,为脊柱形分析提供了更好的性能.
科学领域:
- 医学成像分析 医学成像分析
- 医疗保健中的人工智能
- 脊柱形评估 脊柱形评估
背景情况:
- 自动科布角测量对于诊断和监测脊柱形至关重要.
- 深度学习算法 (DLA) 提供了提高这种测量的准确性和效率的潜力.
- 现有关于Cobb角度估计DLA的研究显示了性能和报告的变化.
研究的目的:
- 为了提供用于自动化科布角度估计的DLA的全面概述.
- 在本应用程序中识别与DLA相关的当前限制和挑战.
- 总结潜在的解决方案和策略,以提高DLA的性能.
主要方法:
- 在六个数据库中进行系统的文献搜索,截至2023年9月.
- 符合条件的研究的元分析,重点关注报告的循环平均绝对误差 (CMAE).
- 使用QUADAS-2进行实施策略和偏差风险评估的子组分析.
主要成果:
- 包括50项研究在系统性审查中,17项研究在元分析中.
- 对于自动化科布角测量的整体CMAE为2.99,具有显著的异质性.
- 基于细分的方法比基于基准的方法 (CMAE 3.31) 取得了更高的准确性 (CMAE 2.40).
结论:
- 对于自动化的科布角测量,DLAs表现出相当大的准确性.
- 在这个任务中,基于细分的DLA似乎优于基于里程碑的方法.
- 未来的研究应该集中在改进DLA设计和遵守质量报告准则上.
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