医生的命令-为什么放射科医生应该考虑调整商业机器学习应用程序的胸部X射线图,以适应他们的特定需求
Frank Philipp Schweikhard1, Anika Kosanke1, Sandra Lange2
1Institute for Diagnostic Radiology and Neuroradiology, University Medicine of Greifswald, 17475 Greifswald, Germany.
Healthcare (Basel, Switzerland)
|April 13, 2024
概括
商业深度学习软件对胸部X射线图分析具有前景,总体达到85%的灵敏度和75.4%的特异性. 性能因疾病和患者人口统计学而异,目前需要人类监督.
科学领域:
- 医疗成像医学成像
- 人工智能在医学中的应用
- 放射学 放射学是一门学科.
背景情况:
- 深度学习 (DL) 软件越来越多地被用于医学图像分析.
- 胸部X射线图是一种常见的诊断工具,需要有效和准确的解释.
研究的目的:
- 为了评估商业深度学习软件的性能,用于胸部X射线图的解释.
- 探索软件在不同疾病和患者群体的准确性,敏感性和特异性.
主要方法:
- 对477名患者的胸部放射图进行了回顾性研究.
- 将DL软件读数与两个放射科医生建立的黄金标准进行比较.
- 接收器运行特征 (ROC) 分析以确定性能指标.
主要成果:
- 总体AUC为0.84,敏感度为85%,特异性为75.4%.
- 胸腔溢液的最高性能 (AUC 0.92,86.4%的灵敏度/特异性).
- 性,年龄和并发症对DL表现的显著影响;老年人和女性患者的潜在偏见.
结论:
- 商业DL软件展示了自主胸部X射线报告的潜力,但目前需要人类监督.
- 在选场景中,DL工具可能有助于排除特定条件,从而减少工作负载.
- 放射科医生应该意识到潜在的偏差,并调整DL值以实现最佳部署.
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