对于COVID-19诊断的胸部X射线的计算机辅助诊断在由放射科医生进行的外部验证研究中,具有和没有深度学习系统
Aki Miyazaki1, Kengo Ikejima2, Mizuho Nishio3
1Department of Radiology, Kobe University Graduate School of Medicine, 7-5-2 Kusunoki-Cho, Chuo-Ku, Kobe, 650-0017, Japan.
Scientific reports
|October 16, 2023
概括
一个新的深度学习 (DL) 模型在胸部X射线检测COVID-19的诊断性能优于放射科医生. 当DL模型被用作参考时,它也显著提高了放射科医生的诊断准确性.
科学领域:
- 医疗成像医学成像
- 人工智能的人工智能
- 放射学 放射学是一门学科.
背景情况:
- 准确和及时诊断COVID-19至关重要.
- 胸部X射线 (CXR) 是诊断肺炎的关键成像方式,包括COVID-19.
- 深度学习 (DL) 模型显示了医学图像分析的潜力.
研究的目的:
- 评估使用CXRs开发的DL模型用于COVID-19检测的诊断性能.
- 调查DL模型是否可以提高放射科医生的诊断性能.
主要方法:
- 使用来自公共和私人来源的大量数据集 (26,393个CXR) 开发和外部验证DL模型.
- 三个CXR类别:正常,非COVID-19肺炎和COVID-19肺炎.
- 八名放射科医生进行了两次阅读:单独CXRs与CXRs与DL模型结果.
主要成果:
- DL模型的准确度达到0.733.3.
- 没有DL参考的放射科医生准确度为0.696 ± 0.031.1.
- 仅DL模型就超过了大多数放射科医生;它显著提高了放射科医生的诊断性能 (AUC,p=0.0038).
结论:
- 开发的DL模型在CXRs上对COVID-19检测表现出强大的诊断性能.
- 将DL模型集成到放射科医生的工作流中可以提高COVID-19的诊断准确性.
- 作为放射性诊断中的辅助工具,DL具有前景.
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