现实世界整合的自动化工具,用于内出血检测在一个未经选择的队列的急诊室患者-一个外部验证研究研究
Ronald Antulov1,2, Martin Weber Kusk1,2, Gustav Højrup Knudsen1
1Department of Radiology and Nuclear Medicine, Esbjerg and Grindsted Hospitals, University Hospital of Southern Denmark, 6700 Esbjerg, Denmark.
Diagnostics (Basel, Switzerland)
|January 28, 2026
概括
深度学习工具 RAPID 内出血 (ICH) 显示出比预期更低的灵敏度和特异性,与许多假阳性结果相比,与第一年放射科住院医生相比,该工具的灵敏度和特异性较低.
科学领域:
- 神经辐射学神经辐射学
- 人工智能在医学中的应用
- 医学成像分析 医学成像分析
背景情况:
- 内出血 (ICH) 是一种严重的疾病,需要通过非对比头部计算机断层扫描 (NCCT) 快速诊断.
- 深度学习 (DL) 工具,如RAPID ICH,旨在在NCCT扫描中自动检测ICH.
- 对DL工具与人类解释的现实世界性能评估对于临床采用至关重要.
研究的目的:
- 为了评估 RAPID ICH 深度学习工具的现实世界性能.
- 为了比较RAPID ICH的诊断准确性与第一年放射学住院医生的诊断准确性.
- 评估来自紧急部门转诊的连续获得的NCCT的性能.
主要方法:
- 844个NCCT的回顾性队列研究,在一个CT扫描仪上获得了三个月的NCCT.
- 排除具有重大文物或不完整性的扫描.
- 资深神经放射学家的基本真相标签;第一年放射学专科医生的盲目评估和RAPID ICH.
主要成果:
- 快速ICH实现了87.3%的灵敏度,74%的特异性,21.3%的PPV和98.6%的NPV.
- 第一年居民获得了95.2%的灵敏度,90.8%的特异性,45.5%的PPV和99.6%的NPV.
- 快速ICH有8个虚假阴性和203个虚假阳性;居民的错误较少.
结论:
- 评估版本的RAPID ICH表现出比以前报告的敏感性和特异性较低,错误阳性率高.
- 在这种现实世界的临床环境中,该工具的通用性可能受到限制.
- 对不同经验水平的放射科医生进行比较测试为DL工具提供了重要的性能见解.
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