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原始和人工改进的基于人工智能的计算机辅助诊断对乳房的影响 美国解释
Wendie A Berg1,2, David Gur1, Andriy I Bandos3
1University of Pittsburgh School of Medicine, Department of Radiology, Pittsburgh, PA,USA.
Journal of breast imaging
|March 1, 2024
概括
乳房超声波中的计算机辅助诊断 (CADx) 改善了放射科医生的表现,特别是当配置为高特异性时. 增强的CADx模式导致更好的癌症检测和更少的假阳性.
科学领域:
- 放射学 放射学是一门学科.
- 医疗成像医学成像
- 医疗保健中的人工智能
背景情况:
- 计算机辅助诊断 (CADx) 系统越来越多地用于协助放射科医生解释医学图像.
- 在乳房超声波 (美国) 解释中CADx的有效性可能因其配置而有所不同,特别是在敏感度和特异性方面.
研究的目的:
- 评估计算机辅助诊断 (CADx) 对乳房超声波解释的影响.
- 评估不同的CADx模式 (原始,高灵敏度,高特异性) 如何影响放射科医生的表现.
主要方法:
- 9名乳腺成像放射科医生使用直角配对的美国图像审查了319个病变 (88种癌症).
- 放射科医生在使用CADx之前和之后提供了BI-RADS评估,采用三个模式:原始,高灵敏度和高特异性.
- 计算了接收器运行特征曲线 (AUC) 下的面积,以测量性能,并考虑了多读器分析的变化.
主要成果:
- 独立CADx实现了0.77.7的AUC.
- 原始的CADx模式没有显著改变放射科医生的表现 (平均AUC为0.82).
- 高灵敏度的CADx将平均AUC提高到0.88 (P < 0.001),而高特异性的CADx则将其提高到0.89 (P < 0.0001).
- 放射科医生在高特异性模式下对恶性CADx线索的反应更快.
结论:
- 原始的CADx模式对乳腺美国的解释影响很小.
- 具有提高灵敏度或特异性的CADx系统可以提高放射科医生的性能.
- 优化CADx以减少假阳性恶性线索,可以提高放射科医生的响应能力和诊断准确度.
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