从商业人工智能为基础的计算机辅助诊断进行乳房扫描的异常得分的积极预测值
Si Eun Lee1, Hanpyo Hong1, Eun-Kyung Kim2
1Department of Radiology, Yongin Severance Hospital, Yonsei University College of Medicine, Yongin, Republic of Korea.
Korean journal of radiology
|March 26, 2024
概括
基于人工智能的计算机辅助诊断 (AI-CAD) 得分的积极预测值 (PPV) 随着得分的提高而增加. AI-CAD 乳腺学 PPV 符合查标准,并且在诊断病例中更高.
科学领域:
- 放射学 放射学是一门学科.
- 医疗成像医学成像
- 人工智能的人工智能
背景情况:
- 基于人工智能的计算机辅助诊断 (AI-CAD) 越来越多地被用于乳房影像.
- 解释AI-CAD对恶性瘤风险的连续得分需要进一步了解.
- 本研究的重点是AI-CAD异常分数的积极预测值 (PPV).
研究的目的:
- 从基于深度学习的商业AI-CAD系统中调查异常得分的PPV.
- 分析AI-CAD得分与临床和放射学发现相关.
- 为了确定AI-CAD分数在乳房影像解释中的临床实用性.
主要方法:
- 对来自599名女性的656个乳房进行了回顾性分析,AI-CAD结果呈阳性 (Lunit Insight MMG,得分≥10).
- 根据AI-CAD异常分数 (10-49,50-69,70-89,90-100) 将乳房细分为四组.
- 单变量和多变量分析以将AI-CAD得分与临床和放射学因素联系起来.
主要成果:
- 整体AI-CADPPV为32.5% (213/656),较高的得分与恶性瘤增加的可能性相关.
- 对于查性乳房扫描,PPVs从5.1% (得分组1) 到96.3% (得分组4) 不等.
- 在具有诊断指示,可触摸的发现,脂肪乳房和特定成像特征的妇女中观察到更高的PPV.
结论:
- AI-CAD异常得分显示出与恶性瘤风险的明显正相关性.
- AI-CAD PPV与查乳房镜检查的可接受范围保持一致,并在诊断环境中得到增强.
- 这些发现支持AI-CAD分数在乳房镜中有效应用,以提高诊断准确度.
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