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使用人工智能检测模型测量乳腺动脉化及其与主要不良心血管事件的关联

Suzanne J Rose¹, Josette Hartnett², Zachary J Estep³

¹Department of Research and Discovery, Stamford Hospital, Stamford, Connecticut, United States of America.

PLOS digital health

|December 23, 2024

概括

人工智能可以从乳房影像中测量乳房动脉化 (BAC),以帮助预测主要不良心脏事件 (MACE). BAC显示在识别心血管疾病风险较高的女性方面有潜力.

科学领域:

心脏病学心脏病学
放射学放射学是一门学科.
人工智能的人工智能

背景情况:

乳腺动脉化 (BAC) 来自乳房影像正在评估心血管风险分层.
冠状动脉化 (CAC) 是一个已知的心血管风险指标.

研究的目的:

确定BAC,CAC严重程度和主要心脏不良事件 (MACE) 之间的关系.
评估AI测量的BAC在心血管风险评估中的有用性.

主要方法:

对女性进行乳房影像和胸部CT扫描的回顾性研究.
用于测量BAC的AI技术.
统计分析包括T测试,Chi平方测试,费舍尔精确测试和后勤回归.

主要成果:

BAC存在与中度至高动脉样硬化心血管疾病 (ASCVD) 风险和MACE的概率增加有关.
与CAC阳性患者相比,BAC阳性患者的ASCVD风险中等至高的几率更高.
人工智能生成的BAC测量显示与MACE和ASCVD风险有显著的相关性.

结论:

通过人工智能测量乳房图的BAC与MACE有关.
BAC可以证实女性的ASCVD风险评估.
人工智能生成的BAC可能有助于识别和风险分层妇女预防心血管疾病.

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