基于乳房镜的深度学习模型用于冠状动脉化
Sangil Ahn1, Yoosoo Chang2,3,4, Ria Kwon2,5
1Department of Electrical and Computer Engineering, Sungkyunkwan University, 2066, Seobu-Ro, Jangan-Gu, Suwon 16149, Republic of Korea.
European heart journal. Cardiovascular Imaging
|November 21, 2023
概括
乳房镜可以使用深度学习模型预测心血管疾病风险. 这些模型对双重风险评估有希望,可与传统方法相比较.
科学领域:
- 放射学 放射学是一门学科.
- 心脏病学 心脏病学
- 人工智能的人工智能
背景情况:
- 乳房摄影主要用于乳腺癌查.
- 乳房影像有可能预测心血管疾病 (CVD) 风险.
- 冠状动脉 (CAC) 评分是冠状动脉事件的既定预测指标.
研究的目的:
- 开发和评估深度学习模型,利用乳房图像来预测CAC分数.
- 评估这些模型在识别有心血管事件风险的个体方面的表现.
主要方法:
- 一个卷积神经网络 (CNN) 是使用一组训练数据集开发的 5235 韩国妇女接受了乳房扫描和 CAC 计算机断层扫描.
- 使用了Resnet18模型,增强了对比学习策略.
- 用灵敏度,特异性,AUROC和精度来评估模型性能.
- 纳入年龄和更年期状态以评估它们对模型性能的影响.
主要成果:
- 基于CNN的深度学习模型实现了0.761.76的AUROC.
- 考虑到年龄和更年期状态,该模型的AUROC提高到0.776.
- 深度学习模型的表现与弗雷明汉风险评分 (AUROC 0.736) 相当.
结论:
- 基于乳房影像的深度学习模型显示了预测CAC分数的巨大潜力.
- 这些模型为乳腺癌和心血管疾病的双重风险评估提供了有希望的方法.
- 建议在不同人群中进行进一步验证,并与国家查计划进行整合.
相关概念视频
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