使用视网膜光学连贯性断层扫描成像预测心血管疾病风险
Cynthia Maldonado-Garcia1, Rodrigo Bonazzola1, Enzo Ferrante2
1Centre for Computational Imaging and Simulation Technologies in Biomedicine, School of Computing, University of Leeds, Leeds, United Kingdom.
Frontiers in artificial intelligence
|December 4, 2025
概括
光学连贯断层扫描 (OCT) 可以预测未来的心血管事件,如心脏病发作和中风. 视网膜OCT成像与深度学习相结合,可以识别患心血管疾病 (CVD) 风险较高的人.
科学领域:
- 眼科医生 眼科 眼科
- 心脏病学 心脏病学
- 人工智能的人工智能
背景情况:
- 心血管疾病 (CVD) 是全球主要的死亡原因.
- 非侵入性成像对于早期发现和预防心血管疾病至关重要.
- 光学一致性断层扫描 (OCT) 检测微血管变化,有助于早期识别有风险的患者.
研究的目的:
- 调查视网膜OCT作为预测未来心血管疾病事件的额外成像技术的潜力.
- 利用深度学习从OCT图像中提取特征,以评估心血管疾病风险.
主要方法:
- 来自2846名英国生物库参与者的视网膜OCT数据分析 (612名未来的心血管疾病事件,2234名对照).
- 应用自主监督深度学习 (变量自动编码器) 方法,从3D OCT图像中提取潜在表示.
- 训练一个随机森林分类器使用潜伏特征和临床数据来预测心肌梗塞 (MI) 或中风.
主要成果:
- 预测模型实现了0.75的曲线下面面积 (AUC),具有0.70的灵敏度,0.70的特异性和0.70的准确性.
- 通过模型可解释性,OCT图像中的冠状腺层被确定为未来心血管疾病事件的重要预测因素.
结论:
- 视网膜OCT成像,增强了深度学习,显示承诺作为心血管事件的预测工具.
- 这种方法可以帮助识别患心血管疾病风险较高的个体,促进及时干预.
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