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从2型糖尿病患者的常规视网膜图像中对心血管结果的深度学习预测
Mohammad Ghouse Syed1, Emanuele Trucco1, Muthu R K Mookiah1
1VAMPIRE project, Computing, School of Science and Engineering, University of Dundee, Dundee, USA.
Cardiovascular diabetology
|January 3, 2025
概括
深度学习AI准确地预测糖尿病患者视网膜照片中的心血管疾病 (CVD) 风险. 这种人工智能工具在例行眼科检查期间对集成心血管疾病风险评估充满希望.
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:
- 视网膜血管特征与心血管疾病 (CVD) 有关,但之前的研究只检查了有限的参数.
- 深度学习 (AI) 模型提供了一种新的方法来分析复杂的视网膜数据以预测心血管疾病风险.
研究的目的:
- 评估AI模型是否可以使用糖尿病视网膜查图像预测心血管疾病的结果.
- 将AI模型的预测性能与传统的临床CVD风险得分进行比较.
主要方法:
- 使用了6127名2型糖尿病患者的队列,分为培训,验证和测试组.
- 一个EfficientNet-B2人工智能模型分析了视网膜图像,以预测10年的心血管疾病风险.
- 性能与聚合队列方程 (PCE) 和冠心病多原风险评分 (PRS) 进行了比较.
主要成果:
- 人工智能模型与PCE风险得分 (r=0.66) 呈现强烈的相关性,并预测了10年主要的不良心血管事件 (MACE).
- 视网膜AI风险预测与PCE (AUC 0.697) 相似,并且与PCE和PRS (AUC 0.728) 相结合时得到改善.
- 3年内视网膜预测风险增加与随后的MACE可能性更高有关.
结论:
- 深度学习的人工智能从糖尿病患者队列中的视网膜照片中准确地预测MACE.
- 人工智能视网膜评估提供与传统风险评分相匹配的性能,并可能使综合心血管疾病风险评估成为可能.
- 将AI视网膜风险与PRS相结合,可以提高心血管疾病风险预测的准确性.
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