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基于人工智能的预测神经心血管风险评分从视网膜扫描-源光学连贯性断层扫描-血管学

C Germanese^1,2, A Anwer², P Eid¹

¹Department of Ophthalmology, Dijon University Hospital, Dijon, France.

Scientific reports

|November 7, 2024

概括

人工智能 (AI) 模型可以使用视网膜图像预测神经心血管风险. 机器学习和深度学习通过扫描源光学连贯性断层扫描-血管学 (SS OCT-A) 扫描精确评估心血管健康.

科学领域:

眼科和人工智能的人工智能
评估心血管风险评估
医学成像分析医学成像分析

背景情况:

视网膜血管变化可能表明系统性心血管 (CV) 疾病.
人工智能为增强医疗评估提供了新的方法,包括简历评分.
扫描源视网膜光学连贯断层扫描-血管学 (SS OCT-A) 提供了详细的微血管成像.

研究的目的:

评估机器学习 (ML) 和深度学习 (DL) 模型在预测神经心血管风险得分方面的有效性.
评估AI算法的准确性,使用SS OCT-A视网膜图像进行心血管风险分层.
确定人工智能驱动的视网膜血管结构分析对心血管评估的潜力.

主要方法:

在RASTA数据集 (491名患者) 的SS OCT-A视网膜图像上训练ML和DL模型.
使用了EfficientNetV2-B3,一个适用于视网膜OCT-A分析的DL模型.
使用诸如曲线下的面积 (AUC) 和平均绝对误差 (MAE) 等指标评估模型性能.

主要成果:

ML模型表现出强的性能,AUC值在0.71到0.96.6之间.
EfficientNetV2-B3模型在将患者分为两个神经心血管风险组时,达到68%的准确性.
DL模型报告了CHA2DS2-VASc得分预测的平均绝对误差大约为0.697.

结论:

人工智能模型,特别是DL,可以从SS OCT-A成像中可靠地预测CHA2DS2-VASc得分.
这种方法为评估神经心血管风险提供了一个有希望的,非侵入性的工具.
整合人工智能与视网膜成像可能会增强未来的心血管健康评估.

关键词:

在CHA2DS2-VASc中使用.深度学习是一种深度学习.机器学习是机器学习.神经心血管神经系统其他国家,地区和地区-A.预测预测预测

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