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Alexandra Cristina Rusu^1,2, Klara Brînzaniuc³, Grigore Tinica^4,5

¹Doctoral School of Medicine and Pharmacy, Faculty of Medicine, University of Medicine, Pharmacy, Science, and Technology of Targu Mures, 540142 Targu Mures, Romania.

Diagnostics (Basel, Switzerland)

|May 14, 2025

概括

结合视网膜微血管特征的机器学习模型显示,与传统风险因素相比,在识别冠状动脉心脏病 (CHD) 患者的准确性有所提高.

科学领域:

眼科医生眼科眼科
心脏病学心脏病学
生物医学工程生物医学工程

背景情况:

心血管疾病 (CVD) 在欧盟造成显著的死亡率.
视网膜微血管学显示为心血管风险的生物标志物具有前途.
现有的心血管疾病风险评分具有可变的有效性.

研究的目的:

确定与冠状动脉心脏病 (CHD) 相关的视网膜微血管特征.
在使用传统风险因素的CHD查算法中评估这些特征的实用性.

主要方法:

120名参与者的横截面研究 (36名心脏病患者,84名对照).
利用光学连贯断层扫描血管学 (OCTA) 来分析视网膜微血管结构.
与机器学习 (k-NN,Naive Bayes,SVM) 的逻辑回归进行比较,用于CHD分类.

主要成果:

机器学习模型的表现优于传统的物流回归.
支持矢量机 (SVM) 在CHD识别方面达到86%的最高准确率.
准确度在78.7% (逻辑回归) 到86% (SVM) 之间.

结论:

机器学习算法与视网膜微血管数据相结合,提高了心血管疾病的识别.
整合OCTA参数的新风险评分可以改善心血管疾病查.
视网膜成像为传统的心血管风险评估提供了有价值的补充.

关键词:

人工智能的人工智能是人工智能.光学连贯性断层扫描血管图谱视网膜微血管化疗法风险评分和风险评分.

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