机器学习可以根据CT血管学上的6型配置分类来检测患有冠状动脉斑的症状患者
Francesco Pisu1, Hui Chen2, Bin Jiang3
1Department of Radiology, Azienda Ospedaliero Universitaria, Monserrato, Cagliari, Italy.
European radiology
|November 20, 2023
概括
机器学习模型使用胆道斑分级和临床数据可以识别患有脑血管事件的患者. 这种方法有助于诊断症状患者,而化斑块是关键指标.
科学领域:
- 心血管成像 - 心血管成像
- 机器学习在医学中的应用
- 脑血管疾病研究研究
背景情况:
- Carotid 斑块的组成与脑血管事件有关,但配置的作用尚不清楚.
- 准确识别患有冠状动脉斑块的症状患者对于预防不良事件至关重要.
研究的目的:
- 开发和验证一种机器学习 (ML) 模型,使用基于CT血管造影 (CTA) 的心动脉板分级和临床数据.
- 为了识别双侧动脉斑块的患者,这些患者具有脑血管事件的症状.
主要方法:
- 一项多中心的追溯诊断研究包括790名接受培训/内部验证的患者和159名接受外部测试的患者.
- 使用四种ML模型和逻辑回归来根据临床因素和6型斑块分级来识别症状患者.
- 内部和外部验证评估模型的歧视和校准.
主要成果:
- 结合斑块分级 (ML-All-G) 的ML模型实现了ROC曲线下的面积为0.71,灵敏度为80%.
- 模型性能在外部验证中是一致的.
- 化斑块,特别是阳性边缘标志,以及晚年和高脂血症,显著影响了症状患者的识别.
结论:
- 以CTA为基础的ML模型利用心动脉斑配置和临床数据可以以合理的诊断准确度识别症状患者.
- 6种类型的分级结合临床变量有效识别症状患者.
- 这种ML方法,利用快速的CTA获取,可以简化脑血管症状的诊断.
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