基于机器学习的诊断和冠状动脉疾病的风险分类使用心肌 perfusion 成像 SPECT:一个放射学研究
Mehdi Amini1, Mohamad Pursamimi2, Ghasem Hajianfar1
1Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, CH-1211, Geneva 4, Switzerland.
Scientific reports
|September 10, 2023
概括
机器学习对心肌输液成像 (MPI) 单光子发射计算机断层扫描 (SPECT) 的放射学分析显示,在诊断冠状动脉疾病 (CAD) 风险方面具有前景. 使用压力成像特征的模型在对CAD风险进行分类时获得了更高的准确性,这可能加快了诊断速度.
科学领域:
- 心脏病学 心脏病学
- 放射学 放射学是一门学科.
- 人工智能的人工智能
背景情况:
- 冠状动脉疾病 (CAD) 诊断依赖于解释心肌输液成像 (MPI) 单光子发射计算机断层扫描 (SPECT).
- 手动解释MPI SPECT可能是劳动密集型和耗时的.
- 机器学习 (ML) 和放射学为自动化和客观分析提供了潜力.
研究的目的:
- 用MPI SPECT评估基于ML的放射学分析对CAD状态和风险分层的诊断性能.
- 为了比较不同放射学特征集 (Rest,Stress,Delta,Combined) 和ML算法的有效性.
主要方法:
- 395名怀疑患有CAD的患者接受了压力休息MPI SPECT.
- 从划分的左心室心肌中提取了118个放射性特征,与临床数据相结合.
- 分类器分别使用80%和20%的数据分割进行培训和测试,用于正常/异常和低/高风险CAD分类.
主要成果:
- 使用压力放射学特征集的模型与其他特征集相比,表现出卓越的性能.
- 压力-博鲁塔-渐变增强模型在高风险CAD分类中获得了最高的性能 (AUC:0.79).
- 对CAD风险的关键特征包括糖尿病状况和纹理特征 (依赖数非均性正常化).
结论:
- 基于ML的MPI SPECT的放射学分析是CAD风险分类的有希望的工具.
- 开发的模型可以帮助减少手工工作量,加快CAD的诊断过程.
- 压力成像功能对于预测CAD风险分层特别有价值.
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