一种基于机器学习的方法来识别外围动脉疾病,使用来自对比增强磁共振成像的纹理特征来识别外围动脉疾病
Bijen Khagi1, Tatiana Belousova2, Christina M Short3
1Penn State Heart and Vascular Institute, Pennsylvania State University College of Medicine, Hershey, PA, USA.
Magnetic resonance imaging
|December 8, 2023
概括
在对比度增强的MRI中对小腿肌肉质感的机器学习分析可以准确地检测周围动脉疾病 (PAD). 这种成像技术在诊断PAD和识别高风险患者亚组方面表现有前途.
科学领域:
- 医学成像医学成像
- 机器学习 机器学习
- 心血管诊断心血管诊断
背景情况:
- 周围动脉疾病 (PAD) 的诊断依赖于评估血液循环受损,特别是在小腿肌肉中,导致间歇性巴.
- 在PAD患者中,改变的微血管输液和结缔组织变化可能表现为骨肌肉中可检测到的纹理变化.
研究的目的:
- 开发一种自动化管道,从CE-MRI扫描中提取纹理特征.
- 利用这些功能来训练用于PAD检测和风险分层的机器学习模型.
主要方法:
- 创建了一个自动管道,用于从对比增强磁共振成像 (CE-MRI) 中提取纹理特征.
- 机器学习模型使用36名PAD患者和20名对照组的特征进行训练,数据分为7:3和交叉验证.
- 应用了特征选择方法来优化模型性能.
主要成果:
- 一个两类分类 (对照组与PAD) 实现了94.11%的峰值精度和84.85%的平均测试精度.
- 三类分类确定了PAD亚组 (对照组与没有糖尿病的PAD和患有糖尿病的PAD) 平均准确率为83.23%.
- 运动结果的分类 (对照组,PAD完成者,非完成者) 平均准确率为78.60%.
结论:
- 应用于CE-MRI纹理特征的机器学习为PAD诊断提供了一种有前途的非侵入性方法.
- 这种方法可以帮助识别高风险的PAD亚组,并评估疾病进展或治疗反应.
- 基于成像的纹理分析具有研究下肢缺血的潜力.
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