基于机器学习的心脏沙丘病的诊断,使用多室壁运动分析
Jan Eckstein1, Negin Moghadasi2, Hermann Körperich1
1Institute for Radiology, Nuclear Medicine and Molecular Imaging, Heart and Diabetes Center North Rhine Westphalia, Bad Oeynhausen, University of Bochum, 32545 Bochum, Germany.
Diagnostics (Basel, Switzerland)
|July 29, 2023
概括
机器学习使用心脏磁共振成像 (CMR) 数据准确诊断心脏肉类发病症 (CS). 这种非对比的方法改善了健康个体和CS患者之间的差异化,可能会影响疾病管理.
科学领域:
- 心脏病学 心脏病学
- 医疗成像医学成像
- 机器学习 机器学习
背景情况:
- 心脏沙丘病 (CS) 呈现出非特异性的临床和表型特征,使得诊断变得困难.
- 精确诊断CS对于有效的患者管理和治疗策略至关重要.
研究的目的:
- 开发和评估基于机器学习的CS诊断方法,使用心脏磁共振成像 (CMR).
- 利用多腔体积测量和应变特征跟踪来改进CS检测.
- 评估各种机器学习分类器在区分CS患者与对照者的有效性.
主要方法:
- 心脏磁共振成像 (CMR) 测试对45名CMR阴性沙尔科发病患者,18名CMR阳性沙尔科发病患者和44名对照进行.
- 使用后勤回归,KNN,DT,RF,SVM,GBoost,XGBoost和投票分类器分析了多室体积和应变参数.
- 采用特征选择技术来提高分类准确性,特别是为了区分CMR阳性和CMR阴性患者.
主要成果:
- 随机森林 (RF) 和投票分类器在三集群分析中 (对照对抗CMR阳性对抗CMR阴性) 实现了最高的预测率 (81.82%).
- 后勤回归,射频和SVM分类器在两组分类分析中显示出高预测率 (96.97%),将对照组与所有肉病患者区分开来.
- 特征选择显著提高了逻辑回归在CMR阳性和CMR阴性患者 (89.47%) 之间的区分的准确性.
结论:
- 使用多室心脏功能和菌株数据的监督机器学习提供了一种非对比方法,用于准确地区分健康人群和肉病患者.
- 特征选择有效地解决了在CMR阳性和CMR阴性患者之间进行歧视的挑战,从而实现了高预测准确度.
- 这项研究表明,心脏参与的患病率可能比以前认可的高,需要对临床疾病管理策略进行重新评估.
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