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在thalassemia患者的左心室肌动脉功能障碍评估使用心声学放射性特征和机器学习算法
Haniyeh Taleie1, Ghasem Hajianfar2, Maziar Sabouri1,3
1Department of Medical Physics, Iran University of Medical Sciences, Tehran, Iran.
Journal of digital imaging
|September 22, 2023
概括
由于铁过载而面临心力衰竭风险的β-thalassemia主要患者可以使用心声学和机器学习来识别. 这种方法提供了一种可行的方法来预测心肌铁沉积的心脏并发症.
科学领域:
- 心脏病学 心脏病学
- 医疗成像医学成像
- 人工智能的人工智能
背景情况:
- 由于心肌铁沉积而引起的心力衰竭是贝塔thalassemia major中死亡的主要原因.
- 心脏磁共振成像T2*是检测心肌铁过载的标准,但有局限性.
- 早期发现心脏干扰对于治疗β-thalassemia major至关重要.
研究的目的:
- 使用心声谱衍生的放射性特征和机器学习来区分具有和没有心肌铁过载的β-thalassemia主要患者.
- 评估使用机器学习模型在患有正常左心室喷射率 (LVEF) 的患者中预测心脏铁过载的可行性.
主要方法:
- 从44名铁过载患者 (T2* ≤20毫秒) 和47名对照患者 (T2* >20毫秒) 的末 (ES) 和末腹 (ED) 心声图像中提取了放射性特征,所有患者的LVEF>55%.
- 使用了三种特征选择方法 (MRMR-XGB,ANOVA-MLP,RFE-KNN) 和六种分类器.
- 模型性能使用AUC,ACC,SEN和SPE进行评估.
主要成果:
- 在ED数据集上,MRMR-XGB模型获得了最高的性能,AUC=0.73,ACC=0.73,SPE=0.73和SEN=0.73.
- ANOVA-MLP和RFE-KNN也在ES和联合ED&ES数据集上展示了有希望的结果.
- 这些发现表明放射性分析在识别心肌铁过载时的潜力.
结论:
- 从心声回声学中提取的放射性特征,结合机器学习,提供了一种可行的方法,用于预测与β-thalassemia主要患者的铁过载有关的心脏并发症.
- 这种非侵入性方法可以补充或作为传统成像技术的替代品来监测心肌铁含量.
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