PDF(Pubmed)
Abstract：
UNASSIGNED: Early detection and localization of myocardial infarction (MI) can reduce the severity of cardiac damage through timely treatment interventions. In recent years, deep learning techniques have shown promise for detecting MI in echocardiographic images. Existing attempts typically formulate this task as classification and rely on a single segmentation model to estimate myocardial segment displacements. However, there has been no examination of how segmentation accuracy affects MI classification performance or the potential benefits of using ensemble learning approaches. Our study investigates this relationship and introduces a robust method that combines features from multiple segmentation models to improve MI classification performance by leveraging ensemble learning.
UNASSIGNED: Our method combines myocardial segment displacement features from multiple segmentation models, which are then input into a typical classifier to estimate the risk of MI. We validated the proposed approach on two datasets: the public HMC-QU dataset (109 echocardiograms) for training and validation, and an E-Hospital dataset (60 echocardiograms) from a local clinical site in Vietnam for independent testing. Model performance was evaluated based on accuracy, sensitivity, and specificity.
UNASSIGNED: The proposed approach demonstrated excellent performance in detecting MI. It achieved an F1 score of 0.942, corresponding to an accuracy of 91.4%, a sensitivity of 94.1%, and a specificity of 88.3%. The results showed that the proposed approach outperformed the state-of-the-art feature-based method, which had a precision of 85.2%, a specificity of 70.1%, a sensitivity of 85.9%, an accuracy of 85.5%, and an accuracy of 80.2% on the HMC-QU dataset. On the external validation set, the proposed model still performed well, with an F1 score of 0.8, an accuracy of 76.7%, a sensitivity of 77.8%, and a specificity of 75.0%.
UNASSIGNED: Our study demonstrated the ability to accurately predict MI in echocardiograms by combining information from several segmentation models. Further research is necessary to determine its potential use in clinical settings as a tool to assist cardiologists and technicians with objective assessments and reduce dependence on operator subjectivity. Our research codes are available on GitHub at https://github.com/vinuni-vishc/mi-detection-echo.
UNASSIGNED: Our method combines myocardial segment displacement features from multiple segmentation models, which are then input into a typical classifier to estimate the risk of MI. We validated the proposed approach on two datasets: the public HMC-QU dataset (109 echocardiograms) for training and validation, and an E-Hospital dataset (60 echocardiograms) from a local clinical site in Vietnam for independent testing. Model performance was evaluated based on accuracy, sensitivity, and specificity.
UNASSIGNED: The proposed approach demonstrated excellent performance in detecting MI. It achieved an F1 score of 0.942, corresponding to an accuracy of 91.4%, a sensitivity of 94.1%, and a specificity of 88.3%. The results showed that the proposed approach outperformed the state-of-the-art feature-based method, which had a precision of 85.2%, a specificity of 70.1%, a sensitivity of 85.9%, an accuracy of 85.5%, and an accuracy of 80.2% on the HMC-QU dataset. On the external validation set, the proposed model still performed well, with an F1 score of 0.8, an accuracy of 76.7%, a sensitivity of 77.8%, and a specificity of 75.0%.
UNASSIGNED: Our study demonstrated the ability to accurately predict MI in echocardiograms by combining information from several segmentation models. Further research is necessary to determine its potential use in clinical settings as a tool to assist cardiologists and technicians with objective assessments and reduce dependence on operator subjectivity. Our research codes are available on GitHub at https://github.com/vinuni-vishc/mi-detection-echo.
摘要：
■早期发现和定位心肌梗死（MI）可以通过及时的治疗干预措施来减轻心脏损害的严重程度。近年来,深度学习技术有望在超声心动图图像中检测MI。现有的尝试通常将该任务表述为分类,并且依赖于单个分割模型来估计心肌段位移。然而,没有检查分割准确性如何影响MI分类性能或使用集成学习方法的潜在好处。我们的研究调查了这种关系，并引入了一种稳健的方法，该方法结合了来自多个分割模型的特征，以通过利用集成学习来提高MI分类性能。
■我们的方法结合了来自多个分割模型的心肌节段位移特征，然后将其输入到典型的分类器中以估计MI的风险。我们在两个数据集上验证了所提出的方法：用于训练和验证的公共HMC-QU数据集（109个超声心动图），和来自越南当地临床站点的电子医院数据集（60张超声心动图）进行独立测试。基于准确性评估模型性能，灵敏度,和特异性。
■所提出的方法在检测MI方面表现出优异的性能。它获得了0.942的F1评分，对应的准确率为91.4%，灵敏度为94.1%，特异性为88.3%。结果表明，该方法优于最先进的基于特征的方法，精度为85.2%，特异性为70.1%，灵敏度为85.9%，准确率为85.5%，在HMC-QU数据集上的准确率为80.2%。在外部验证集上，所提出的模型仍然表现良好，F1得分为0.8，准确率为76.7%，灵敏度为77.8%，特异性为75.0%。
■我们的研究证明了通过结合来自几种分割模型的信息来准确预测超声心动图中MI的能力。需要进一步的研究以确定其在临床环境中的潜在用途，作为辅助心脏病专家和技术人员进行客观评估并减少对操作者主观性的依赖的工具。我们的研究代码可在GitHub上获得，网址为https://github.com/vinuni-vishc/mi-detection-echo。
■我们的方法结合了来自多个分割模型的心肌节段位移特征，然后将其输入到典型的分类器中以估计MI的风险。我们在两个数据集上验证了所提出的方法：用于训练和验证的公共HMC-QU数据集（109个超声心动图），和来自越南当地临床站点的电子医院数据集（60张超声心动图）进行独立测试。基于准确性评估模型性能，灵敏度,和特异性。
■所提出的方法在检测MI方面表现出优异的性能。它获得了0.942的F1评分，对应的准确率为91.4%，灵敏度为94.1%，特异性为88.3%。结果表明，该方法优于最先进的基于特征的方法，精度为85.2%，特异性为70.1%，灵敏度为85.9%，准确率为85.5%，在HMC-QU数据集上的准确率为80.2%。在外部验证集上，所提出的模型仍然表现良好，F1得分为0.8，准确率为76.7%，灵敏度为77.8%，特异性为75.0%。
■我们的研究证明了通过结合来自几种分割模型的信息来准确预测超声心动图中MI的能力。需要进一步的研究以确定其在临床环境中的潜在用途，作为辅助心脏病专家和技术人员进行客观评估并减少对操作者主观性的依赖的工具。我们的研究代码可在GitHub上获得，网址为https://github.com/vinuni-vishc/mi-detection-echo。
