• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Computers in Biology and Medicine >Wall-based measurement features provides an improved IVUS coronary artery risk assessment when fused with plaque texture-based features during machine learning paradigm
【24h】

Wall-based measurement features provides an improved IVUS coronary artery risk assessment when fused with plaque texture-based features during machine learning paradigm

机译：基于墙壁的测量功能提供了一种改进的IVUS冠状动脉风险评估，当时在机器学习范式期间融合了斑块纹理的特征

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

Abstract Background Planning of percutaneous interventional procedures involves a pre-screening and risk stratification of the coronary artery disease. Current screening tools use stand-alone plaque texture-based features and therefore lack the ability to stratify the risk. Method This IRB approved study presents a novel strategy for coronary artery disease risk stratification using an amalgamation of IVUS plaque texture-based and wall-based measurement features. Due to common genetic plaque makeup, carotid plaque burden was chosen as a gold standard for risk labels during training-phase of machine learning (ML) paradigm. Cross-validation protocol was adopted to compute the accuracy of the ML framework. A set of 59 plaque texture-based features was padded with six wall-based measurement features to show the improvement in stratification accuracy. The ML system was executed using principle component analysis-based framework for dimensionality reduction and uses support vector machine classifier for training and testing-phases. Results The ML system produced a stratification accuracy of 91.28%, demonstrating an improvement of 5.69% when wall-based measurement features were combined with plaque texture-based features. The fused system showed an improvement in mean sensitivity, specificity, positive predictive value, and area under the curve by: 6.39%, 4.59%, 3.31% and 5.48%, respectively when compared to the stand-alone system. While meeting the stability criteria of 5%, the ML system also showed a high average feature retaining power and mean reliability of 89.32% and 98.24%, respectively. Conclusions The ML system showed an improvement in risk stratification accuracy when the wall-based measurement features were fused with the plaque texture-based features. Highlights ? Amalgamation of IVUS plaque texture-based and wall-based measurement features. ? Principle component analysis-based framework was used for dimensionality reduction. ? During the training phase, carotid plaque burden was chosen as a gold standard. ? Support vector machine was used as a classifier for training and testing-phases. ? Proposed ML system demonstrate improvement in risk stratification accuracy.
机译：摘要经皮介入程序的背景规划涉及冠状动脉疾病的预筛选和风险分层。当前的筛选工具使用基于独立的斑块纹理的特征，因此缺乏分层风险的能力。方法这种IRB批准的研究使用基于IVUS斑块纹理和基于壁的测量特征的融合来提出冠状动脉疾病风险分层的新策略。由于常见的遗传斑块化妆，选择颈动脉斑块负担作为机器学习（ML）范式的训练期间风险标签的金标准。采用交叉验证协议来计算ML框架的准确性。一套59个基于纹理的基于纹理的特征填充了六个基于墙体的测量功能，以显示分层精度的提高。使用基于原理分析分析的框架执行ML系统，用于减少维度减少，并使用支持向量机分类器进行训练和测试阶段。结果ML系统产生91.28％的分层精度，当基于壁的测量功能与基于斑块纹理的特征相结合时，展示了5.69％的提高。融合系统表现出平均敏感性，特异性，阳性预测值和曲线面积的改善：与独立系统相比，分别为6.39％，4.59％，3.31％和5.48％。虽然达到5％的稳定性标准，但ML系统还表现出高平均特征保持力和平均可靠性，分别为89.32％和98.24％。结论当基于壁的测量特征与基于斑块纹理的特征融合时，ML系统显示出风险分层精度的提高。强调 ？基于IVUS斑块纹理和基于壁的测量特征的融合。还基于原理分析的基于分析的框架用于维数减少。还在训练阶段，选择颈动脉斑块负担作为金标准。还支持向量机用作培训和测试阶段的分类器。还提出的ML系统表现出风险分层精度的提高。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号