Saba Luca, Jain Pankaj K, Suri Harman S, Ikeda Nobutaka, Araki Tadashi, Singh Bikesh K, Nicolaides Andrew, Shafique Shoaib, Gupta Ajay, Laird John R, Suri Jasjit S
Department of Radiology, University of Cagliari, Cagliari, Italy.
Point-of-Care Devices, Global Biomedical Technologies, Inc., Roseville, CA, USA.
J Med Syst. 2017 Jun;41(6):98. doi: 10.1007/s10916-017-0745-0. Epub 2017 May 13.
Severe atherosclerosis disease in carotid arteries causes stenosis which in turn leads to stroke. Machine learning systems have been previously developed for plaque wall risk assessment using morphology-based characterization. The fundamental assumption in such systems is the extraction of the grayscale features of the plaque region. Even though these systems have the ability to perform risk stratification, they lack the ability to achieve higher performance due their inability to select and retain dominant features. This paper introduces a polling-based principal component analysis (PCA) strategy embedded in the machine learning framework to select and retain dominant features, resulting in superior performance. This leads to more stability and reliability. The automated system uses offline image data along with the ground truth labels to generate the parameters, which are then used to transform the online grayscale features to predict the risk of stroke. A set of sixteen grayscale plaque features is computed. Utilizing the cross-validation protocol (K = 10), and the PCA cutoff of 0.995, the machine learning system is able to achieve an accuracy of 98.55 and 98.83%corresponding to the carotidfar wall and near wall plaques, respectively. The corresponding reliability of the system was 94.56 and 95.63%, respectively. The automated system was validated against the manual risk assessment system and the precision of merit for same cross-validation settings and PCA cutoffs are 98.28 and 93.92%for the far and the near wall, respectively.PCA-embedded morphology-based plaque characterization shows a powerful strategy for risk assessment and can be adapted in clinical settings.
颈动脉严重动脉粥样硬化疾病会导致狭窄，进而引发中风。机器学习系统此前已被开发用于基于形态学特征的斑块壁风险评估。此类系统的基本假设是提取斑块区域的灰度特征。尽管这些系统有能力进行风险分层，但由于无法选择和保留主导特征，它们缺乏实现更高性能的能力。本文介绍了一种嵌入机器学习框架的基于投票的主成分分析（PCA）策略，以选择和保留主导特征，从而实现卓越性能。这带来了更高的稳定性和可靠性。该自动化系统使用离线图像数据以及地面真值标签来生成参数，然后这些参数用于转换在线灰度特征以预测中风风险。计算了一组16个灰度斑块特征。利用交叉验证协议（K = 10）以及PCA截止值0.995，该机器学习系统对于颈动脉远壁和近壁斑块分别能够达到98.55%和98.83%的准确率。该系统相应的可靠性分别为94.56%和95.63%。该自动化系统针对手动风险评估系统进行了验证，对于相同的交叉验证设置和PCA截止值，远壁和近壁的优点精度分别为98.28%和93.92%。基于PCA嵌入的基于形态学的斑块特征显示出一种强大的风险评估策略，并且可以应用于临床环境。
