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

Comput Biol Med. 2017 Dec 1:91:198-212. doi: 10.1016/j.compbiomed.2017.10.019. Epub 2017 Oct 23.

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.

Keywords: Atherosclerosis; Cardiovascular disease; Carotid artery; Coronary arteries; Ultrasound imaging.

MeSH terms

  • Adult
  • Aged
  • Aged, 80 and over
  • Carotid Arteries / diagnostic imaging*
  • Cohort Studies
  • Coronary Artery Disease / diagnostic imaging*
  • Humans
  • Machine Learning
  • Middle Aged
  • Plaque, Atherosclerotic / diagnostic imaging*
  • Reproducibility of Results
  • Sensitivity and Specificity
  • Ultrasonography, Interventional / methods*