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

Sumit K. Banchhor, Narendra D. Londhe, Tadashi Araki, Luca Saba, Petia Radeva, John R. Laird, Jasjit S. Suri

Research output: Contribution to journalArticlepeer-review

15 Scopus citations

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.

Original languageEnglish (US)
Pages (from-to)198-212
Number of pages15
JournalComputers in Biology and Medicine
Volume91
DOIs
StatePublished - Dec 1 2017
Externally publishedYes

Keywords

  • Atherosclerosis
  • Cardiovascular disease
  • Carotid artery
  • Coronary arteries
  • Ultrasound imaging

ASJC Scopus subject areas

  • Computer Science Applications
  • Health Informatics

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