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 language | English (US) |
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Pages (from-to) | 198-212 |
Number of pages | 15 |
Journal | Computers in Biology and Medicine |
Volume | 91 |
DOIs | |
State | Published - Dec 1 2017 |
Externally published | Yes |
Keywords
- Atherosclerosis
- Cardiovascular disease
- Carotid artery
- Coronary arteries
- Ultrasound imaging
ASJC Scopus subject areas
- Computer Science Applications
- Health Informatics