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The International Journal of Cardiovascular Imaging

28-04-2024 | Original Paper

Deep learning approach for cardiovascular disease risk stratification and survival analysis on a Canadian cohort

Authors: Mrinalini Bhagawati, Sudip Paul, Laura Mantella, Amer M. Johri, John R. Laird, Inder M. Singh, Rajesh Singh, Deepak Garg, Mostafa M. Fouda, Narendra N. Khanna, Riccardo Cau, Ajith Abraham, Mostafa Al-Maini, Esma R. Isenovic, Aditya M. Sharma, Jose Fernandes E. Fernandes, Seemant Chaturvedi, Mannudeep K. Karla, Andrew Nicolaides, Luca Saba, Jasjit S. Suri

Published in: The International Journal of Cardiovascular Imaging

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Abstract

The quantification of carotid plaque has been routinely used to predict cardiovascular risk in cardiovascular disease (CVD) and coronary artery disease (CAD). To determine how well carotid plaque features predict the likelihood of CAD and cardiovascular (CV) events using deep learning (DL) and compare against the machine learning (ML) paradigm. The participants in this study consisted of 459 individuals who had undergone coronary angiography, contrast-enhanced ultrasonography, and focused carotid B-mode ultrasound. Each patient was tracked for thirty days. The measurements on these patients consisted of maximum plaque height (MPH), total plaque area (TPA), carotid intima-media thickness (cIMT), and intraplaque neovascularization (IPN). CAD risk and CV event stratification were performed by applying eight types of DL-based models. Univariate and multivariate analysis was also conducted to predict the most significant risk predictors. The DL’s model effectiveness was evaluated by the area-under-the-curve measurement while the CV event prediction was evaluated using the Cox proportional hazard model (CPHM) and compared against the DL-based concordance index (c-index). IPN showed a substantial ability to predict CV events (p < 0.0001). The best DL system improved by 21% (0.929 vs. 0.762) over the best ML system. DL-based CV event prediction showed a ~ 17% increase in DL-based c-index compared to the CPHM (0.86 vs. 0.73). CAD and CV incidents were linked to IPN and carotid imaging characteristics. For survival analysis and CAD prediction, the DL-based system performs superior to ML-based models.
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Metadata
Title
Deep learning approach for cardiovascular disease risk stratification and survival analysis on a Canadian cohort
Authors
Mrinalini Bhagawati
Sudip Paul
Laura Mantella
Amer M. Johri
John R. Laird
Inder M. Singh
Rajesh Singh
Deepak Garg
Mostafa M. Fouda
Narendra N. Khanna
Riccardo Cau
Ajith Abraham
Mostafa Al-Maini
Esma R. Isenovic
Aditya M. Sharma
Jose Fernandes E. Fernandes
Seemant Chaturvedi
Mannudeep K. Karla
Andrew Nicolaides
Luca Saba
Jasjit S. Suri
Publication date
28-04-2024
Publisher
Springer Netherlands
Published in
The International Journal of Cardiovascular Imaging
Print ISSN: 1569-5794
Electronic ISSN: 1875-8312
DOI
https://doi.org/10.1007/s10554-024-03100-3

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11-04-2024 Lifestyle Modifications News

New intervention for STEMI-related cardiogenic shock

10-04-2024 Myocardial Infarction News

» View more highlights on the ACC 2024 congress hub page

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