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

Journal: The international journal of cardiovascular imaging
PMID: 38678144

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.

Authors

  • Mrinalini Bhagawati
    Department of Biomedical Engineering, North-Eastern Hill University, Shillong, India.
  • Sudip Paul
    a Department of Biomedical Engineering , North Eastern Hill University , Meghalaya , India ;
  • Laura Mantella
    Division of Cardiology, Department of Medicine, University of Toronto, Toronto, Canada.
  • Amer M Johri
    Division of Cardiology, Department of Medicine, Queen's University, Kingston, ON, Canada.
  • John R Laird
    UC Davis Vascular Center, University of California, Davis, CA, USA.
  • Inder M Singh
    Stroke Monitoring and Diagnostic Division, AtheroPoint™, Roseville, 95747, CA, USA.
  • Rajesh Singh
    Division of Research and Innovation, UTI, Uttaranchal University, Dehradun, India.
  • Deepak Garg
    Department of Computer Science Engineering, SEAS, Bennett University, Greater Noida, PIN: 201308, Uttar Pradesh, India.
  • Mostafa M Fouda
    Department of Electrical and Computer Engineering, College of Science and Engineering, Idaho State University, Pocatello, ID 83209, USA.
  • Narendra N Khanna
    Cardiology Department, Apollo Hospitals, New Delhi, India.
  • Riccardo Cau
    Department of Radiology, Azienda Ospedaliero Universitaria (A.O.U.), di Cagliari, Cagliari.
  • Ajith Abraham
    Machine Intelligence Research Labs, Auburn, USA.
  • Mostafa Al-Maini
    Allergy, Clinical Immunology and Rheumatology Institute, Toronto, ON, Canada.
  • Esma R Isenovic
    Laboratory for Molecular Genetics and Radiobiology, University of Belgrade, Belgrade, Serbia.
  • Aditya M Sharma
    Cardiovascular Medicine, University of Virginia, Charlottesville, VA, USA.
  • Jose Fernandes E Fernandes
    Department of Vascular Surgery, University of Lisbon, Lisbon, Portugal.
  • Seemant Chaturvedi
    Department of Neurology & Stroke Program, University of Maryland, Baltimore, MD, USA.
  • Mannudeep K Karla
    Department of Radiology, Massachusetts General Hospital, Boston, MA, USA.
  • Andrew Nicolaides
    Vascular Screening and Diagnostic Centre, London, England, United Kingdom; Vascular Diagnostic Center, University of Cyprus, Nicosia, Cyprus.
  • Luca Saba
    Department of Radiology, A.O.U., Italy.
  • Jasjit S Suri
    Advanced Knowledge Engineering Center, Global Biomedical Technologies, Inc., Roseville, CA, USA. Electronic address: jsuri@comcast.net.

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