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A Special Report on Changing Trends in Preventive Stroke/Cardiovascular Risk Assessment Via B-Mode Ultrasonography

  • Cardiovascular Disease and Stroke (S. Prabhakaran, Section Editor)
  • Published:
Current Atherosclerosis Reports Aims and scope Submit manuscript

Abstract

Purpose of Review

Cardiovascular disease (CVD) and stroke risk assessment have been largely based on the success of traditional statistically derived risk calculators such as Pooled Cohort Risk Score or Framingham Risk Score. However, over the last decade, automated computational paradigms such as machine learning (ML) and deep learning (DL) techniques have penetrated into a variety of medical domains including CVD/stroke risk assessment. This review is mainly focused on the changing trends in CVD/stroke risk assessment and its stratification from statistical-based models to ML-based paradigms using non-invasive carotid ultrasonography.

Recent Findings

In this review, ML-based strategies are categorized into two types: non-image (or conventional ML-based) and image-based (or integrated ML-based). The success of conventional (non-image-based) ML-based algorithms lies in the different data-driven patterns or features which are used to train the ML systems. Typically these features are the patients’ demographics, serum biomarkers, and multiple clinical parameters. The integrated (image-based) ML-based algorithms integrate the features derived from the ultrasound scans of the arterial walls (such as morphological measurements) with conventional risk factors in ML frameworks.

Summary

Even though the review covers ML-based system designs for carotid and coronary ultrasonography, the main focus of the review is on CVD/stroke risk scores based on carotid ultrasound. There are two key conclusions from this review: (i) fusion of image-based features with conventional cardiovascular risk factors can lead to more accurate CVD/stroke risk stratification; (ii) the ability to handle multiple sources of information in big data framework using artificial intelligence-based paradigms (such as ML and DL) is likely to be the future in preventive CVD/stroke risk assessment.

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Acknowledgments

Authors would like thank the editors and reviewers of the Current Reports of Atherosclerosis for giving valuable suggestions for improving the manuscript.

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Authors and Affiliations

  1. Department of ECE, Visvesvaraya National Institute of Technology, Nagpur, Maharashtra, India

    Ankush Jamthikar & Deep Gupta

  2. Department of Cardiology, Indraprastha APOLLO Hospitals, New Delhi, India

    Narendra N. Khanna

  3. Division of Cardiovascular Medicine, Toho University, Tokyo, Japan

    Tadashi Araki

  4. Department of Radiology, University of Cagliari, Cagliari, Italy

    Luca Saba

  5. Vascular Screening and Diagnostic Centre, University of Cyprus, Nicosia, Cyprus

    Andrew Nicolaides

  6. Cardiovascular Medicine, University of Virginia, Charlottesville, VA, USA

    Aditya Sharma

  7. Department of Neurology, University Medical Centre Maribor, Maribor, Slovenia

    Tomaz Omerzu & Monika Turk

  8. Brown University, Providence, RI, USA

    Harman S. Suri

  9. Department of Radiology, Cornell Medical Center, New York, NY, USA

    Ajay Gupta

  10. Cardiology Clinic, Onassis Cardiac Surgery Center, Athens, Greece

    Sophie Mavrogeni

  11. Heart and Vascular Institute, Adventist Health St. Helena, St. Helena, CA, USA

    John R. Laird

  12. Department of Cardiovascular Prevention & Research Unit Clinic & Laboratory of Pathophysiology|, National and Kapodistrian University of Athens, Athens, Greece

    Athanasios Protogerou

  13. Rheumatology Unit, National Kapodistrian University of Athens, Athens, Greece

    Petros P. Sfikakis

  14. R&D Academic Affairs, Dudley Group NHS Foundation Trust, Dudley, UK

    George D. Kitas

  15. MV Hospital for Diabetes and Professor M Viswanathan Diabetes Research Centre, Chennai, India

    Vijay Viswanathan

  16. Minimally Invasive Urology Institute, Brown University, Providence, RI, USA

    Gyan Pareek

  17. Men’s Health Center, Miriam Hospital Providence, Providence, RI, USA

    Martin Miner

  18. Stroke Monitoring and Diagnostic Division, AtheroPoint™, Roseville, CA, 95661, USA

    Jasjit S. Suri

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Correspondence to Jasjit S. Suri.

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Conflict of Interest

Ankush Jamthikar, Deep Gupta, Narendra N. Khanna, Tadashi Araki, Luca Saba, Andrew Nicolaides, Aditya Sharma, Tomaz Omerzu, Harman S. Suri, Ajay Gupta, Sophie Mavrogeni, Monika Turk, John R. Laird, Athanasios Protogerou, Petros P. Sfikakis, George D. Kitas, Vijay Viswanathan, Gyan Pareek, and Martin Miner declare no conflict of interest. Dr. Jasjit S Suri is affiliated to AtheroPoint, focused in the area of stroke and cardiovascular imaging.

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Appendix: Performance Evaluation Parameters

Appendix: Performance Evaluation Parameters

Sensitivity and specificity are computed using true positive (TP), true negative (TN), false positive (FP), and false negative (FN). TP indicates the count for which predicted class labels matches with ground truth label for high-risk threshold point, FN is defined as the number of times the predicted class labels that are incorrectly classified as low-risk, FP is defined as the number of times the predicted class labels that are incorrectly classified as high-risk, and TN is defined as the number of times predicted class labels that are correctly matched with low-risk ground truth label. Sensitivity and specificity are mathematically represented as, \( \mathrm{Sensitivity}=\frac{\mathrm{TP}}{\left(\mathrm{TP}+\mathrm{FN}\right)} \) and \( \mathrm{Specificity}=\frac{\mathrm{TN}}{\left(\mathrm{TN}+\mathrm{FP}\right)} \). Furthermore, the accuracy of risk stratification is mathematically represented as: \( \mathrm{Accuracy}=\frac{\mathrm{TP}+\mathrm{TN}}{\left(\mathrm{TP}+\mathrm{FN}+\mathrm{FP}+\mathrm{FN}\right)} \).

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Jamthikar, A., Gupta, D., Khanna, N.N. et al. A Special Report on Changing Trends in Preventive Stroke/Cardiovascular Risk Assessment Via B-Mode Ultrasonography. Curr Atheroscler Rep 21, 25 (2019). https://doi.org/10.1007/s11883-019-0788-4

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