Skip to main content

Advertisement

SpringerLink
Log in

Role of artificial intelligence in cardiovascular risk prediction and outcomes: comparison of machine-learning and conventional statistical approaches for the analysis of carotid ultrasound features and intra-plaque neovascularization

  • Original Paper
  • Published:
The International Journal of Cardiovascular Imaging Aims and scope Submit manuscript

Abstract

The aim of this study was to compare machine learning (ML) methods with conventional statistical methods to investigate the predictive ability of carotid plaque characteristics for assessing the risk of coronary artery disease (CAD) and cardiovascular (CV) events. Focused carotid B-mode ultrasound, contrast-enhanced ultrasound, and coronary angiography were performed on 459 participants. These participants were followed for 30 days. Plaque characteristics such as carotid intima-media thickness (cIMT), maximum plaque height (MPH), total plaque area (TPA), and intraplaque neovascularization (IPN) were measured at baseline. Two ML-based algorithms—random forest (RF) and random survival forest (RSF) were used for CAD and CV event prediction. The performance of these algorithms was compared against (i) univariate and multivariate analysis for CAD prediction using the area-under-the-curve (AUC) and (ii) Cox proportional hazard model for CV event prediction using the concordance index (c-index). There was a significant association between CAD and carotid plaque characteristics [cIMT (odds ratio (OR) = 1.49, p = 0.03), MPH (OR = 2.44, p < 0.0001), TPA (OR = 1.61, p < 0.0001), and IPN (OR = 2.78, p < 0.0001)]. IPN alone reported significant CV event prediction (hazard ratio = 1.24, p < 0.0001). CAD prediction using the RF algorithm reported an improvement in AUC by ~ 3% over the univariate analysis with IPN alone (0.97 vs. 0.94, p < 0.0001). Cardiovascular event prediction using RSF demonstrated an improvement in the c-index by ~ 17.8% over the Cox-based model (0.86 vs. 0.73). Carotid imaging phenotypes and IPN were associated with CAD and CV events. The ML-based system is superior to the conventional statistically-derived approaches for CAD prediction and survival analysis.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

Similar content being viewed by others

References

  1. Organization WH. Cardiovascular diseases (CVDs): Key facts by WHO May 2016. http://www.who.int/news-room/fact-sheets/detail/cardiovascular-diseases-(cvds).

  2. Suri JS, Kathuria C, Molinari F (2010) Atherosclerosis disease management. Springer Science & Business Media, New York

    Google Scholar 

  3. Saba L, Jamthikar A, Gupta D, Khanna NN, Viskovic K, Suri HS, Gupta A, Mavrogeni S, Turk M, Laird JR, Pareek G, Miner M, Sfikakis P, Protogerou A, Kitas GD, Viswanathan V, Nicolaides A, Bhatt D, Suri JS (2019) Global perspective on carotid intima-media thickness and plaque: should the current measurement guidelines be revisited? Int Angiol 38(6):451–465

    PubMed  Google Scholar 

  4. Amato M, Montorsi P, Ravani A, Oldani E, Galli S, Ravagnani PM, Tremoli E, Baldassarre D (2007) Carotid intima-media thickness by B-mode ultrasound as surrogate of coronary atherosclerosis: correlation with quantitative coronary angiography and coronary intravascular ultrasound findings. Eur Heart J 28(17):2094–2101

    Article  PubMed  Google Scholar 

  5. Bots ML (2006) Carotid intima-media thickness as a surrogate marker for cardiovascular disease in intervention studies. Curr Med Res Opin 22(11):2181–2190

    Article  PubMed  Google Scholar 

  6. Spence JD (2002) Ultrasound measurement of carotid plaque as a surrogate outcome for coronary artery disease. Am J Cardiol 89(4):10–15

    Article  Google Scholar 

  7. Mantella LE, Colledanchise KN, Hétu M-F, Feinstein SB, Abunassar J, Johri AM (2019) Carotid intraplaque neovascularization predicts coronary artery disease and cardiovascular events. Eur Heart J Cardiovasc Imaging 20(11):1239–1247

    Article  PubMed  PubMed Central  Google Scholar 

  8. Goldstein BA, Navar AM, Carter RE (2016) Moving beyond regression techniques in cardiovascular risk prediction: applying machine learning to address analytic challenges. Eur Heart J 38(23):1805–1814

    PubMed Central  Google Scholar 

  9. Jamthikar AD, Gupta D, Saba L, Khanna NN, Viskovic K, Mavrogeni S, Laird JR, Sattar N, Johri AM, Pareek G, Miner M, Sfikakis PP, Protogerou A, Viswanathan V, Sharma A, Kitas GD, Nicolaides A, Kolluri R, Suri JS (2020) Artificial intelligence framework for predictive cardiovascular and stroke risk assessment models: A narrative review of integrated approaches using carotid ultrasound. Comput Biol Med. https://doi.org/10.1016/j.compbiomed.2020.104043

    Article  PubMed  Google Scholar 

  10. Alaa AM, Bolton T, Di Angelantonio E, Rudd JH, van Der Schaar M (2019) Cardiovascular disease risk prediction using automated machine learning: A prospective study of 423,604 UK Biobank participants. PloS one. https://doi.org/10.1371/journal.pone.0213653

    Article  PubMed  PubMed Central  Google Scholar 

  11. Weng SF, Reps J, Kai J, Garibaldi JM, Qureshi N (2017) Can machine-learning improve cardiovascular risk prediction using routine clinical data? PloS one. https://doi.org/10.1371/journal.pone.0174944

    Article  PubMed  PubMed Central  Google Scholar 

  12. Biswas M, Kuppili V, Saba L, Edla DR, Suri HS, Cuadrado-Godia E, Laird JR, Marinhoe RT, Sanches JM, Nicolaides A (2019) State-of-the-art review on deep learning in medical imaging. Front Biosci (Landmark edition) 24:392–426

    Article  Google Scholar 

  13. Jamthikar A, Gupta D, Khanna NN, Saba L, Araki T, Viskovic K, Suri HS, Gupta A, Mavrogeni S, Turk M, Laird JR, Pareek G, Miner M, Sfikakis PP, Protogerou A, Kitas GD, Viswanathan V, Nicolaides A, Bhatt DL, Suri JS (2019) A low-cost machine learning-based cardiovascular/stroke risk assessment system: integration of conventional factors with image phenotypes. Cardiovasc Diagn Ther 9(5):420–430

    Article  PubMed  PubMed Central  Google Scholar 

  14. Kakadiaris IA, Vrigkas M, Yen AA, Kuznetsova T, Budoff M, Naghavi M (2018) Machine Learning Outperforms ACC/AHA CVD Risk Calculator in MESA. J Am Heart Assoc. https://doi.org/10.1161/JAHA.118.009476

    Article  PubMed  PubMed Central  Google Scholar 

  15. Jamthikar A, Gupta D, Saba L, Khanna NN, Araki T, Viskovic K, Mavrogeni S, Laird JR, Pareek G, Miner M, Sfikakis PP, Protogerou A, Viswanathan V, Sharma A, Nicolaides A, Kitas GD, Suri JS (2020) Cardiovascular/stroke risk predictive calculators: a comparison between statistical and machine learning models. Cardiovasc Diagn Ther 10(4):919–938

    Article  PubMed  PubMed Central  Google Scholar 

  16. Jamthikar A, Gupta D, Khanna NN, Saba L, Laird JR, Suri JS (2020) Cardiovascular/stroke risk prevention: A new machine learning framework integrating carotid ultrasound image-based phenotypes and its harmonics with conventional risk factors. Indian Heart J. https://doi.org/10.1016/j.ihj.2020.06.004

    Article  PubMed  PubMed Central  Google Scholar 

  17. Wallert J, Tomasoni M, Madison G, Held C (2017) Predicting two-year survival versus non-survival after first myocardial infarction using machine learning and Swedish national register data. BMC Med Inform Decis Mak 17(1):99

    Article  PubMed  PubMed Central  Google Scholar 

  18. Miao F, Cai Y-P, Zhang Y-X, Fan X-M, Li Y (2018) Predictive modeling of hospital mortality for patients with heart failure by using an improved random survival forest. IEEE Access 6:7244–7253

    Article  Google Scholar 

  19. Johri AM, Chitty DW, Matangi M, Malik P, Mousavi P, Day A, Gravett M, Simpson C (2013) Can carotid bulb plaque assessment rule out significant coronary artery disease? A comparison of plaque quantification by two-and three-dimensional ultrasound. J Am Soc Echocardiogr 26(1):86–95

    Article  PubMed  Google Scholar 

  20. Jamthikar A, Gupta D, Mantella LE, Saba L, Laird JR, Johri AM, Suri JS (2020) Multiclass machine learning vs. conventional calculators for Stroke/CVD Risk assessment using carotid plaque predictors with coronary angiography scores as gold standard: A 500 participants study. Int J Cardiovasc Imaging. https://doi.org/10.1007/s10554-020-02099-7

    Article  PubMed  Google Scholar 

  21. Johri AM, Calnan CM, Matangi MF, MacHaalany J, Hétu M-F (2016) Focused vascular ultrasound for the assessment of atherosclerosis: a proof-of-concept study. J Am Soc Echocardiogr 29(9):842–849

    Article  PubMed  Google Scholar 

  22. Members TF, Montalescot G, Sechtem U, Achenbach S, Andreotti F, Arden C, Budaj A, Bugiardini R, Crea F, Cuisset T (2013) 2013 ESC guidelines on the management of stable coronary artery disease: the Task Force on the management of stable coronary artery disease of the European Society of Cardiology. Eur Heart J 34(38):2949–3003

    Article  Google Scholar 

  23. Braun T, Spiliopoulos S, Veltman C, Hergesell V, Passow A, Tenderich G, Borggrefe M, Koerner MM (2020) Detection of myocardial ischemia due to clinically asymptomatic coronary artery stenosis at rest using supervised artificial intelligence-enabled vectorcardiography–A five-fold cross validation of accuracy. J Electrocardiol 59:100–105

    Article  PubMed  Google Scholar 

  24. Touboul P-J, Hennerici M, Meairs S, Adams H, Amarenco P, Bornstein N, Csiba L, Desvarieux M, Ebrahim S, Hernandez RH (2012) Mannheim carotid intima-media thickness and plaque consensus (2004–2006–2011). Cerebrovasc Dis 34(4):290–296

    Article  PubMed  Google Scholar 

  25. Deyama J, Nakamura T, Takishima I, Fujioka D, Kawabata K-I, Obata J-E, Watanabe K, Watanabe Y, Saito Y, Mishina H (2013) Contrast-enhanced ultrasound imaging of carotid plaque neovascularization is useful for identifying high-risk patients with coronary artery disease. Circ J 77(6):1499–1507

    Article  PubMed  Google Scholar 

  26. Acharya UR, Mookiah MRK, Sree SV, Afonso D, Sanches J, Shafique S, Nicolaides A, Pedro LM, Fernandes JF, Suri JS (2013) Atherosclerotic plaque tissue characterization in 2D ultrasound longitudinal carotid scans for automated classification: a paradigm for stroke risk assessment. Med Biol Eng Comput 51(5):513–523

    Article  PubMed  Google Scholar 

  27. Acharya UR, Sree SV, Ribeiro R, Krishnamurthi G, Marinho RT, Sanches J, Suri JS (2012) Data mining framework for fatty liver disease classification in ultrasound: a hybrid feature extraction paradigm. Med Phys 39(7Part1):4255–4264

    Article  PubMed  Google Scholar 

  28. Martis RJ, Acharya UR, Prasad H, Chua CK, Lim CM, Suri JS (2013) Application of higher order statistics for atrial arrhythmia classification. Biomed Signal Process Control 8(6):888–900

    Article  Google Scholar 

  29. Acharya UR, Sree SV, Krishnan MMR, Krishnananda N, Ranjan S, Umesh P, Suri JS (2013) Automated classification of patients with coronary artery disease using grayscale features from left ventricle echocardiographic images. Comput Methods Programs Biomed 112(3):624–632

    Article  PubMed  Google Scholar 

  30. El-Baz A, Suri JS (2011) Lung imaging and computer aided diagnosis. CRC Press, Boca Raton

    Google Scholar 

  31. Acharya UR, Faust O, Sree SV, Molinari F, Saba L, Nicolaides A, Suri JS (2012) An accurate and generalized approach to plaque characterization in 346 carotid ultrasound scans. IEEE Trans Instrum Meas 61(4):1045–1053

    Article  Google Scholar 

  32. Saba L, Dey N, Ashour AS, Samanta S, Nath SS, Chakraborty S, Sanches J, Kumar D, Marinho R, Suri JS (2016) Automated stratification of liver disease in ultrasound: An online accurate feature classification paradigm. Comput Methods Programs Biomed 130:118–134

    Article  PubMed  Google Scholar 

  33. Wong T-T (2015) Performance evaluation of classification algorithms by k-fold and leave-one-out cross validation. Pattern Recogn 48(9):2839–2846

    Article  Google Scholar 

  34. Duval S, Van’t Hof JR, Steffen LM, Luepker RV (2020) Estimation of cardiovascular risk from self-reported knowledge of risk factors: insights from the Minnesota heart survey. Clin Epidemiol 12:41–49

    Article  PubMed  PubMed Central  Google Scholar 

  35. Sammut C, Webb GI (2010) Leave-One-Out Cross-Validation. In: Sammut C, Webb GI (eds) Encyclopedia of Machine Learning. Springer US, Boston, MA, pp 600–601

    Chapter  Google Scholar 

  36. Ho TK. Random decision forests. In: Document analysis and recognition, 1995., proceedings of the third international conference on. 1995: Abstract 1, p. 278–282. IEEE.

  37. Goff DC, Lloyd-Jones DM, Bennett G, Coady S, Dagostino RB, Gibbons R, Greenland P, Lackland DT, Levy D, Odonnell CJ (2014) 2013 ACC/AHA guideline on the assessment of cardiovascular risk: a report of the American College of Cardiology/American Heart Association Task Force on Practice Guidelines. J Ame Coll Cardiol 63(25):2935–2959

    Article  Google Scholar 

  38. Ishwaran H, Kogalur UB, Blackstone EH, Lauer MS (2008) Random survival forests. Ann Appl Stat 2(3):841–860

    Article  Google Scholar 

  39. Pedregosa F, Varoquaux G, Gramfort A, Michel V, Thirion B, Grisel O, Blondel M, Prettenhofer P, Weiss R, Dubourg V (2011) Scikit-learn: Machine learning in Python. J Mach Learn Res 12:2825–2830

    Google Scholar 

  40. Sebastian P (2020) Scikit-survival: A library for time-to-event analysis built on top of scikit-learn. J Mach Learn Res 21(212):1–6

    Google Scholar 

  41. Jamthikar AD, Gupta D, Johri AM, Mantella LE, Saba L, Kolluri R, Sharma AM, Viswanathan V, Nicolaides A, Suri JS (2020) Low-cost office-based cardiovascular risk stratification using machine learning and focused carotid ultrasound in an Asian-Indian cohort. J Med Syst 44(12):208

    Article  PubMed  Google Scholar 

  42. Johri AM, Behl P, Hétu MF, Haqqi M, Ewart P, Day AG, Parfrey B, Matangi MF (2016) Carotid ultrasound maximum plaque height–a sensitive imaging biomarker for the assessment of significant coronary artery disease. Echocardiography 33(2):281–289

    Article  PubMed  Google Scholar 

  43. Krittanawong C, Virk HUH, Bangalore S, Wang Z, Johnson KW, Pinotti R, Zhang H, Kaplin S, Narasimhan B, Kitai T (2020) Machine learning prediction in cardiovascular diseases: a meta-analysis. Sci Rep 10(1):1–11

    Article  CAS  Google Scholar 

  44. Velusamy D, Ramasamy K (2021) Ensemble of heterogeneous classifiers for diagnosis and prediction of coronary artery disease with reduced feature subset. Comput Methods Programs Biomed. https://doi.org/10.1016/j.cmpb.2020.105770

    Article  PubMed  Google Scholar 

  45. Ikeda N, Araki T, Sugi K, Nakamura M, Deidda M, Molinari F, Meiburger KM, Acharya UR, Saba L, Bassareo PP, Di Martino M, Nagashima Y, Mercuro G, Nakano M, Nicolaides A, Suri JS (2014) Ankle-brachial index and its link to automated carotid ultrasound measurement of intima-media thickness variability in 500 Japanese coronary artery disease patients. Curr Atheroscler Rep 16(3):393

    Article  PubMed  Google Scholar 

  46. Ikeda N, Dey N, Sharma A, Gupta A, Bose S, Acharjee S, Shafique S, Cuadrado-Godia E, Araki T, Saba L, Laird JR, Nicolaides A, Suri JS (2017) Automated segmental-IMT measurement in thin/thick plaque with bulb presence in carotid ultrasound from multiple scanners: Stroke risk assessment. Comput Methods Programs Biomed 141:73–81

    Article  PubMed  Google Scholar 

  47. Saba L, Than JCM, Noor NM, Rijal OM, Kassim RM, Yunus A, Ng CR, Suri JS (2016) Inter-observer variability analysis of automatic lung delineation in normal and disease patients. Journal of Medical Systems. https://doi.org/10.1007/s10916-016-0504-7

    Article  PubMed  Google Scholar 

  48. Molinari F, Meiburger KM, Zeng G, Saba L, Rajendra Acharya U, Famiglietti L, Georgiou N, Nicolaides A, Sriswan Mamidi R, Kuper H, Suri JS (2012) Automated carotid IMT measurement and its validation in low contrast ultrasound database of 885 patient Indian population epidemiological study: results of AtheroEdgeTM Software. Int Angiol 31(1):42–53

    PubMed  PubMed Central  CAS  Google Scholar 

  49. Saba L, Banchhor SK, Araki T, Suri HS, Londhe ND, Laird JR, Viskovic K, Suri JS (2018) Intra-and inter-operator reproducibility analysis of automated cloud-based carotid intima media thickness ultrasound measurement. J Clin Diagn Res 12(2):KC01-KC11

    Google Scholar 

  50. Acharya UR, Sree SV, Molinari F, Saba L, Nicolaides A, Suri JS (2015) An automated technique for carotid far wall classification using grayscale features and wall thickness variability. J Clin Ultrasound 43(5):302–311

    Article  PubMed  Google Scholar 

  51. Banchhor SK, Londhe ND, Araki T, Saba L, Radeva P, Khanna NN, Suri JS (2018) Calcium detection, its quantification, and grayscale morphology-based risk stratification using machine learning in multimodality big data coronary and carotid scans: A review. Comput Biol Med 101:184–198

    Article  PubMed  CAS  Google Scholar 

  52. Saba L, Biswas M, Kuppili V, Godia EC, Suri HS, Edla DR, Omerzu T, Laird JR, Khanna NN, Mavrogeni S (2019) The present and future of deep learning in radiology. Eur J Radiol. https://doi.org/10.1016/j.ejrad.2019.02.038

    Article  PubMed  PubMed Central  Google Scholar 

  53. Molinari F, Mantovani A, Deandrea M, Limone P, Garberoglio R, Suri JS (2010) Characterization of single thyroid nodules by contrast-enhanced 3-D ultrasound. Ultrasound Med Biol 36(10):1616–1625

    Article  PubMed  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Medicine, Division of Cardiology, Queen’s University, Kingston, ON, Canada

    Amer M. Johri

  2. Department of Biomedical and Molecular Sciences, Queen’s University, Kingston, ON, Canada

    Laura E. Mantella

  3. Department of ECE, Visvesvaraya National Institute of Technology, Nagpur, MH, India

    Ankush D. Jamthikar

  4. Department of Radiology, Azienda Ospedaliero Universitaria (A.O.U.), Cagliari, Italy

    Luca Saba

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

    John R. Laird

  6. Stroke Diagnosis and Monitoring Division, AtheroPoint™, Roseville, CA, 95661, USA

    Jasjit S. Suri

Authors
  1. Amer M. Johri
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Laura E. Mantella
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Ankush D. Jamthikar
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Luca Saba
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. John R. Laird
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Jasjit S. Suri
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Jasjit S. Suri.

Ethics declarations

Conflict of interest

All authors declare that they have no conflict of interest to disclose.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Below is the link to the electronic supplementary material.

Supplementary file1 (docx 260 kb)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Johri, A.M., Mantella, L.E., Jamthikar, A.D. et al. Role of artificial intelligence in cardiovascular risk prediction and outcomes: comparison of machine-learning and conventional statistical approaches for the analysis of carotid ultrasound features and intra-plaque neovascularization. Int J Cardiovasc Imaging 37, 3145–3156 (2021). https://doi.org/10.1007/s10554-021-02294-0

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10554-021-02294-0

Keywords

Search

Navigation