@article{3071257,
    title = "Artificial intelligence framework for predictive cardiovascular and stroke risk assessment models: A narrative review of integrated approaches using carotid ultrasound",
    author = "Jamthikar, A.D. and Gupta, D. and Saba, L. and Khanna, N.N. and Viskovic, K. and Mavrogeni, S. and Laird, J.R. and Sattar, N. and Johri, A.M. and Pareek, G. and Miner, M. and Sfikakis, P.P. and Protogerou, A. and Viswanathan, V. and Sharma, A. and Kitas, G.D. and Nicolaides, A. and Kolluri, R. and Suri, J.S.",
    journal = "Computers in Biology and Medicine",
    year = "2020",
    volume = "126",
    publisher = "Elsevier Ireland Ltd",
    issn = "0010-4825",
    doi = "10.1016/j.compbiomed.2020.104043",
    keywords = "Artificial intelligence;  Medical imaging;  Predictive analytics;  Ultrasonics, Cardiovascular disease;  Carotid ultrasounds;  Conventional models;  Health care providers;  Integrated approach;  Non-linear variation;  Risk assessment models;  Risk based approaches, Risk assessment, artificial intelligence;  cardiovascular disease;  cardiovascular risk;  cerebrovascular accident;  comparative study;  human;  machine learning;  phenotype;  predictive value;  priority journal;  Review;  risk factor;  stroke risk score;  systematic review",
    abstract = "Recent findings: Cardiovascular disease (CVD) is the leading cause of mortality and poses challenges for healthcare providers globally. Risk-based approaches for the management of CVD are becoming popular for recommending treatment plans for asymptomatic individuals. Several conventional predictive CVD risk models based do not provide an accurate CVD risk assessment for patients with different baseline risk profiles. Artificial intelligence (AI) algorithms have changed the landscape of CVD risk assessment and demonstrated a better performance when compared against conventional models, mainly due to its ability to handle the input nonlinear variations. Further, it has the flexibility to add risk factors derived from medical imaging modalities that image the morphology of the plaque. The integration of noninvasive carotid ultrasound image-based phenotypes with conventional risk factors in the AI framework has further provided stronger power for CVD risk prediction, so-called “integrated predictive CVD risk models. Purpose: of the review: The objective of this review is (i) to understand several aspects in the development of predictive CVD risk models, (ii) to explore current conventional predictive risk models and their successes and challenges, and (iii) to refine the search for predictive CVD risk models using noninvasive carotid ultrasound as an exemplar in the artificial intelligence-based framework. Conclusion: Conventional predictive CVD risk models are suboptimal and could be improved. This review examines the potential to include more noninvasive image-based phenotypes in the CVD risk assessment using powerful AI-based strategies. © 2020 Elsevier Ltd"
}