@article{2984875,
    title = "A Review on Joint Carotid Intima-Media Thickness and Plaque Area Measurement in Ultrasound for Cardiovascular/Stroke Risk Monitoring: Artificial Intelligence Framework",
    author = "Biswas, M. and Saba, L. and Omerzu, T. and Johri, A.M. and Khanna, N.N. and Viskovic, K. and Mavrogeni, S. and Laird, J.R. and Pareek, G. and Miner, M. and Balestrieri, A. and Sfikakis, P.P. and Protogerou, A. and Misra, D.P. and Agarwal, V. and Kitas, G.D. and Kolluri, R. and Sharma, A. and Viswanathan, V. and Ruzsa, Z. and Nicolaides, A. and Suri, J.S.",
    journal = "Journal of Digital Imaging",
    year = "2021",
    volume = "34",
    number = "3",
    pages = "581-604",
    publisher = "Springer Science and Business Media Deutschland GmbH",
    issn = "0897-1889, 1618-727X",
    doi = "10.1007/s10278-021-00461-2",
    keywords = "Automation;  Cardiology;  Deep learning;  Diseases;  Image segmentation;  Pathology;  Ultrasonic applications, Automated techniques;  Cardio-vascular disease;  Carotid intima-media thickness;  Carotid ultrasounds;  Interface detection;  Mathematical representations;  Measurement methods;  Myocardial Infarction, Risk assessment, arterial wall thickness;  artificial intelligence;  carotid artery;  cerebrovascular accident;  diagnostic imaging;  echography;  human, Artificial Intelligence;  Carotid Arteries;  Carotid Intima-Media Thickness;  Humans;  Stroke;  Ultrasonography",
    abstract = "Cardiovascular diseases (CVDs) are the top ten leading causes of death worldwide. Atherosclerosis disease in the arteries is the main cause of the CVD, leading to myocardial infarction and stroke. The two primary image-based phenotypes used for monitoring the atherosclerosis burden is carotid intima-media thickness (cIMT) and plaque area (PA). Earlier segmentation and measurement methods were based on ad hoc conventional and semi-automated digital imaging solutions, which are unreliable, tedious, slow, and not robust. This study reviews the modern and automated methods such as artificial intelligence (AI)-based. Machine learning (ML) and deep learning (DL) can provide automated techniques in the detection and measurement of cIMT and PA from carotid vascular images. Both ML and DL techniques are examples of supervised learning, i.e., learn from “ground truth” images and transformation of test images that are not part of the training. This review summarizes (1) the evolution and impact of the fast-changing AI technology on cIMT/PA measurement, (2) the mathematical representations of ML/DL methods, and (3) segmentation approaches for cIMT/PA regions in carotid scans based for (a) region-of-interest detection and (b) lumen-intima and media-adventitia interface detection using ML/DL frameworks. AI-based methods for cIMT/PA segmentation have emerged for CVD/stroke risk monitoring and may expand to the recommended parameters for atherosclerosis assessment by carotid ultrasound. © 2021, Society for Imaging Informatics in Medicine."
}