A Review on Joint Carotid Intima-Media Thickness and Plaque Area Measurement in Ultrasound for Cardiovascular/Stroke Risk Monitoring: Artificial Intelligence Framework.
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...
| Publicado en: | Journal of Digital Imaging Vol. 34; no. 3; pp. 581 - 605 |
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| Autores principales: | , , , , , , , , , , , , , , , , , , , |
| Formato: | diagnostic images equations & formulas pictorial review tables/charts Journal Article |
| Publicado: |
Springer Nature
Jun2021
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=151702170&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 151702170 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 08971889 DOQ jtl: Journal of Digital Imaging issn: 08971889 maglogo: N pubinfo: dt: Jun2021 vid: 34 iid: 3 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 151702170 150631435 151702170 151702170 10.1007/s10278-021-00461-2 151702170 ppf: 581 ppct: 24 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: A Review on Joint Carotid Intima-Media Thickness and Plaque Area Measurement in Ultrasound for Cardiovascular/Stroke Risk Monitoring: Artificial Intelligence Framework. aug: au: Biswas, Mainak Saba, Luca Omerzu, Tomaž Johri, Amer M. Khanna, Narendra N. Viskovic, Klaudija Mavrogeni, Sophie Laird, John R. Pareek, Gyan Miner, Martin Balestrieri, Antonella Sfikakis, Petros P Protogerou, Athanasios Misra, Durga Prasanna Agarwal, Vikas Kitas, George D Kolluri, Raghu Sharma, Aditya Viswanathan, Vijay Ruzsa, Zoltan affil: JIS University Kolkata, Kolkata, West Bengal, India sug: subj: Carotid Intima-Media Thickness Evaluation Cardiovascular Risk Factors Stroke Risk Factors Atherosclerosis Ultrasonography Artificial Intelligence Monitoring, Physiologic Machine Learning Deep Learning Mathematics Models, Statistical ab: 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. pubtype: Academic Journal doctype: diagnostic images equations & formulas pictorial review tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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