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...
Elmentve itt :
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| Dokumentumtípus: | Cikk |
| Megjelent: |
2021
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| Sorozat: | JOURNAL OF DIGITAL IMAGING
34 No. 3 |
| Tárgyszavak: | |
| doi: | 10.1007/s10278-021-00461-2 |
| mtmt: | 32383470 |
| Online Access: | http://publicatio.bibl.u-szeged.hu/23387 |
| LEADER | 02833nab a2200385 i 4500 | ||
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| 040 | |a SZTE Publicatio Repozitórium |b hun | ||
| 041 | |a Angol | ||
| 100 | 1 | |a Biswas Mainak | |
| 245 | 1 | 2 | |a A Review on Joint Carotid Intima-Media Thickness and Plaque Area Measurement in Ultrasound for Cardiovascular/Stroke Risk Monitoring |h [elektronikus dokumentum] : |b Artificial Intelligence Framework / |c Biswas Mainak |
| 260 | |c 2021 | ||
| 300 | |a 581-604 | ||
| 490 | 0 | |a JOURNAL OF DIGITAL IMAGING |v 34 No. 3 | |
| 520 | 3 | |a 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. | |
| 650 | 4 | |a Klinikai orvostan | |
| 700 | 0 | 1 | |a Saba Luca |e aut |
| 700 | 0 | 1 | |a Omerzu Tomaz |e aut |
| 700 | 0 | 1 | |a Johri Amer M. |e aut |
| 700 | 0 | 1 | |a Khanna Narendra N. |e aut |
| 700 | 0 | 1 | |a Viskovic Klaudija |e aut |
| 700 | 0 | 1 | |a Mavrogeni Sophie |e aut |
| 700 | 0 | 1 | |a Laird John R. |e aut |
| 700 | 0 | 1 | |a Pareek Gyan |e aut |
| 700 | 0 | 1 | |a Miner Martin |e aut |
| 700 | 0 | 1 | |a Balestrieri Antonella |e aut |
| 700 | 0 | 1 | |a Sfikakis Petros P. |e aut |
| 700 | 0 | 1 | |a Protogerou Athanasios |e aut |
| 700 | 0 | 1 | |a Misra Durga Prasanna |e aut |
| 700 | 0 | 1 | |a Ruzsa Zoltán |e aut |
| 856 | 4 | 0 | |u http://publicatio.bibl.u-szeged.hu/23387/1/Biswas2021.pdf |z Dokumentum-elérés |