دورية أكاديمية
A Review on Joint Carotid Intima‑Media Thickness and Plaque Area Measurement in Ultrasound for Cardiovascular/Stroke Risk Monitoring: Artificial Intelligence Framework
| العنوان: | A Review on Joint Carotid Intima‑Media Thickness and Plaque Area Measurement in Ultrasound for Cardiovascular/Stroke Risk Monitoring: Artificial Intelligence Framework |
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| المؤلفون: | Biswas, Mainak, Saba, Luca, Omerzu, Toma, Johri, Amer M, N Khanna, Narendra, Viskovic, Klaudija, Mavrogeni, Sophie, R Laird, John, Pareek, Gyan, Miner, Martin, Balestrieri, Antonella, P Sfikakis, Petros P, Protogerou, Athanasios, Prasanna Misra, Durga, Agarwal, Vikas, D Kitas, George, Kolluri, Raghu, Sharma, Aditya, Viswanathan, Vijay, Ruzsa, Zoltan, Nicolaides, Andrew, S Suri, Jasjit |
| المصدر: | Journal of Digital Imaging ; Volume 34 ; Issue 3 ; ISSN 0897-1889 (Print) ; ISSN 1618-727X (Online) |
| سنة النشر: | 2021 |
| المجموعة: | Repository of the University of Rijeka |
| مصطلحات موضوعية: | Artificial intelligence, Atherosclerosis, Carotid intima-media thickness, Carotid plaque area, Carotid ultrasound, Deep learning, Machine learning, Plaque, BIOMEDICINA I ZDRAVSTVO. Kliničke medicinske znanosti. Interna medicina, BIOMEDICINE AND HEALTHCARE. Clinical Medical Sciences. Internal Medicine |
| الوصف: | 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. |
| نوع الوثيقة: | article in journal/newspaper |
| وصف الملف: | application/pdf |
| اللغة: | English |
| العلاقة: | Sveučilište u Rijeci. Fakultet zdravstvenih studija u Rijeci. Katedra za laboratorijsku i radiološku dijagnostiku.; University of Rijeka. Faculty of Health Studies. Department of Laboratory and Radiological Diagnostics.; https://www.unirepository.svkri.uniri.hr/islandora/object/fzsri:2236Test; https://urn.nsk.hr/urn:nbn:hr:184:092873Test; https://www.unirepository.svkri.uniri.hr/islandora/object/fzsri:2236/datastream/FILE0Test |
| الإتاحة: | https://doi.org/10.1007/s10278-021-00461-2Test https://www.unirepository.svkri.uniri.hr/islandora/object/fzsri:2236Test https://urn.nsk.hr/urn:nbn:hr:184:092873Test https://www.unirepository.svkri.uniri.hr/islandora/object/fzsri:2236/datastream/FILE0Test |
| حقوق: | info:eu-repo/semantics/openAccess ; http://rightsstatements.org/vocab/InC/1.0Test/ |
| رقم الانضمام: | edsbas.43C77ED3 |
| قاعدة البيانات: | BASE |
| الوصف غير متاح. |