prikaz prve stranice dokumenta 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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A Review on Joint Carotid Intima‑Media Thickness and Plaque Area Measurement in Ultrasound for Cardiovascular/Stroke Risk Monitoring: Artificial Intelligence Framework
Journal of Digital Imaging, 34 (2021), 3; 581-604. https://doi.org/10.1007/s10278-021-00461-2
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 Više autora...

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Biswas, M., Saba, L., Omerzu, T., Johri, A. M., N Khanna, N., Viskovic, K. ... S Suri, J. (2021). A Review on Joint Carotid Intima‑Media Thickness and Plaque Area Measurement in Ultrasound for Cardiovascular/Stroke Risk Monitoring: Artificial Intelligence Framework. Journal of Digital Imaging, 34. (3), 581-604. doi: 10.1007/s10278-021-00461-2

Biswas, Mainak, et al. "A Review on Joint Carotid Intima‑Media Thickness and Plaque Area Measurement in Ultrasound for Cardiovascular/Stroke Risk Monitoring: Artificial Intelligence Framework." Journal of Digital Imaging, vol. 34, br. 3, 2021, str. 581-604. https://doi.org/10.1007/s10278-021-00461-2

Biswas, Mainak, Luca Saba, Toma Omerzu, Amer M Johri, Narendra N Khanna, Klaudija Viskovic, Sophie Mavrogeni, et al. "A Review on Joint Carotid Intima‑Media Thickness and Plaque Area Measurement in Ultrasound for Cardiovascular/Stroke Risk Monitoring: Artificial Intelligence Framework." Journal of Digital Imaging 34, br. 3 (2021): 581-604. https://doi.org/10.1007/s10278-021-00461-2

Biswas, M., et al. (2021) 'A Review on Joint Carotid Intima‑Media Thickness and Plaque Area Measurement in Ultrasound for Cardiovascular/Stroke Risk Monitoring: Artificial Intelligence Framework', Journal of Digital Imaging, 34(3), str. 581-604. doi: 10.1007/s10278-021-00461-2

Biswas M, Saba L, Omerzu T, Johri AM, N Khanna N, Viskovic K, i sur.. A Review on Joint Carotid Intima‑Media Thickness and Plaque Area Measurement in Ultrasound for Cardiovascular/Stroke Risk Monitoring: Artificial Intelligence Framework. Journal of Digital Imaging [Internet]. 06.2021. [pristupljeno 18.04.2024.];34(3):581-604. doi: 10.1007/s10278-021-00461-2

M. Biswas, et al., "A Review on Joint Carotid Intima‑Media Thickness and Plaque Area Measurement in Ultrasound for Cardiovascular/Stroke Risk Monitoring: Artificial Intelligence Framework", Journal of Digital Imaging, vol. 34, br. 3, str. 581-604, Lipanj 2021. [Online]. Dostupno na: https://urn.nsk.hr/urn:nbn:hr:184:092873. [Citirano: 18.04.2024.]

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Naslov (engleski)A Review on Joint Carotid Intima‑Media Thickness and Plaque Area Measurement in Ultrasound for Cardiovascular/Stroke Risk Monitoring: Artificial Intelligence Framework
AutorMainak Biswas
AutorLuca Saba
AutorToma Omerzu
AutorAmer M Johri
AutorNarendra N Khanna
AutorKlaudija Viskovic
AutorSophie Mavrogeni
AutorJohn R Laird
AutorGyan Pareek
AutorMartin Miner
AutorAntonella Balestrieri
AutorPetros P P Sfikakis
AutorAthanasios Protogerou
AutorDurga Prasanna Misra
AutorVikas Agarwal
AutorGeorge D Kitas
AutorRaghu Kolluri
AutorAditya Sharma
AutorVijay Viswanathan
AutorZoltan Ruzsa
AutorAndrew Nicolaides
AutorJasjit S Suri
Autorova ustanovaSveučilište u Rijeci
Fakultet zdravstvenih studija
(Katedra za laboratorijsku i radiološku dijagnostiku)
Znanstveno / umjetničko
područje, polje i grana		BIOMEDICINA I ZDRAVSTVO
Kliničke medicinske znanosti
Interna medicina
Sažetak (engleski)
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.
Ključne riječi (engleski)
Jezikengleski
Vrsta publikacijeZnanstveni rad - Pregledni rad
Status objaveObjavljen
Vrsta recenzijeRecenziran
Verzija publikacijeObjavljena verzija rada (izdavačev PDF)
Naslov časopisaJournal of Digital Imaging
Brojčani podacivol. 34, br. 3, str. 581-604
p-ISSN0897-1889
e-ISSN1618-727X
DOIhttps://doi.org/10.1007/s10278-021-00461-2
URN:NBNurn:nbn:hr:184:092873
Datum objave publikacije2021-06
Vrsta resursaTekst
Prava pristupaOtvoreni pristup
Uvjeti korištenjaIkona licence Zaštićeno autorskim pravom.
Datum i vrijeme pohrane2023-07-07 11:43:29