Atherosclerotic Plaque Stability Prediction from Longitudinal Ultrasound Images
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61988987%3A17110%2F25%3AA2603ATW" target="_blank" >RIV/61988987:17110/25:A2603ATW - isvavai.cz</a>
Result on the web
<a href="https://link.springer.com/10.1007/978-3-031-73284-3_13" target="_blank" >https://link.springer.com/10.1007/978-3-031-73284-3_13</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/978-3-031-73284-3_13" target="_blank" >10.1007/978-3-031-73284-3_13</a>
Alternative languages
Result language
angličtina
Original language name
Atherosclerotic Plaque Stability Prediction from Longitudinal Ultrasound Images
Original language description
We aim to predict the stability of carotid artery plaques from longitudinal ultrasound images. This is important since atherosclerosis is the primary cause of heart disease and stroke. Accurately predicting plaque stability would allow for more targeted follow-up and treatment, saving healthcare costs.We analyze data from over 400 patients followed for 3 years, exceeding the size of previous studies. We first localize the carotid artery and segment the plaque within the images. A self-supervised learning approach was used for plaque segmentation, leveraging the power of unlabeled data. The plaque stability predictor uses three image channels derived from the ultrasound image and its segmentation. As an auxiliary task, we predict the plaque width, which helps to prevent overfitting. The balance between the criteria is maintained automatically.Our estimate of the plaque width correlated well with expert measurements (p = 0.56). We confirmed that there is a relationship between the plaque ultrasound appearance in longitudinal images and their stability. However, the future width correlation and the plaque stability prediction performance remained modest (AUC = 0.61), similar to previous studies.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
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OECD FORD branch
30224 - Radiology, nuclear medicine and medical imaging
Result continuities
Project
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Continuities
S - Specificky vyzkum na vysokych skolach
Others
Publication year
2025
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
Machine Learning in Medical Imaging I
ISBN
978-3-031-73284-3
ISSN
0302-9743
e-ISSN
1611-3349
Number of pages
9
Pages from-to
124-132
Publisher name
Springer
Place of publication
Cham
Event location
Marrakesh
Event date
Oct 6, 2024
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
001424557900013