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Machine Learning Approaches to Surrogate Multifidelity Growth and Remodeling Models for Efficient Abdominal Aortic Aneurysmal Applications

Computational Growth and Remodeling (G&R) models have been widely used to capture the pathological development of arterial diseases and have shown promise for aiding clinical diagnosis, prognosis prediction, and staging classification. However, due to the high complexity of the arterial adaptati...

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Bibliografiske detaljer
Udgivet i:Comput Biol Med
Main Authors: Jiang, Zhenxiang, Choi, Jongeun, Baek, Seungik
Format: Artigo
Sprog:Inglês
Udgivet: 2021
Fag:
Online adgang:https://ncbi.nlm.nih.gov/pmc/articles/PMC8169625/
https://ncbi.nlm.nih.gov/pubmed/34015599
https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1016/j.compbiomed.2021.104394
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