Cargando…
Deep Shape Features for Predicting Future Intracranial Aneurysm Growth
Introduction: Intracranial aneurysms (IAs) are a common vascular pathology and are associated with a risk of rupture, which is often fatal. Aneurysm growth is considered a surrogate of rupture risk; therefore, the study aimed to develop and evaluate prediction models of future artificial intelligenc...
Autores principales: | Bizjak, Žiga, Pernuš, Franjo, Špiclin, Žiga |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2021
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8281925/ https://www.ncbi.nlm.nih.gov/pubmed/34276391 http://dx.doi.org/10.3389/fphys.2021.644349 |
Ejemplares similares
-
A Systematic Review of Deep-Learning Methods for Intracranial Aneurysm Detection in CT Angiography
por: Bizjak, Žiga, et al.
Publicado: (2023) -
Extensive T1-weighted MRI Preprocessing Improves Generalizability of Deep Brain Age Prediction Models
por: Dular, Lara, et al.
Publicado: (2023) -
Assessment of shape-based features ability to predict the ascending aortic aneurysm growth
por: Geronzi, Leonardo, et al.
Publicado: (2023) -
Detection and Localization of Hyperfunctioning Parathyroid Glands on [(18)F]fluorocholine PET/ CT Using Deep Learning – Model Performance and Comparison to Human Experts
por: Jarabek, Leon, et al.
Publicado: (2022) -
Deep learning-based recognition and segmentation of intracranial aneurysms under small sample size
por: Zhu, Guangyu, et al.
Publicado: (2022)