A deep-learning method for the end-to-end prediction of intracranial aneurysm rupture risk

It is critical to accurately predict the rupture risk of an intracranial aneurysm (IA) for timely and appropriate treatment because the fatality rate after rupture is [Formula: see text]. Existing methods relying on morphological features (e.g., height-width ratio) measured manually by neuroradiolog...

Descripción completa

Detalles Bibliográficos
Autores principales: Li, Peiying, Liu, Yongchang, Zhou, Jiafeng, Tu, Shikui, Zhao, Bing, Wan, Jieqing, Yang, Yunjun, Xu, Lei
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2023
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10140611/
https://www.ncbi.nlm.nih.gov/pubmed/37123440
http://dx.doi.org/10.1016/j.patter.2023.100709

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10140611/
https://www.ncbi.nlm.nih.gov/pubmed/37123440
http://dx.doi.org/10.1016/j.patter.2023.100709

Ejemplares similares