Assessing Machine Learning Models for Predicting Age with Intracranial Vessel Tortuosity and Thickness Information

This study aimed to develop and validate machine learning (ML) models that predict age using intracranial vessels’ tortuosity and diameter features derived from magnetic resonance angiography (MRA) data. A total of 171 subjects’ three-dimensional (3D) time-of-flight MRA image data were considered fo...

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Detalles Bibliográficos
Autores principales: Yoon, Hoon-Seok, Oh, Jeongmin, Kim, Yoon-Chul
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10669197/
https://www.ncbi.nlm.nih.gov/pubmed/38002472
http://dx.doi.org/10.3390/brainsci13111512

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10669197/
https://www.ncbi.nlm.nih.gov/pubmed/38002472
http://dx.doi.org/10.3390/brainsci13111512

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