Evaluating the value of machine learning models for predicting hematoma expansion in acute spontaneous intracerebral hemorrhage based on CT imaging features of hematomas and surrounding oedema

Evaluating the value of machine learning models for predicting hematoma expansion in acute spontaneous intracerebral hemorrhage based on CT imaging features of hematomas and surrounding oedema

ObjectiveThis study evaluates the utility of artificial intelligence (AI) for automated segmentation of intracranial hematomas and surrounding oedema in non-contrast computed tomography (CT) images. Additionally, it aims to extract imaging features for developing machine learning models to predict h...

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Bibliographic Details
Main Authors: Tianyu Yang, Zhen Zhao, Yan Gu, Shengkai Yang, Yonggang Zhang, Lei Li, Ting Wang, Zhongchang Miao
Format: Article
Language:English
Published: Frontiers Media S.A. 2025-06-01
Series:Frontiers in Neurology
Subjects:
spontaneous intracerebral hemorrhage
hematoma expansion
imaging features
machine learning models
computed tomography scan
Online Access:https://www.frontiersin.org/articles/10.3389/fneur.2025.1567525/full
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https://www.frontiersin.org/articles/10.3389/fneur.2025.1567525/full

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