A machine learning model reveals invisible microscopic variation in acute ischaemic stroke (≤ 6 h) with non-contrast computed tomography

A machine learning model reveals invisible microscopic variation in acute ischaemic stroke (≤ 6 h) with non-contrast computed tomography

Abstract Background In most medical centers, particularly in primary hospitals, non-contrast computed tomography (NCCT) serves as the primary imaging modality for diagnosing acute ischemic stroke. However, due to the small density difference between the infarct and the surrounding normal brain tissu...

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Bibliographic Details
Main Authors: Jiahe Tan, Mengjun Xiao, Zhipeng Wang, Shuzhen Wu, Kun Han, Haiyan Wang, Yong Huang
Format: Article
Language:English
Published: BMC 2025-07-01
Series:BMC Medical Imaging
Subjects:
Artificial intelligence
Radiomics
Acute ischaemic stroke
Computed tomography
Machine learning
Online Access:https://doi.org/10.1186/s12880-025-01822-x
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https://doi.org/10.1186/s12880-025-01822-x

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