Target-based deep learning network surveillance of non-contrast computed tomography for small infarct core of acute ischemic stroke

Rapid diagnosis of acute ischemic stroke (AIS) is critical to achieve positive outcomes and prognosis. This study aimed to construct a model to automatically identify the infarct core based on non-contrast-enhanced CT images, especially for small infarcts. The baseline CT scans of AIS patients, who...

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Bibliographic Details
Published in:Frontiers in neurology 2024-09, Vol.15, p.1477811
Main Authors: Qu, Hang, Tang, Hui, Gao, Dong-Yang, Li, Yong-Xin, Zhao, Yi, Ban, Qi-Qi, Chen, Yu-Chen, Lu, Lu, Wang, Wei
Format: Article
Language:English
Subjects:
acute ischemic stroke
Neurology
non-contrast CT
small infarct core
target-based deep learning network
you only look once (YOLO)
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Summary:Rapid diagnosis of acute ischemic stroke (AIS) is critical to achieve positive outcomes and prognosis. This study aimed to construct a model to automatically identify the infarct core based on non-contrast-enhanced CT images, especially for small infarcts. The baseline CT scans of AIS patients, who had DWI scans obtained within less than 2 h apart, were included in this retrospective study. A modified Target-based deep learning model of YOLOv5 was developed to detect infarctions on CT. Randomly selected CT images were used for testing and evaluated by neuroradiologists and the model, using the DWI as a reference standard. Intraclass correlation coefficient (ICC) and weighted kappa were calculated to assess the agreement. The paired chi-square test was used to compare the diagnostic efficacy of physician groups and automated models in subregions.  
ISSN:1664-2295
1664-2295
DOI:10.3389/fneur.2024.1477811