A weakly supervised deep learning model integrating noncontrasted computed tomography images and clinical factors facilitates haemorrhagic transformation prediction after intravenous thrombolysis in acute ischaemic stroke patients

A weakly supervised deep learning model integrating noncontrasted computed tomography images and clinical factors facilitates haemorrhagic transformation prediction after intravenous thrombolysis in acute ischaemic stroke patients

Abstract Background Haemorrhage transformation (HT) is a serious complication of intravenous thrombolysis (IVT) in acute ischaemic stroke (AIS). Accurate and timely prediction of the risk of HT before IVT may change the treatment decision and improve clinical prognosis. We aimed to develop a deep le...

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Bibliographic Details
Main Authors: Xiaoshuang Ru, Shilong Zhao, Weidao Chen, Jiangfen Wu, Ruize Yu, Dawei Wang, Mengxing Dong, Qiong Wu, Daoyong Peng, Yang Song
Format: Article
Language:English
Published: BMC 2023-12-01
Series:BioMedical Engineering OnLine
Subjects:
Noncontrast computed tomography
Haemorrhagic transformation
Deep learning
Machine learning
Ischaemic stroke
Online Access:https://doi.org/10.1186/s12938-023-01193-w

Internet

https://doi.org/10.1186/s12938-023-01193-w

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