Deep learning of noncontrast CT for fast prediction of hemorrhagic transformation of acute ischemic stroke: a multicenter study

Deep learning of noncontrast CT for fast prediction of hemorrhagic transformation of acute ischemic stroke: a multicenter study

Abstract Background Hemorrhagic transformation (HT) is a complication of reperfusion therapy following acute ischemic stroke (AIS). We aimed to develop and validate a model for predicting HT and its subtypes with poor prognosis—parenchymal hemorrhage (PH), including PH-1 (hematoma within infarcted t...

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Bibliographic Details
Main Authors: Huanhuan Ren, Haojie Song, Shaoguo Cui, Hua Xiong, Bangyuan Long, Yongmei Li
Format: Article
Language:English
Published: SpringerOpen 2025-01-01
Series:European Radiology Experimental
Subjects:
Deep learning
Hemorrhage
Ischemic stroke
Thrombolytic therapy
Tomography (x-ray computed)
Online Access:https://doi.org/10.1186/s41747-024-00535-0
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