In recent years, there have been major advances in deep learning algorithms for image
recognition in traumatic brain injury (TBI). Interest in this area has increased due to the …

Objective To devise and validate radiomic signatures of impending hematoma expansion
(HE) based on admission non-contrast head computed tomography (CT) of patients with …

Introduction Cerebrovascular diseases are known to cause significant morbidity and
mortality to the general population. In patients with cerebrovascular disease, prompt clinical …

Background Accurate measurement of hemorrhage volume is critical for both the prediction
of prognosis and the selection of appropriate clinical treatment after spontaneous …

This study evaluated deep learning algorithms for semantic segmentation and quantification
of intracerebral hemorrhage (ICH), perihematomal edema (PHE), and intraventricular …

Deep Learning (DL) algorithm holds great potential in the field of stroke imaging. It has been
applied not only to the “downstream” side such as lesion detection, treatment decision …

Hemorrhagic stroke poses a critical medical emergency that necessitates prompt and
accurate diagnosis to prevent irreversible brain damage. The emergence of automated deep …

Heterogeneity is recognized as a major barrier in efforts to improve the care and outcomes
of patients with traumatic brain injury (TBI). Even within the narrower stratum of moderate …

Objectives We aimed to predict hematoma expansion in intracerebral hemorrhage (ICH)
patients by using the deep learning technique. Methods We retrospectively collected data …

Intracerebral hemorrhage is the subtype of stroke with the highest mortality rate, especially
when it also causes secondary intraventricular hemorrhage. The optimal surgical option for …