Publications by authors named "Ye Zeju"
Purpose: The prognosis following a hemorrhagic stroke is usually extremely poor. Rating scales have been developed to predict the outcomes of patients with intracerebral hemorrhage (ICH). To date, however, the prognostic prediction models have not included the full range of relevant imaging features.
View Article and Find Full Text PDFObjectives: Preventing the expansion of perihematomal edema (PHE) represents a novel strategy for the improvement of neurological outcomes in intracerebral hemorrhage (ICH) patients. Our goal was to predict early and delayed PHE expansion using a machine learning approach.
Methods: We enrolled 550 patients with spontaneous ICH to study early PHE expansion, and 389 patients to study delayed expansion.
Objectives: The use of hematoma expansion (HE) in intracerebral hemorrhage (ICH) patients is limited due to its low sensitivity. Perihematomal edema (PHE) has been considered an important marker of secondary brain injury after ICH. Enrolling PHE expansion to redefine traditional ICH expansion merits exploration.
View Article and Find Full Text PDFWe attempt to generate a definition of delayed perihematomal edema expansion (DPE) and analyze its time course, risk factors, and clinical outcomes. A multi-cohort data was derived from the Chinese Intracranial Hemorrhage Image Database (CICHID). A non-contrast computed tomography (NCCT) -based deep learning model was constructed for fully automated segmentation hematoma and perihematomal edema (PHE).
View Article and Find Full Text PDFHemorrhage expansion (HE) is a common and serious condition in patients with intracerebral hemorrhage (ICH). In contrast to the volume changes, little is known about the morphological changes that occur during HE. We developed a novel method to explore the patterns of morphological change and investigate the clinical significance of this change in ICH patients.
View Article and Find Full Text PDFArticle Synopsis
- About one-third of patients with spontaneous intracerebral hemorrhage don't know when their symptoms began, which limits research on time-sensitive treatments for these cases.
- The study introduces an artificial intelligence model that uses weakly supervised multitask learning (WS-MTL) to estimate the onset time of symptoms from non-contrast CT scans, aiming to assist clinicians in treating unclear-onset patients.
- With a dataset of 4004 patients and 10,780 CT scans, the WS-MTL model demonstrated high accuracy in classifications and satisfactory results in estimating onset time, suggesting it could be integrated into clinical practice for better treatment outcomes.