Studies in health technology and informatics
29295365
Identifying important predicative indicators for prognosis is useful since these factors help for understanding diseases and determining treatments for patients. We extracted important factors for prognosis of cerebral infarction from EHR. We analyze...
Background and Purpose- Automatic segmentation of cerebral infarction on diffusion-weighted imaging (DWI) is typically performed based on a fixed apparent diffusion coefficient (ADC) threshold. Fixed ADC threshold methods may not be accurate because ...
CT Perfusion (CTP) imaging has gained importance in the diagnosis of acute stroke. Conventional perfusion analysis performs a deconvolution of the measurements and thresholds the perfusion parameters to determine the tissue status. We pursue a data-d...
Computer methods and programs in biomedicine
31753591
BACKGROUND AND OBJECTIVE: Nowadays computer-aided disease diagnosis from medical data through deep learning methods has become a wide area of research. Existing works of analyzing clinical text data in the medical domain, which substantiate useful in...
BACKGROUND AND PURPOSE: Infarct volume is a valuable outcome measure in treatment trials of acute ischemic stroke and is strongly associated with functional outcome. Its manual volumetric assessment is, however, too demanding to be implemented in cli...
This study aims at analyzing the separability of acute cerebral infarction lesions which were invisible in CT. 38 patients, who were diagnosed with acute cerebral infarction and performed both CT and MRI, and 18 patients, who had no positive finding ...
Automatic evaluation of 3D volumes is a topic of importance in order to speed up clinical decision making. We describe a method to classify computed tomography scans on volume level for the presence of non-acute cerebral infarction. This is not a tri...
This study was to explore the application value of magnetic resonance imaging (MRI) image reconstruction model based on complex convolutional neural network (CCNN) in the diagnosis and prognosis of cerebral infarction. Two image reconstruction method...
The performance of current machine learning methods to detect heterogeneous pathology is limited by the quantity and quality of pathology in medical images. A possible solution is anomaly detection; an approach that can detect all abnormalities by le...
BACKGROUND AND PURPOSE: The ISLES challenge (Ischemic Stroke Lesion Segmentation) enables globally diverse teams to compete to develop advanced tools for stroke lesion analysis with machine learning. Detection of irreversibly damaged tissue on comput...