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Development of Machine Learning Models to Predict Probabilities and Types of Stroke at Prehospital Stage: the Japan Urgent Stroke Triage Score Using Machine Learning (JUST-ML)
AbstractIn conjunction with recent advancements in machine learning (ML), such technologies have been applied in various fields owing to their high predictive performance. We tried to develop prehospital stroke scale with ML. We conducted multi-center retrospective and prospective cohort study. The training cohort had eight centers in Japan from June 2015 to March 2018, and the test cohort had 13 centers from April 2019 to March 2020. We use the three different ML algorithms (logistic regression, random forests, XGBoost) to develop models. Main outcomes were large vessel occlusion (LVO), intracranial hemorrhage (ICH), suba...
Source: Translational Stroke Research - August 14, 2021 Category: Neurology Source Type: research

Imaging-Based Outcome Prediction of Acute Intracerebral Hemorrhage
AbstractWe hypothesized that imaging-only-based machine learning algorithms can analyze non-enhanced CT scans of patients with acute intracerebral hemorrhage (ICH). This retrospective multicenter cohort study analyzed 520 non-enhanced CT scans and clinical data of patients with acute spontaneous ICH. Clinical outcome at hospital discharge was dichotomized into good outcome and poor outcome using different modified Rankin Scale (mRS) cut-off values. Predictive performance of a random forest machine learning approach based on filter- and texture-derived high-end image features was evaluated for differentiation of functional ...
Source: Translational Stroke Research - February 6, 2021 Category: Neurology Source Type: research

Ifenprodil Improves Long-Term Neurologic Deficits Through Antagonizing Glutamate-Induced Excitotoxicity After Experimental Subarachnoid Hemorrhage
AbstractExcessive glutamate leading to excitotoxicity worsens brain damage after SAH and contributes to long-term neurological deficits. The drug ifenprodil is a non-competitive antagonist of GluN1-GluN2B N-methyl-d-aspartate (NMDA) receptor, which mediates excitotoxic damage in vitro and in vivo. Here, we show that cerebrospinal fluid (CSF) glutamate level within 48  h was significantly elevated in aSAH patients who later developed poor outcome. In rat SAH model, ifenprodil can improve long-term sensorimotor and spatial learning deficits. Ifenprodil attenuates experimental SAH-induced neuronal death of basal cortex and h...
Source: Translational Stroke Research - March 12, 2021 Category: Neurology Source Type: research

Machine Learning-Enabled Determination of Diffuseness of Brain Arteriovenous Malformations from Magnetic Resonance Angiography
AbstractThe diffuseness of brain arteriovenous malformations (bAVMs) is a significant factor in surgical outcome evaluation and hemorrhagic risk prediction. However, there are still predicaments in identifying diffuseness, such as the judging variety resulting from different experience and difficulties in quantification. The purpose of this study was to develop a machine learning (ML) model to automatically identify the diffuseness of bAVM niduses using three-dimensional (3D) time-of-flight magnetic resonance angiography (TOF-MRA) images. A total of 635 patients with bAVMs who underwent TOF-MRA imaging were enrolled. Three...
Source: Translational Stroke Research - August 12, 2021 Category: Neurology Source Type: research

Subarachnoid Hemorrhage Induces Sub-acute and Early Chronic Impairment in Learning and Memory in Mice
AbstractSubarachnoid hemorrhage (SAH) leads to significant long-term cognitive deficits, so-called the post-SAH syndrome. Existing neurological scales used to assess outcomes of SAH are focused on sensory-motor functions. To better evaluate short-term and chronic consequences of SAH, we explored and validated a battery of neurobehavioral tests to gauge the functional outcomes in mice after the circle of Willis perforation-induced SAH. The 18-point Garcia scale, applied up to 4  days, detected impairment only at 24-h time point and showed no significant difference between the Sham and SAH group. A decrease in locomotion wa...
Source: Translational Stroke Research - March 8, 2022 Category: Neurology Source Type: research

Alpha-Asarone Ameliorates Neurological Dysfunction of Subarachnoid Hemorrhagic Rats in Both Acute and Recovery Phases via Regulating the CaMKII-Dependent Pathways
AbstractEarly brain injury (EBI) is the leading cause of poor prognosis for patients suffering from subarachnoid hemorrhage (SAH), particularly learning and memory deficits in the repair phase. A recent report has involved calcium/calmodulin-dependent protein kinase II (CaMKII) in the pathophysiological process underlying SAH-induced EBI. Alpha-asarone (ASA), a major compound isolated from the Chinese medicinal herbAcorus tatarinowii Schott, was proven to reduce secondary brain injury by decreasing CaMKII over-phosphorylation in rats ’ model of intracerebral hemorrhage in our previous report. However, the effect of ASA o...
Source: Translational Stroke Research - February 13, 2023 Category: Neurology Source Type: research

CT Angiography Radiomics Combining Traditional Risk Factors to Predict Brain Arteriovenous Malformation Rupture: a Machine Learning, Multicenter Study
This study aimed to develop a machine learning model for predicting brain arteriovenous malformation (bAVM) rupture using a combination of traditional risk factors and radiomics features. This multicenter retrospective study enrolled 586 patients with unruptured bAVMs from 2010 to 2020. All patients were grouped into the hemorrhage (n = 368) and non-hemorrhage (n = 218) groups. The bAVM nidus were segmented on CT angiography images using Slicer software, and radiomic features were extracted using Pyradiomics. The dataset included a training set and an independent testing set. The machine learning model was developed on the...
Source: Translational Stroke Research - June 13, 2023 Category: Neurology Source Type: research