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Condition: Hemorrhagic Stroke
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Total 196 results found since Jan 2013.

Machine learning prediction of symptomatic intracerebral hemorrhage after stroke thrombolysis: a cross-cultural validation in Caucasian and Han Chinese cohort
CONCLUSION: The established SVM model is feasible for predicting the risk of sICH after thrombolysis quickly and efficiently in both Caucasian and Han Chinese cohort.PMID:36225969 | PMC:PMC9549180 | DOI:10.1177/17562864221129380
Source: Adv Data - October 13, 2022 Category: Epidemiology Authors: Junfeng Liu Xinyue Chen Xiaonan Guo Renjie Xu Yanan Wang Ming Liu Source Type: research

Design and implementation of a Stroke Rehabilitation Registry for the systematic assessment of processes and outcomes and the development of data-driven prediction models: The STRATEGY study protocol
ConclusionsThis study will test the feasibility of a stroke rehabilitation registry in the Italian health context and provide a systematic assessment of processes and outcomes for quality assessment and benchmarking. By the development of data-driven prediction models in stroke rehabilitation, this study will pave the way for the development of decision support tools for patient-oriented therapy planning and rehabilitation outcomes maximization.Clinical tial registrationThe registration on ClinicalTrials.gov is ongoing and under review. The identification number will be provided when the review process will be completed.
Source: Frontiers in Neurology - October 10, 2022 Category: Neurology Source Type: research

Prediction of bleb formation in intracranial aneurysms using machine learning models based on aneurysm hemodynamics, geometry, location, and patient population
Conclusions Based on the premise that aneurysm characteristics prior to bleb formation resemble those derived from vascular reconstructions with their blebs virtually removed, machine learning models can identify aneurysms prone to bleb development with good accuracy. Pending further validation with longitudinal data, these models may prove valuable for assessing the propensity of IAs to progress to vulnerable states and potentially rupturing.
Source: Journal of NeuroInterventional Surgery - September 14, 2022 Category: Neurosurgery Authors: Salimi Ashkezari, S. F., Mut, F., Slawski, M., Cheng, B., Yu, A. K., White, T. G., Woo, H. H., Koch, M. J., Amin-Hanjani, S., Charbel, F. T., Rezai Jahromi, B., Niemelä, M., Koivisto, T., Frosen, J., Tobe, Y., Maiti, S., Robertson, A. M., Cebral, Tags: Hemorrhagic stroke Source Type: research

Ischemic and haemorrhagic stroke risk estimation using a machine-learning-based retinal image analysis
ConclusionA fast and fully automatic method can be used for stroke subtype risk assessment and classification based on fundus photographs alone.
Source: Frontiers in Neurology - August 22, 2022 Category: Neurology Source Type: research

Unsupervised Deep Learning for Stroke Lesion Segmentation on Follow-up CT Based on Generative Adversarial Networks FUNCTIONAL
CONCLUSIONS: An unsupervised generative adversarial network can be used to obtain automated infarct lesion segmentations with a moderate Dice similarity coefficient and good volumetric correspondence.
Source: American Journal of Neuroradiology - August 8, 2022 Category: Radiology Authors: van Voorst, H., Konduri, P. R., van Poppel, L. M., van der Steen, W., van der Sluijs, P. M., Slot, E. M. H., Emmer, B. J., van Zwam, W. H., Roos, Y. B. W. E. M., Majoie, C. B. L. M., Zaharchuk, G., Caan, M. W. A., Marquering, H. A., on behalf of the CONTR Tags: FUNCTIONAL Source Type: research

Using convolutional neural network to analyze brain MRI images for predicting functional outcomes of stroke
AbstractNowadays, the physicians usually predict functional outcomes of stroke based on clinical experiences and big data, so we wish to develop a model to accurately identify imaging features for predicting functional outcomes of stroke patients. Using magnetic resonance imaging of ischemic and hemorrhagic stroke patients, we developed and trained a VGG-16 convolutional neural network (CNN) to predict functional outcomes after 28-day hospitalization. A total of 44 individuals (24 men and 20 women) were recruited from Taoyuan General Hospital and China Medical University Hsinchu Hospital to enroll in the study. Based on â€...
Source: Medical and Biological Engineering and Computing - August 2, 2022 Category: Biomedical Engineering Source Type: research

Machine learning based reanalysis of clinical scores for distinguishing between ischemic and hemorrhagic stroke in low resource setting
Identifying ischemic or hemorrhagic strokes clinically may help in situations where neuroimaging is unavailable to provide primary-care prior to referring to stroke-ready facility. Stroke classification-based solely on clinical scores faces two unresolved issues. One pertains to overestimation of score performance, while other is biased performance due to class-imbalance inherent in stroke datasets. After correcting the issues using Machine Learning theory, we quantitatively compared existing scores to study the capabilities of clinical attributes for stroke classification.
Source: Journal of Stroke and Cerebrovascular Diseases - August 1, 2022 Category: Neurology Authors: Aman Bhardwaj, MV Padma Srivastava, Pulikottil Vinny Wilson, Amit Mehndiratta, Venugopalan Y Vishnu, Rahul Garg Source Type: research

A deep learning based automatic system for intracranial aneurysms diagnosis on Three-Dimensional digital subtraction angiographic images
CONCLUSIONS: We have demonstrated that a comprehensive system can automatically detect, measure morphology and report aneurysm location of aneurysms without human intervention. This can be a potential tool for the diagnosis of intracranial aneurysms, improving radiologists' performance and reducing their workload. This article is protected by copyright. All rights reserved.PMID:35792717 | DOI:10.1002/mp.15846
Source: Health Physics - July 6, 2022 Category: Physics Authors: Chubin Ou Yi Qian Winston Chong Xiaoxi Hou Mingzi Zhang Xin Zhang Weixin Si Chuan-Zhi Duan Source Type: research

Machine Learning-Based Model for Prediction of Hemorrhage Transformation in Acute Ischemic Stroke After Alteplase
In this study, an RF machine learning method was successfully established to predict HT in AIS patients after intravenous alteplase, which the sensitivity was 66.7%, and the specificity was 80.7%.
Source: Frontiers in Neurology - June 10, 2022 Category: Neurology Source Type: research

Pilot Report for Intracranial Hemorrhage Detection with Deep Learning Implanted Head Computed Tomography Images at Emergency Department
AbstractHemorrhagic stroke is a serious clinical condition that requires timely diagnosis. An artificial intelligence algorithm system called DeepCT can identify hemorrhagic lesions rapidly from non-contrast head computed tomography (NCCT) images and has received regulatory clearance.  A non-controlled retrospective pilot clinical trial was conducted. Patients who received NCCT at the emergency department (ED) of Kaohsiung Veteran General Hospital were collected. From 2020 January-1st to April-30th, the physicians read NCCT images without DeepCT. From 2020May-1st to August-31st, the physicians were assisted by DeepCT. The...
Source: Journal of Medical Systems - June 8, 2022 Category: Information Technology Source Type: research

Pregnancy-Related Stroke: A Review
Conclusions and Relevance Early recognition and management are integral in decreasing the morbidity and mortality associated with a stroke in pregnancy. Relevance Statement This study was an evidence-based review of stroke in pregnancy and how to diagnose and mange a pregnancy complicated by a stroke. Target Audience Obstetricians and gynecologist, family physicians Learning Objectives After completing this learning activity, the participant should be better able to identify the pregnancy-related risk factors for a stroke; explain the presenting signs and symptoms of a stroke in pregnancy; describe...
Source: Obstetrical and Gynecological Survey - June 1, 2022 Category: OBGYN Tags: CME ARTICLES Source Type: research

Natural Language Processing of Radiology Reports to Detect Complications of Ischemic Stroke
ConclusionsOur study demonstrates robust performance and external validity of a core NLP tool kit for identifying both categorical and continuous outcomes of ischemic stroke from unstructured radiographic text data. Medically tailored NLP methods have multiple important big data applications, including scalable electronic phenotyping, augmentation of clinical risk prediction models, and facilitation of automatic alert systems in the hospital setting.
Source: Neurocritical Care - May 9, 2022 Category: Neurology Source Type: research

Deep Transfer Learning for Automatic Prediction of Hemorrhagic Stroke on CT Images
In this study, we propose an automated transfer deep learning method that combines ResNet-50 and dense layer for accurate prediction of intracranial hemorrhage on NCCT brain images. A total of 1164 NCCT brain images were collected from 62 patients with hemorrhagic stroke from Kalinga Institute of Medical Science, Bhubaneswar and used for evaluating the model. The proposed model takes individual CT images as input and classifies them as hemorrhagic or normal. This deep transfer learning approach reached 99.6% accuracy, 99.7% specificity, and 99.4% sensitivity which are better results than that of ResNet-50 only. It is evide...
Source: Computational and Mathematical Methods in Medicine - April 26, 2022 Category: Statistics Authors: B Nageswara Rao Sudhansu Mohanty Kamal Sen U Rajendra Acharya Kang Hao Cheong Sukanta Sabut Source Type: research