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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 Magnetic Resonance Angiography-Based Study Comparing Machine Learning and Clinical Evaluation: Screening Intracranial Regions Associated with the Hemorrhagic Stroke of Adult Moyamoya Disease
Moyamoya disease (MMD) is a chronic occlusive cerebrovascular disease characterized by bilateral progressive steno-occlusive changes of unknown etiology at the distal portion of the internal carotid artery or proximal portion of the anterior arteries and middle cerebral arteries, accompanied by the presence of an abnormal vessel network (moyamoya vessels) at the base of the brain.1 The incidence and prevalence of MMD are increasing worldwide, which may indicate an increase in the number of MMD patients or an underestimation of the actual number of MMD patients in the past.
Source: Journal of Stroke and Cerebrovascular Diseases - February 17, 2022 Category: Neurology Authors: Hao-lin Yin, Yu Jiang, Wen-jun Huang, Shi-hong Li, Guang-wu Lin Source Type: research

Improving the Accuracy of Scores to Predict Gastrostomy after Intracerebral Hemorrhage with Machine Learning
Gastrostomy placement after intracerebral hemorrhage indicates the need for continued medical care and predicts patient dependence. Our objective was to determine the optimal machine learning technique to predict gastrostomy.
Source: Journal of Stroke and Cerebrovascular Diseases - September 7, 2018 Category: Neurology Authors: Ravi Garg, Shyam Prabhakaran, Jane L. Holl, Yuan Luo, Roland Faigle, Konrad Kording, Andrew M. Naidech Source Type: research

Deep Learning-Based Approach for the Diagnosis of Moyamoya Disease
Moyamoya disease is a unique cerebrovascular disorder that is characterized by chronic progressive bilateral stenosis of the terminal portion of the internal carotid arteries (ICAs), and it is associated with the formation of an abnormal vascular network at the base of the brain.1,2 For the diagnosis of the moyamoya disease, digital subtraction angiography (DSA), which helps evaluate collateral circulation from the view point of the hemorrhagic risk, is the gold standard.3,4 On the contrary, magnetic resonance imaging (MRI) and magnetic resonance angiography (MRA) can be used as alternatives to conventional angiography bec...
Source: Journal of Stroke and Cerebrovascular Diseases - September 25, 2020 Category: Neurology Authors: Yukinori Akiyama, Takeshi Mikami, Nobuhiro Mikuni Source Type: research

Machine Learning Models Prognosticate Functional Outcomes Better than Clinical Scores in Spontaneous Intracerebral Haemorrhage
Spontaneous intracerebral haemorrhage (SICH) was associated with an overall incidence of 24.6 per 100,000 person-years worldwide and was most common in Asians, with an incidence of 51.8 per 100,000 person-years.1 SICH is a major cause of mortality and morbidity, with a case fatality of 40.4% at one month, one-year mortality rate of 54.0% and only 12 to 39% of patients leading an independent life on follow up.1,2 Current management for SICH remains supportive, and includes outcome prediction and rehabilitation.
Source: Journal of Stroke and Cerebrovascular Diseases - December 10, 2021 Category: Neurology Authors: Mervyn Jun Rui Lim, Raphael Hao Chong Quek, Kai Jie Ng, Ne-Hooi Will Loh, Sein Lwin, Kejia Teo, Vincent Diong Weng Nga, Tseng Tsai Yeo, Mehul Motani Source Type: research

Machine Learning-Based Perihematomal Tissue Features to Predict Clinical Outcome after Spontaneous Intracerebral Hemorrhage
To explore whether radiomic features of perihematomal tissue can improve the forecasting accuracy for the prognosis of patients with an intracerebral hemorrhage (ICH).
Source: Journal of Stroke and Cerebrovascular Diseases - April 11, 2022 Category: Neurology Authors: Xin Qi, Guorui Hu, Haiyan Sun, Zhigeng Chen, Chao Yang Source Type: research