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Machine learning-based identification of symptomatic carotid atherosclerotic plaques with dual-energy computed tomography angiography
This study aimed to develop and validate a machine learning model incorporating both dual-energy computed tomography (DECT) angiography quantitative parameters and clinically relevant risk factors for the identification of symptomatic carotid plaques to prevent acute cerebrovascular events.
Source: Journal of Stroke and Cerebrovascular Diseases - June 7, 2023 Category: Neurology Authors: Ling-Jie Wang, Pei-Qing Zhai, Li-Li Xue, Cai-Yun Shi, Qian Zhang, Hua Zhang Source Type: research

Machine learning prediction of malignant middle cerebral artery infarction after mechanical thrombectomy for anterior circulation large vessel occlusion
Prediction of malignant middle cerebral artery infarction (MMI) could identify patients for early intervention. We trained and internally validated a ML model that predicts MMI following mechanical thrombectomy (MT) for ACLVO.
Source: Journal of Stroke and Cerebrovascular Diseases - January 16, 2023 Category: Neurology Authors: Haydn Hoffman, Jacob S. Wood, John R. Cote, Muhammad S. Jalal, Hesham E. Masoud, Grahame C. Gould Source Type: research

The mucormycosis and Stroke: the learning curve during the second COVID-19 pandemic
Background The Angio-invasive Rhino-orbito-cerebral mucormycosis (ROCM) producing strokes is a less explored entity. Our hospital, a stroke-ready one, had an opportunity to manage mucormycosis when it was identified as the nodal center for mucormycosis management. We are sharing our experiences and mistakes in managing the cerebrovascular manifestations of ROCM.Methods We conducted a prospective observational study during the second wave of the COVID-19 pandemic from 1st May 2021 to 30th September 2021, where consecutive patients aged more than 18 years with microbiologically confirmed cases of ROCM were included.
Source: Journal of Stroke and Cerebrovascular Diseases - October 12, 2022 Category: Neurology Authors: Dileep Ramachandran, Aravind R, Praveen Panicker, Jayaprabha S, MC Sathyabhama, Abhilash Nair, Raj S. Chandran, Simon George, Chintha S, Thomas Iype Source Type: research

End-to-end artificial intelligence platform for the management of large vessel occlusions: A preliminary study
In this study, we developed a deep learning pipeline that detects large vessel occlusion (LVO) and predicts functional outcome based on computed tomography angiography (CTA) images to improve the management of the LVO patients.
Source: Journal of Stroke and Cerebrovascular Diseases - September 15, 2022 Category: Neurology Authors: Shujuan Meng, Thi My Linh Tran, Mingzhe Hu, PanPan Wang, Thomas Yi, Zhusi Zhong, Luoyun Wang, Braden Vogt, Zhicheng Jiao, Arko Barman, Ugur Cetintemel, Ken Chang, Dat-Thanh Nguyen, Ferdinand K. Hui, Ian Pan, Bo Xiao, Li Yang, Hao Zhou, Harrison X. Bai Source Type: research

Image level detection of large vessel occlusion on 4D-CTA perfusion data using deep learning in acute stroke
Acute ischemic stroke (AIS) secondary to LVOs represent approximately 30-40% of all stroke cases and are associated with disproportionately higher morbidity and mortality.1, 2 The importance of endovascular thrombectomy (EVT) in patients with acute ischemic stroke (AIS) has been well established in multiple randomized controlled trials for patients in both early and late stroke windows.3-6 A critical aspect of the patient triage is the accurate and timely detection of underlying large vessel occlusion (LVO).
Source: Journal of Stroke and Cerebrovascular Diseases - September 10, 2022 Category: Neurology Authors: Girish Bathla, Dhruba Durjoy, Sarv Priya, Edgar Samaniego, Colin P. Derdeyn 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

Can We Learn from Our Children About stroke? Effectiveness of a School-Based Educational Programme in Greece
Stroke is the second most common cause of death worldwide and the leading cause of chronic functional limitations.1 Without appropriate and timely care between the stroke symptom appearance and the treatment in acute stroke incidents, the ischemic brain ages 3.6 years for every hour of blood deprivation.2 Yet patients repeatedly arrive late to the hospital.3,4 The lack of public awareness about stroke symptoms has been reported in the literature as one of the main factors, causing this belated arrival to the hospital and hence the belated medical treatment.
Source: Journal of Stroke and Cerebrovascular Diseases - May 13, 2022 Category: Neurology Authors: Hariklia Proios, Maria Baskini, Christos Keramydas, Tatiana Pourliaka, Kalliopi Tsakpounidou Source Type: research

Clinical Prediction Rule for Identifying the Stroke Patients who will Obtain Clinically Important Improvement of Upper Limb Motor Function by Robot-Assisted Upper Limb
The number of studies on the characteristics of patients with stroke who would benefit from robot-assisted upper limb rehabilitation is limited, and there are no clear criteria for determining which individuals should receive such treatment. The current study aimed to develop a clinical prediction rule using machine learning to identify the characteristics of patients with stroke who can the achieve minimal clinically important difference of the Fugl-Meyer Upper Extremity Evaluation (FMA-UE) after single-joint hybrid assistive limb (HAL-SJ) rehabilitation.
Source: Journal of Stroke and Cerebrovascular Diseases - April 29, 2022 Category: Neurology Authors: Yuji Iwamoto, Takeshi Imura, Ryo Tanaka, Tsubasa Mitsutake, Hungu Jung, Takahiro Suzukawa, Shingo Taki, Naoki Imada, Tetsuji Inagawa, Hayato Araki, Osamu Araki 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

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

Time metrics in acute ischemic stroke care during the second and first wave of COVID 19 Pandemic: A tertiary care center experience from South India.
This study shares our experience in stroke time metrics during the second wave of pandemic compared to the first wave.
Source: Journal of Stroke and Cerebrovascular Diseases - January 13, 2022 Category: Neurology Authors: Dileep Ramachandran, Praveen Panicker, P Chitra, Thomas Iype 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

Large Vessel Occlusion Prediction in the Emergency Department with National Institutes of Health Stroke Scale Components: A Machine Learning Approach
To determine the feasibility of using a machine learning algorithm to screen for large vessel occlusions (LVO) in the Emergency Department (ED).
Source: Journal of Stroke and Cerebrovascular Diseases - August 15, 2021 Category: Neurology Authors: Donglai Huo, Michelle Leppert, Rebecca Pollard, Sharon N. Poisson, Xiang Fang, David Rubinstein, Igor Malenky, Kelsey Eklund, Eric Nyberg Tags: Short Communication Source Type: research

Machine-Learning-Derived Model for the Stratification of Cardiovascular risk in Patients with Ischemic Stroke
Background Stratification of cardiovascular risk in patients with ischemic stroke is important as it may inform management strategies. We aimed to develop a machine-learning-derived prognostic model for the prediction of cardiovascular risk in ischemic stroke patients.
Source: Journal of Stroke and Cerebrovascular Diseases - August 2, 2021 Category: Neurology Authors: George Ntaios, Dimitrios Sagris, Athanasios Kallipolitis, Efstathia Karagkiozi, Eleni Korompoki, Efstathios Manios, Vasileios Plagianakos, Konstantinos Vemmos, Ilias Maglogiannis Source Type: research

Comparison of Supervised Machine Learning Algorithms for Classifying of Home Discharge Possibility in Convalescent Stroke Patients: A Secondary Analysis
Classifying the possibility of home discharge is important during stroke rehabilitation to support decision-making. There have been several studies on supervised machine learning algorithms, but only a few have compared the performance of different algorithms based on the same dataset for the classification of home discharge possibility. Therefore, we aimed to evaluate five supervised machine learning algorithms for the classification of home discharge possibility in stroke patients.
Source: Journal of Stroke and Cerebrovascular Diseases - July 26, 2021 Category: Neurology Authors: Takeshi Imura, Haruki Toda, Yuji Iwamoto, Tetsuji Inagawa, Naoki Imada, Ryo Tanaka, Yu Inoue, Hayato Araki, Osamu Araki Source Type: research

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