Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Ischemic Stroke l: Introduction

Ischemic Stroke l: Introduction

Ischemic stroke is an acute cerebrovascular condition in which blood flow to a brain region is suddenly interrupted, leading to tissue infarction. Neurons depend on continuous oxygen and glucose supply, so even brief reductions in perfusion cause energy failure, ionic imbalance, and irreversible injury. Ischemic strokes are classified into thrombotic and embolic types based on their underlying mechanisms.Thrombotic MechanismsThrombotic stroke develops when a clot forms within a cerebral artery.

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Co-designing Nova Sleepcare: identifying priority areas for implementation.

Frontiers in pediatrics·2026

Same author

Sex Differences in Screening for Large Vessel Occlusion and Thrombectomy: A Population-Based Cohort Study.

Stroke·2026

Same author

Safety and Efficacy of Nerinetide at Year 1 in Participants Enrolled in ESCAPE-NEXT: A Multicenter, Double-Blind, Randomized Controlled Trial.

Journal of the American Heart Association·2026

Same author

Advances in Acute Ischemic Stroke 2025: Medical Treatments.

Stroke·2026

Same author

Association of Successful Recanalization and Functional Outcomes in Minor Ischemic Stroke With Proven Occlusion: A Secondary Analysis of TEMPO-2 Trial.

Stroke·2026

Same author

Feasibility of a parent-delivered attention and working memory intervention for early school-aged children born preterm.

Applied neuropsychology. Child·2026

Search research articles

Related Experiment Video

Updated: Jun 9, 2026

Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images

Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images

Published on: April 13, 2013

Risk Factors for Perinatal Arterial Ischemic Stroke: A Machine Learning Approach.

Ratika Srivastava¹, Lauran Cole¹, Kimberly Amador¹

¹From the Division of Pediatric Neurology (R.S., L.C.), Department of Pediatrics, University of Alberta; Alberta Children's Hospital Research Institute and Department of Clinical Neurosciences (K.A.); Department of Clinical Neurosciences (N.D.F.); Department of Pediatrics and Clinical Neurosciences (M.D.), University of Calgary, Alberta; Departments of Pediatrics and Neurology/Neurosurgery (M.I.S., M.O.), McGill University, Montreal, Quebec, Canada; Newcastle upon Tyne Hospitals (A.P.B.), NHS Foundation Trust, Newcastle upon Tyne, United Kingdom; Department of Neurology (M.J.R.), Boston Children's Hospital and Department of Neurology, Harvard Medical School, Boston, MA; Department of Neonatology (E.S.), Soroka University Medical Center and Faculty of Health sciences, Ben-Gurion University of the Negev, Beer-Sheva, Israel; Department of Neonatology (L.S.V.), University Medical Center Utrecht, The Netherlands; Departments of Pediatrics and Community Health Sciences (D.D.), Owerko Centre at the Alberta Children's Hospital Research Institute, Hotchkiss Brain Institute, Cummings School of Medicine; Faculty of Nursing and Cumming School of Medicine (N.L.), Departments of Pediatrics, Psychiatry and Community Health Sciences; Alberta Children's Hospital Research Institute and Department of Clinical Neurosciences (P.M.); Departments of Clinical Neurosciences (M.D.H.), Community Health Sciences, Medicine and Radiology, Hotchkiss Brain Institute and Department of Pediatrics (A.K.), Cumming School of Medicine, University of Calgary, Alberta, Canada.

|May 15, 2024

Summary

Machine learning identified key clinical factors for perinatal arterial ischemic stroke (PAIS), a leading cause of cerebral palsy. This data-driven approach accurately predicts PAIS risk in neonates, outperforming traditional models.

More Related Videos

Transient Middle Cerebral Artery Occlusion Model of Neonatal Stroke in P10 Rats

Transient Middle Cerebral Artery Occlusion Model of Neonatal Stroke in P10 Rats

Published on: April 21, 2017

Early Pathological and Magnetic Resonance Detection of Cerebral Injury Using a Rat Model of Neonatal Hypoxic Ischemic Encephalopathy

Early Pathological and Magnetic Resonance Detection of Cerebral Injury Using a Rat Model of Neonatal Hypoxic Ischemic Encephalopathy

Published on: October 28, 2022

Related Experiment Videos

Last Updated: Jun 9, 2026

Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images

Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images

Published on: April 13, 2013

Transient Middle Cerebral Artery Occlusion Model of Neonatal Stroke in P10 Rats

Transient Middle Cerebral Artery Occlusion Model of Neonatal Stroke in P10 Rats

Published on: April 21, 2017

Early Pathological and Magnetic Resonance Detection of Cerebral Injury Using a Rat Model of Neonatal Hypoxic Ischemic Encephalopathy

Early Pathological and Magnetic Resonance Detection of Cerebral Injury Using a Rat Model of Neonatal Hypoxic Ischemic Encephalopathy

Published on: October 28, 2022

Area of Science:

Neurology
Pediatrics
Data Science

Background:

Perinatal arterial ischemic stroke (PAIS) is a significant cause of hemiparetic cerebral palsy.
Previous studies on PAIS predictors were limited by sample size and complex factor interactions.

Purpose of the Study:

To apply machine learning to large datasets for unbiased identification of PAIS clinical predictors.
To compare a data-driven machine learning model with traditional literature-driven prediction models for PAIS.

Main Methods:

Utilized common data elements from three PAIS registries and a healthy control cohort.
Employed a random forest machine learning pipeline on data from 2,571 neonates (527 cases, 2,044 controls).
Included maternal/pregnancy, intrapartum, and neonatal factors in the analysis.

Main Results:

The machine learning model achieved 86.5% balanced accuracy in predicting PAIS.
Identified key predictors including maternal age, substance exposure, intrapartum fever, and Apgar scores.
The machine learning model (AUC 0.93) significantly outperformed the literature-driven model (AUC 0.73).

Conclusions:

Machine learning offers an unbiased method for identifying PAIS clinical predictors.
The findings support the multifactorial nature of PAIS pathophysiology.
Neonates at risk for PAIS can be identified using this approach.