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Related Concept Videos

Ischemic Stroke l: Introduction01:15

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.
Ischemic Stroke ll: Pathophysiology01:15

Ischemic Stroke ll: Pathophysiology

An ischemic stroke occurs when a cerebral blood vessel becomes obstructed, most often by a thrombus or embolus, interrupting the delivery of oxygen and glucose to brain tissue. Because neurons rely on continuous aerobic metabolism, energy failure begins within minutes of reduced perfusion. The region receiving the least blood flow becomes the infarct core, an area of irreversible cellular death. Surrounding this core lies the penumbra, a zone of hypoperfused but still viable tissue that is...
Electrocardiogram01:29

Electrocardiogram

An electrocardiogram (ECG or EKG) is a critical diagnostic tool that records the electrical signals produced by the heart during each heartbeat. This recording is achieved through electrodes placed strategically on the arms, legs, and chest. The electrocardiograph amplifies these signals and produces 12 distinct tracings, offering a comprehensive understanding of the heart's electrical activity.
Three major waveforms are present in a typical ECG recording: the P wave, the QRS complex, and the T...

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Related Experiment Video

Updated: May 14, 2026

Electroencephalography Network Indices as Biomarkers of Upper Limb Impairment in Chronic Stroke
06:37

Electroencephalography Network Indices as Biomarkers of Upper Limb Impairment in Chronic Stroke

Published on: July 14, 2023

ECG Signatures and Long-Term Ischemic Stroke Risk: A Deep Learning Analysis of 200,000 Patients.

Rahul Mahajan1, Danielle F Pace2, Samuel F Friedman2

  • 1Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA; Department of Neurology, Brigham and Women's Hospital, Boston, Massachusetts, USA.

Journal of the American College of Cardiology
|May 13, 2026
PubMed
Summary

Deep learning of 12-lead electrocardiograms (ECGs) can predict 10-year ischemic stroke risk, similar to clinical scores. This artificial intelligence (AI) approach may help prioritize stroke prevention strategies.

Keywords:
artificial intelligenceatrial cardiopathydeep learningelectrocardiographyischemic strokerisk stratification

Related Experiment Videos

Last Updated: May 14, 2026

Electroencephalography Network Indices as Biomarkers of Upper Limb Impairment in Chronic Stroke
06:37

Electroencephalography Network Indices as Biomarkers of Upper Limb Impairment in Chronic Stroke

Published on: July 14, 2023

Area of Science:

  • Cardiology
  • Artificial Intelligence
  • Stroke Prediction

Background:

  • Scalable risk stratification for ischemic stroke is a significant unmet clinical need.
  • Current methods may not fully capture stroke risk mechanisms.

Purpose of the Study:

  • To assess deep learning (DL) on 12-lead electrocardiograms (ECGs) for estimating longitudinal ischemic stroke risk.
  • To quantify how DL-derived risk signals reflect potential stroke mechanisms like atrial cardiopathy.

Main Methods:

  • A convolutional neural network (CNN) was trained to predict 10-year ischemic stroke risk using 12-lead ECGs from Massachusetts General Hospital (MGH).
  • The model (ECG2Stroke) integrated DL probabilities with age and sex in a Cox model.
  • Model performance (discrimination and calibration) was evaluated in independent hospital datasets (MGH, Brigham and Women's Hospital, Beth Israel Deaconess Medical Center) and compared to the Framingham Stroke Risk Profile (FSRP).

Main Results:

  • ECG2Stroke demonstrated moderate discrimination for incident stroke across multiple cohorts (10-year AUCs ranging from 0.772 to 0.795).
  • Model calibration was favorable (integrated calibration indices ranging from 0.005 to 0.030).
  • Performance was comparable to FSRP and persisted across subgroups, including those without atrial fibrillation; saliency maps indicated P-wave importance, correlating with atrial substrate markers and cardioembolic stroke.

Conclusions:

  • ECG-based artificial intelligence (AI) effectively predicts 10-year ischemic stroke risk, achieving performance similar to established clinical scores.
  • AI analysis of ECGs may identify abnormal atrial substrate linked to cardioembolism, enabling efficient stroke prevention prioritization.