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Related Concept Videos

Ischemic Stroke l: Introduction01:15

Ischemic Stroke l: Introduction

57
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.
57
Ischemic Stroke ll: Pathophysiology01:15

Ischemic Stroke ll: Pathophysiology

74
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...
74

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Predictive etiological classification of acute ischemic stroke through interpretable machine learning algorithms: a

Siding Chen1,2,3, Xiaomeng Yang1, Hongqiu Gu1,2

  • 1Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, No.119 South 4th Ring West Road, Fengtai District, Beijing, 100070, China.

BMC Medical Research Methodology
|September 10, 2024
PubMed
Summary

Machine learning models accurately predict acute ischemic stroke (AIS) subtypes, outperforming logistic regression. These AI tools can identify cardioembolism (CE) in undetermined cases, improving secondary prevention strategies.

Keywords:
Acute ischemic strokeClinical predictionEtiological classificationMachine learningProspective cohort study

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Area of Science:

  • Neurology
  • Artificial Intelligence
  • Medical Informatics

Background:

  • Prognosis and secondary prevention vary significantly across acute ischemic stroke (AIS) subtypes.
  • Machine learning (ML) can uncover complex patterns in medical data for etiological classification.
  • Limited research exists on using ML for predicting AIS etiology.

Purpose of the Study:

  • Develop interpretable ML models for AIS etiology prediction.
  • Identify key factors influencing stroke etiology classification.
  • Enhance current clinical categorization of stroke subtypes.

Main Methods:

  • Utilized the Third China National Stroke Registry (CNSR-III) dataset.
  • Trained nine ML models (NGBoost, CatBoost, XGBoost, RF, LGBM, GBDT, AdaBoost, SVM, LR) to predict large artery atherosclerosis (LAA), small vessel occlusion (SVO), and cardioembolism (CE).
  • Employed SFS-XGB with 10-fold cross-validation for feature selection and evaluated models using AUC and Brier scores.

Main Results:

  • Included 5,213 patients: 47.4% LAA, 41.3% SVO, 11.3% CE.
  • ML models significantly outperformed logistic regression (LR) in LAA and SVO prediction (P < 0.001).
  • Optimal models achieved AUCs of 0.932 (SVO/RF), 0.917 (LAA/NGBoost), and 0.846 (CE/LGBM); models showed satisfactory calibration.
  • The optimal CE model identified potential CE in 45.7% of undetermined etiology (SUE) cases.

Conclusions:

  • ML algorithms effectively classify AIS subtypes (LAA, SVO, CE) with superior performance over LR.
  • The optimal ML model can identify potential CE patients within the SUE group.
  • These predictive factors may enhance etiological classification and guide secondary prevention strategies.