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Related Concept Videos

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DefinitionRenal angiography, also known as renal arteriography, is an imaging technique used to obtain a comprehensive view of blood flow and the vascular structure of blood vessels in the kidneys and surrounding areas.PurposeRenal angiography detects blood vessel abnormalities in the kidneys, such as aneurysms, stenosis, thrombosis, vascular tumors, and renal artery stenosis. It evaluates kidney function and guides interventional treatments like angioplasty or stent placement.Pre-Procedure...
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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.
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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...
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Related Experiment Video

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Deep Learning-Based Automatic Classification of Ischemic Stroke Subtype Using Diffusion-Weighted Images.

Wi-Sun Ryu1,2, Dawid Schellingerhout3, Hoyoun Lee2

  • 1Department of Neurology, Dongguk University Ilsan Hospital, Goyang, Korea.

Journal of Stroke
|June 5, 2024
PubMed
Summary

A deep learning model accurately classifies ischemic stroke subtypes using diffusion-weighted imaging (DWI) and atrial fibrillation (AF) data. The DWI+AF model achieved results comparable to expert consensus, aiding stroke prevention.

Keywords:
Artificial intelligenceAtrial fibrillationDeep learningDiffusion magnetic resonance imagingIschemic stroke

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Area of Science:

  • Neurology
  • Artificial Intelligence
  • Medical Imaging

Background:

  • Accurate ischemic stroke subtype classification is crucial for effective secondary prevention strategies.
  • Deep learning models offer potential for improving diagnostic accuracy in stroke care.

Purpose of the Study:

  • To develop and validate a deep learning algorithm for classifying ischemic stroke subtypes.
  • To assess the performance of models using diffusion-weighted imaging (DWI) alone and combined with atrial fibrillation (AF) data.

Main Methods:

  • A deep learning model utilizing U-net for infarct segmentation and EfficientNetV2 for classification was developed using data from 2,988 ischemic stroke patients.
  • Two algorithms were trained: one using only DWI data (DWI-only) and another incorporating AF data (DWI+AF).
  • Model performance was evaluated through internal testing against expert neurologists, external DWI data, and a clinical trial dataset.

Main Results:

  • The DWI+AF algorithm demonstrated superior performance, achieving 79.1% agreement in internal testing and 73.7%-74.0% in external testing, compared to the DWI-only algorithm.
  • In the clinical trial dataset, the DWI+AF algorithm achieved 72.9% agreement and a Cohen's kappa of 0.57, comparable to expert consensus (76.0% agreement, 0.61 kappa).
  • The DWI-only algorithm showed moderate agreement (65.3% internal, 59.3%-60.7% external).

Conclusions:

  • A deep learning model trained on a large DWI dataset, with or without AF information, can classify ischemic stroke subtypes.
  • The DWI+AF deep learning model shows performance comparable to stroke expert consensus.
  • This AI-driven approach holds promise for enhancing stroke subtype classification and guiding secondary prevention efforts.