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Ischemic and haemorrhagic stroke risk estimation using a machine-learning-based retinal image analysis.

Yimin Qu1,2,3, Yuanyuan Zhuo4, Jack Lee1,2

  • 1Centre for Clinical Research and Biostatistics, Jockey Club School of Public Health and Primary Care, The Chinese University of Hong Kong, Hong Kong, China.

Frontiers in Neurology
|September 8, 2022
PubMed
Summary
This summary is machine-generated.

Machine learning accurately predicts stroke subtypes using retinal images. This automated method aids in early risk assessment and classification for ischemic and hemorrhagic stroke.

Keywords:
haemorrhagic strokeischemic strokemachine-learning methodretinal image analysisstroke subtypes classification

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Area of Science:

  • Ophthalmology
  • Neurology
  • Medical Imaging
  • Artificial Intelligence

Background:

  • Stroke is a leading global cause of death, with distinct subtypes (ischemic and hemorrhagic) requiring different management.
  • Early risk classification is crucial for stroke prevention and treatment.
  • Retinal characteristics show potential for non-invasive stroke risk estimation.

Purpose of the Study:

  • To investigate machine learning (ML) for stroke risk estimation and classification using retinal images.
  • To develop and validate ML models for differentiating between ischemic and hemorrhagic stroke.
  • To assess the feasibility of using fundus photographs for automated stroke subtype analysis.

Main Methods:

  • A case-control study involving 711 participants (145 ischemic stroke, 86 hemorrhagic stroke, 480 controls).
  • Collection of demographic and medical data, along with retinal images within two weeks of admission.
  • Development of ML classification models validated using 10-fold cross-validation.

Main Results:

  • High accuracy in stroke subtype risk estimation: Ischemic stroke (91.0% sensitivity, 94.8% specificity, AUC 0.929).
  • High accuracy in stroke subtype risk estimation: Hemorrhagic stroke (93.0% sensitivity, 97.1% specificity, AUC 0.951).
  • The models demonstrated strong performance in classifying stroke subtypes based on retinal data.

Conclusions:

  • A rapid, fully automated method for stroke subtype risk assessment and classification is feasible.
  • Fundus photography combined with machine learning offers a promising tool for early stroke detection and stratification.
  • This approach can aid in clinical decision-making and potentially improve patient outcomes.