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Related Concept Videos

Imaging Studies for Cardiovascular System I:Echocardiography01:17

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Related Experiment Video

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Ultrasonic Assessment of Myocardial Microstructure
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Utilizing echocardiography and unsupervised machine learning for heart failure risk identification.

Jakob Øystein Simonsen1, Daniel Modin1, Kristoffer Skaarup1

  • 1Department of Cardiology, Herlev and Gentofte University Hospital, Copenhagen, Denmark.

International Journal of Cardiology
|October 12, 2024
PubMed
Summary

Unsupervised machine learning identified novel patterns in cardiac strain curves, predicting heart failure (HF) risk beyond global longitudinal strain (GLS). These patterns reveal previously unknown ventricular deformation characteristics associated with increased HF incidence.

Keywords:
Artificial intelligenceCluster analysisEchocardiographyHeart failureLongitudinal strainUnsupervised machine learning

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Area of Science:

  • Cardiology
  • Biomedical Engineering
  • Data Science

Background:

  • Global longitudinal strain (GLS) is a key heart failure (HF) predictor.
  • Focusing solely on peak GLS may miss crucial prognostic information within the entire strain curve.

Purpose of the Study:

  • To investigate if analyzing the complete cardiac strain curve with unsupervised machine learning (uML) can identify novel ventricular deformation patterns.
  • To determine if these patterns predict incident HF independently of GLS.

Main Methods:

  • Longitudinal strain curves from 3710 individuals without prevalent HF were analyzed using uML.
  • A hierarchical clustering tree (HCT) was generated, resulting in 10 distinct clusters.

Main Results:

  • The uML algorithm identified 10 clusters, with increasing HF incidence correlating with reduced early diastolic strain to peak-strain ratio.
  • Cluster 9 showed a significantly increased HF risk (HR 8.95) despite younger age and healthier baseline characteristics, linked to specific basal lateral segment deformation.
  • This cluster exhibited early systolic lengthening followed by late, reduced contraction.

Conclusions:

  • Unsupervised machine learning successfully identified novel cardiac strain patterns beyond GLS.
  • These patterns are associated with an increased risk of developing heart failure.