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  1. Home
  3. Left Ventricular Myocardial Dysfunction Evaluation In Thalassemia Patients Using Echocardiographic Radiomic Features And Machine Learning Algorithms.
  1. Home
  3. Left Ventricular Myocardial Dysfunction Evaluation In Thalassemia Patients Using Echocardiographic Radiomic Features And Machine Learning Algorithms.

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Left Ventricular Myocardial Dysfunction Evaluation in Thalassemia Patients Using Echocardiographic Radiomic Features

Haniyeh Taleie1, Ghasem Hajianfar2, Maziar Sabouri1,3

  • 1Department of Medical Physics, Iran University of Medical Sciences, Tehran, Iran.

Journal of Digital Imaging
|September 22, 2023

View abstract on PubMed

Summary
This summary is machine-generated.

Beta-thalassemia major patients at risk of heart failure due to iron overload can be identified using echocardiography and machine learning. This approach offers a feasible method to predict cardiac complications from myocardial iron deposits.

Keywords:
Cardiac magnetic resonance imagingEchocardiographyMachine learningRadiomicsThalassemia

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Area of Science:

  • Cardiology
  • Medical Imaging
  • Artificial Intelligence

Background:

  • Heart failure from myocardial iron deposits is a major cause of mortality in beta-thalassemia major.
  • Cardiac magnetic resonance imaging T2* is standard for detecting myocardial iron overload but has limitations.
  • Early detection of cardiac involvement is crucial for managing beta-thalassemia major.

Purpose of the Study:

  • To differentiate beta-thalassemia major patients with and without myocardial iron overload using echocardiography-derived radiomic features and machine learning.
  • To assess the feasibility of using machine learning models for predicting cardiac iron overload in patients with normal left ventricular ejection fraction (LVEF).

Main Methods:

  • Radiomic features were extracted from end-systolic (ES) and end-diastolic (ED) echocardiography images of 44 patients with iron overload (T2* ≤ 20 ms) and 47 controls (T2* > 20 ms), all with LVEF > 55%.
  • Three feature selection methods (MRMR-XGB, ANOVA-MLP, RFE-KNN) and six classifiers were employed.
  • Model performance was evaluated using AUC, ACC, SEN, and SPE.

    • Main Results:

    • The MRMR-XGB model achieved the highest performance on the ED dataset with AUC=0.73, ACC=0.73, SPE=0.73, and SEN=0.73.
    • ANOVA-MLP and RFE-KNN also demonstrated promising results on ES and combined ED&ES datasets, respectively.
    • These findings indicate the potential of radiomic analysis in identifying myocardial iron overload.

    Conclusions:

    • Radiomic features extracted from echocardiography, combined with machine learning, provide a feasible method for predicting cardiac complications related to iron overload in beta-thalassemia major patients.
    • This non-invasive approach could complement or serve as an alternative to traditional imaging techniques for monitoring myocardial iron levels.