Machine-learning-based diagnostics of cardiac sarcoidosis using multi-chamber wall motion analyses

  • Background: Hindered by its unspecific clinical and phenotypical presentation, cardiac sarcoidosis (CS) remains a challenging diagnosis. Objective: Utilizing cardiac magnetic resonance imaging (CMR), we acquired multi-chamber volumetrics and strain feature tracking for a support vector machine learning (SVM)-based diagnostic approach to CS. Method: Forty-five CMR-negative (CMR(−), 56.5(53.0;63.0)years), eighteen CMR-positive (CMR(+), 64.0(57.8;67.0)years) sarcoidosis patients and forty-four controls (CTRL, 56.5(53.0;63.0)years)) underwent CMR examination. Cardiac parameters were processed using the classifiers of logistic regression, KNN(K-nearest-neighbor), DT (decision tree), RF (random forest), SVM, GBoost, XGBoost, Voting and feature selection. Results: In a three-cluster analysis of CTRL versus vs. CMR(+) vs. CMR(−), RF and Voting classifier yielded the highest prediction rates (81.82%). The two-cluster analysis of CTRL vs. all sarcoidosis (All Sarc.) yielded high prediction rates with the classifiers logistic regression, RF and SVM (96.97%), and low prediction rates for the analysis of CMR(+) vs. CMR(−), which were augmented using feature selection with logistic regression (89.47%). Conclusion: Multi-chamber cardiac function and strain-based supervised machine learning provides a non-contrast approach to accurately differentiate between healthy individuals and sarcoidosis patients. Feature selection overcomes the algorithmically challenging discrimination between CMR(+) and CMR(−) patients, yielding high accuracy predictions. The study findings imply higher prevalence of cardiac involvement than previously anticipated, which may impact clinical disease management.

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Author:Jan Wigand EcksteinORCiDGND, Negin MoghadasiORCiDGND, Hermann KörperichORCiDGND, Rehsan AkkuzuGND, Vanessa SciaccaORCiDGND, Christian SohnsORCiDGND, Philipp SommerORCiDGND, Julian BergGND, Jerzy PaluszkiewiczGND, Wolfgang BurchertORCiDGND, Misagh PiranORCiDGND
Parent Title (English):Diagnostics
Place of publication:Basel
Document Type:Article
Date of Publication (online):2024/02/26
Date of first Publication:2023/07/20
Publishing Institution:Ruhr-Universität Bochum, Universitätsbibliothek
Tag:Open Access Fonds
cardiac magnetic resonance; cardiac sarcoidosis 3; cardiac strain; machine learning 2; multi-chamber analyses
Issue:14, Article 2426
First Page:2426-1
Last Page:2426-14
Article Processing Charge funded by the Deutsche Forschungsgemeinschaft (DFG) and the Open Access Publication Fund of Ruhr-Universität Bochum.
Institutes/Facilities:Herz- und Diabeteszentrum NRW
Herz- und Diabeteszentrum NRW, Institut für Radiologie, Nuklearmedizin und molekulare Bildgebung
Dewey Decimal Classification:Technik, Medizin, angewandte Wissenschaften / Medizin, Gesundheit
open_access (DINI-Set):open_access
Licence (English):License LogoCreative Commons - CC BY 4.0 - Attribution 4.0 International