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.
Author: | Jan Wigand EcksteinORCiDGND, Negin MoghadasiORCiDGND, Hermann KörperichORCiDGND, Rehsan AkkuzuGND, Vanessa SciaccaORCiDGND, Christian SohnsORCiDGND, Philipp SommerORCiDGND, Julian BergGND, Jerzy PaluszkiewiczGND, Wolfgang BurchertORCiDGND, Misagh PiranORCiDGND |
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URN: | urn:nbn:de:hbz:294-108762 |
DOI: | https://doi.org/10.3390/diagnostics13142426 |
Parent Title (English): | Diagnostics |
Publisher: | MDPI |
Place of publication: | Basel |
Document Type: | Article |
Language: | English |
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 |
Volume: | 13 |
Issue: | 14, Article 2426 |
First Page: | 2426-1 |
Last Page: | 2426-14 |
Note: | 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): | Creative Commons - CC BY 4.0 - Attribution 4.0 International |