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Machine-Learning-Based Diagnostics of Cardiac Sarcoidosis Using Multi-Chamber Wall Motion Analyses
- Source :
- Diagnostics, Vol 13, Iss 14, p 2426 (2023)
- Publication Year :
- 2023
- Publisher :
- MDPI AG, 2023.
-
Abstract
- 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.
Details
- Language :
- English
- ISSN :
- 20754418
- Volume :
- 13
- Issue :
- 14
- Database :
- Directory of Open Access Journals
- Journal :
- Diagnostics
- Publication Type :
- Academic Journal
- Accession number :
- edsdoj.f20572c4ffe46e7aca01a66e7aab4b6
- Document Type :
- article
- Full Text :
- https://doi.org/10.3390/diagnostics13142426