A machine learning challenge
- Background: This study challenges state-of-the-art cardiac amyloidosis (CA) diagnostics by feeding multi-chamber strain and cardiac function into supervised machine (SVM) learning algorithms. Methods: Forty-three CA (32 males; 79 years (IQR 71; 85)), 20 patients with hypertrophic cardiomyopathy (HCM, 10 males; 63.9 years (\(\pm\)7.4)) and 44 healthy controls (CTRL, 23 males; 56.3 years (IQR 52.5; 62.9)) received cardiovascular magnetic resonance imaging. Left atrial, right atrial and right ventricular strain parameters and cardiac function generated a 41-feature matrix for decision tree (DT), k-nearest neighbor (KNN), SVM linear and SVM radial basis function (RBF) kernel algorithm processing. A 10-feature principal component analysis (PCA) was conducted using SVM linear and RBF. Results: Forty-one features resulted in diagnostic accuracies of 87.9% (AUC = 0.960) for SVM linear, 90.9% (0.996; Precision = 94%; Sensitivity = 100%; F1-Score = 97%) using RBF kernel, 84.9% (0.970) for KNN, and 78.8% (0.787) for DT. The 10-feature PCA achieved 78.9% (0.962) via linear SVM and 81.8% (0.996) via RBF SVM. Explained variance presented bi-atrial longitudinal strain and left and right atrial ejection fraction as valuable CA predictors. Conclusion: SVM RBF kernel achieved competitive diagnostic accuracies under supervised conditions. Machine learning of multi-chamber cardiac strain and function may offer novel perspectives for non-contrast clinical decision-support systems in CA diagnostics.
Author: | Jan Wigand EcksteinORCiDGND, Negin MoghadasiORCiDGND, Hermann KörperichORCiDGND, Elena Weise ValdésGND, Vanessa SciaccaORCiDGND, Lech PaluszkiewiczORCiDGND, Wolfgang BurchertORCiDGND, Misagh PiranORCiDGND |
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URN: | urn:nbn:de:hbz:294-103504 |
DOI: | https://doi.org/https://doi.org/10.3390/diagnostics12112693 |
Parent Title (English): | Diagnostics |
Subtitle (English): | detection of cardiac amyloidosis based on bi-atrial and right ventricular strain and cardiac function |
Publisher: | MDPI |
Place of publication: | Basel |
Document Type: | Article |
Language: | English |
Date of Publication (online): | 2023/11/07 |
Date of first Publication: | 2022/11/04 |
Publishing Institution: | Ruhr-Universität Bochum, Universitätsbibliothek |
Tag: | Open Access Fonds artificial intelligence; cardiac amyloidosis; cardiac strain; cardiovascular magnetic resonance; support vector machine |
Volume: | 12 |
Issue: | 11, Article 2693 |
First Page: | 2693-1 |
Last Page: | 2693-13 |
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 |