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A Machine Learning Challenge: Detection of Cardiac Amyloidosis Based on Bi-Atrial and Right Ventricular Strain and
Jan Eckstein1, Negin Moghadasi2, Hermann Körperich1
1Institute for Radiology, Nuclear Medicine and Molecular Imaging, Heart and Diabetes Center North-Rhine Westphalia, Ruhr-University of Bochum, 32545 Bad Oeynhausen, Germany.
Insights
Machine learning accurately diagnoses cardiac amyloidosis (CA) using multi-chamber strain and function data. Support vector machine with radial basis function kernel showed high diagnostic performance, offering new clinical decision support.
Area of Science:
- Cardiology
- Artificial Intelligence
- Medical Imaging
Background:
- Cardiac amyloidosis (CA) diagnostics require improved methods.
- This study explores machine learning for CA detection using cardiac function and strain data.
Purpose of the Study:
- To evaluate the efficacy of supervised machine learning algorithms in diagnosing cardiac amyloidosis.
- To assess the diagnostic performance of multi-chamber strain and cardiac function parameters.
Main Methods:
- Cardiovascular magnetic resonance imaging (CMR) was performed on 43 CA patients, 20 hypertrophic cardiomyopathy (HCM) patients, and 44 controls.
- A 41-feature matrix including multi-chamber strain and function was used for Decision Tree (DT), K-Nearest Neighbor (KNN), and Support Vector Machine (SVM) algorithms.
- Principal Component Analysis (PCA) was applied to reduce features to 10 for SVM analysis.
Main Results:
- SVM with Radial Basis Function (RBF) kernel achieved 90.9% accuracy (AUC=0.996), with 100% sensitivity and 97% F1-Score.
- SVM linear kernel achieved 87.9% accuracy (AUC=0.960).
- Bi-atrial longitudinal strain and atrial ejection fraction were identified as key predictors for CA.
Conclusions:
- SVM RBF kernel demonstrates high diagnostic accuracy for cardiac amyloidosis under supervised learning.
- Machine learning analysis of cardiac strain and function offers a promising avenue for non-contrast CA diagnostics and clinical decision support.
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 (±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.
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