ArticleDiagnostics (Basel, Switzerland)2022
A Machine Learning Challenge: Detection of Cardiac Amyloidosis Based on Bi-Atrial and Right Ventricular Strain and Cardiac Function.
Article in Diagnostics (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 1 of them a synthesis that pooled it.
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Who cites it
17 citing papers in PubMed, 1 synthesis or guideline pooled it, 24 citations in OpenAlex.
- Machine learning-based methods in diagnosing cardiac amyloidosis: a meta-analysis.Frontiers in cardiovascular medicine · 2026Pooled it
- Assessing the quality of reporting in artificial intelligence/machine learning research for cardiac amyloidosis.JAMIA open · 2025Article
- The Role of Artificial Intelligence in Cardiac Amyloidosis: A Focus on Diagnosis and Clinical Application.Journal of cardiovascular development and disease · 2025Review
- Review
- The Role of Artificial Intelligence in the Detection of Cardiac Amyloidosis: A Systematic Review.Cureus · 2025Review
- Revolutionizing Cardiology: The Role of Artificial Intelligence in Echocardiography.Journal of clinical medicine · 2025Review
- A machine learning prediction model for Cardiac Amyloidosis using routine blood tests in patients with left ventricular hypertrophy.Scientific reports · 2024Article
- Review
- Revolutionizing Cardiology through Artificial Intelligence-Big Data from Proactive Prevention to Precise Diagnostics and Cutting-Edge Treatment-A Comprehensive Review of the Past 5 Years.Diagnostics (Basel, Switzerland) · 2024Review
- How Artificial Intelligence Can Enhance the Diagnosis of Cardiac Amyloidosis: A Review of Recent Advances and Challenges.Journal of cardiovascular development and disease · 2024Review
- Artificial Intelligence in the Differential Diagnosis of Cardiomyopathy Phenotypes.Diagnostics (Basel, Switzerland) · 2024Review
- Clinical and Cardiovascular Magnetic Resonance Imaging Features of Cardiac Amyloidosis.Reviews in cardiovascular medicine · 2023Review
- Imaging findings of right cardiac amyloidosis: impact on prognosis and clinical course.Journal of ultrasound · 2023Review
- Machine-Learning-Based Diagnostics of Cardiac Sarcoidosis Using Multi-Chamber Wall Motion Analyses.Diagnostics (Basel, Switzerland) · 2023Article
- Artificial Intelligence Applications in Cardiovascular Magnetic Resonance Imaging: Are We on the Path to Avoiding the Administration of Contrast Media?Diagnostics (Basel, Switzerland) · 2023Review
- Machine Learning Approaches in Diagnosis, Prognosis and Treatment Selection of Cardiac Amyloidosis.International journal of molecular sciences · 2023Review
- Personalized biomechanical insights in atrial fibrillation: opportunities & challenges.Expert review of cardiovascular therapyReview
Corrections and comments
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Authors and funding
8 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThis study challenges state-of-the-art cardiac amyloidosis (CA) diagnostics by feeding multi-chamber strain and cardiac function into supervised machine (SVM) learning algorithms.
methodsForty-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.
resultsForty-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.
conclusionSVM 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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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.