ArticleDiagnostics (Basel, Switzerland)2021
Deep Learning Supplants Visual Analysis by Experienced Operators for the Diagnosis of Cardiac Amyloidosis by Cine-CMR.
Article in Diagnostics (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 1 of them a synthesis that pooled it.
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Who cites it
8 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Machine learning-based methods in diagnosing cardiac amyloidosis: a meta-analysis.Frontiers in cardiovascular medicine · 2026Pooled it
- Predicting prognosis of light-chain cardiac amyloidosis by magnetic resonance imaging and deep learning.European heart journal. Cardiovascular Imaging · 2025Article
- Assessing the quality of reporting in artificial intelligence/machine learning research for cardiac amyloidosis.JAMIA open · 2025Article
- Article
- Article
- A Deep Learning Approach to Classify Fabry Cardiomyopathy from Hypertrophic Cardiomyopathy Using Cine Imaging on Cardiac Magnetic Resonance.International journal of biomedical imaging · 2024Article
- Deep Learning to Classify AL versus ATTR Cardiac Amyloidosis MR Images.Biomedicines · 2023Article
- Editorial on Special Issue "Artificial Intelligence in Image-Based Screening, Diagnostics, and Clinical Care of Cardiopulmonary Diseases".Diagnostics (Basel, Switzerland) · 2022Article
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Authors and funding
7 authors.
Funding
Abstract
backgroundDiagnosing cardiac amyloidosis (CA) from cine-CMR (cardiac magnetic resonance) alone is not reliable. In this study, we tested if a convolutional neural network (CNN) could outperform the visual diagnosis of experienced operators.
method119 patients with cardiac amyloidosis and 122 patients with left ventricular hypertrophy (LVH) of other origins were retrospectively selected. Diastolic and systolic cine-CMR images were preprocessed and labeled. A dual-input visual geometry group (VGG ) model was used for binary image classification. All images belonging to the same patient were distributed in the same set. Accuracy and area under the curve (AUC) were calculated per frame and per patient from a 40% held-out test set. Results were compared to a visual analysis assessed by three experienced operators.
resultsframe-based comparisons between humans and a CNN provided an accuracy of 0.605 vs. 0.746 (
conclusionbased on cine-CMR images alone, a CNN is able to discriminate cardiac amyloidosis from LVH of other origins better than experienced human operators (15 to 20 points more in absolute value for accuracy and AUC), demonstrating a unique capability to identify what the eyes cannot see through classical radiological analysis.
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