Abstract
Aims: We tested the hypothesis that artificial intelligence (AI)-powered algorithms applied to cardiac magnetic resonance (CMR) images could be able to detect the potential patterns of cardiac amyloidosis (CA). Readers in CMR centers with a low volume of referrals for the detection of myocardial storage diseases or a low volume of CMRs, in general, may overlook CA. In light of the growing prevalence of the disease and emerging therapeutic options, there is an urgent need to avoid misdiagnoses. Methods and Results: Using CMR data from 502 patients (CA: n = 82), we trained convolutional neural networks (CNNs) to automatically diagnose patients with CA. We compared the diagnostic accuracy of different state-of-the-art deep learning techniques on common CMR imaging protocols in detecting imaging patterns associated with CA. As a result of a 10-fold cross-validated evaluation, the best-performing fine-tuned CNN achieved an average ROC AUC score of 0.96, resulting in a diagnostic accuracy of 94% sensitivity and 90% specificity. Conclusions: Applying AI to CMR to diagnose CA may set a remarkable milestone in an attempt to establish a fully computational diagnostic path for the diagnosis of CA, in order to support the complex diagnostic work-up requiring a profound knowledge of experts from different disciplines.
Highlights
The predominant condition was heart failure patients with preserved ejection fraction (HFpEF) (n = 163), 107 patients were diagnosed with ischemic cardiomyopathy, 53 were diagnosed with hypertrophic and other cardiomyopathies, 44 patients had valvular heart disease, 30 patients suffered from cardiac sarcoidosis, 19 patients had HF condition linked with congenital heart disease, including muscular dystrophies, and the remaining 4 patients were diagnosed with rare HF conditions, such as pericardial disease (n = 3) and left atrial myxoma (n = 1)
late gadolinium enhancement (LGE) cardiac magnetic resonance (CMR) imaging represents a real alternative to myocardial biopsy for diagnosing cardiac amyloidosis (CA) with an excellent diagnostic accuracy [19], readers in CMR centers with a low volume of referrals for the detection of myocardial storage diseases or a low volume of cardiac CMRs in general may overlook nonspecific or rare signs for CA
We demonstrate here that an automated classification of CA patients by CMR images using state-of-the-art convolutional neural networks (CNNs) is possible and akin to human experts
Summary
Cardiac amyloidosis (CA) is associated with substantial morbidity and mortality. The increased use of cardiac magnetic resonance imaging (CMR) in cardiology has revealed a previously unrecognized prevalence of CA, which has emerged from a “rare” disease that was often only diagnosed post mortem, to a condition of significant clinical relevance that every cardiologist is confronted with. An autopsy study could demonstrate the presence of CA in 25% of elderly people (≥85 years) [3]. Further studies showed that 14% of patients undergoing transcatheter aortic valve implantation, 13% of heart failure patients with preserved ejection fraction (HFpEF), and 8% of severe aortic stenosis patients suffer from concomitant CA [4,5,6]
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