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- Title
Deep learning to diagnose cardiac amyloidosis from cardiovascular magnetic resonance.
- Authors
Martini, Nicola; Aimo, Alberto; Barison, Andrea; Latta, Daniele Della; Vergaro, Giuseppe; Aquaro, Giovanni Donato; Ripoli, Andrea; Emdin, Michele; Chiappino, Dante
- Abstract
Background: Cardiovascular magnetic resonance (CMR) is part of the diagnostic work-up for cardiac amyloidosis (CA). Deep learning (DL) is an application of artificial intelligence that may allow to automatically analyze CMR findings and establish the likelihood of CA. Methods: 1.5 T CMR was performed in 206 subjects with suspected CA (n = 100, 49% with unexplained left ventricular (LV) hypertrophy; n = 106, 51% with blood dyscrasia and suspected light-chain amyloidosis). Patients were randomly assigned to the training (n = 134, 65%), validation (n = 30, 15%), and testing subgroups (n = 42, 20%). Short axis, 2-chamber, 4-chamber late gadolinium enhancement (LGE) images were evaluated by 3 networks (DL algorithms). The tags “amyloidosis present” or “absent” were attributed when the average probability of CA from the 3 networks was ≥ 50% or < 50%, respectively. The DL strategy was compared to a machine learning (ML) algorithm considering all manually extracted features (LV volumes, mass and function, LGE pattern, early blood-pool darkening, pericardial and pleural effusion, etc.), to reproduce exam reading by an experienced operator. Results: The DL strategy displayed good diagnostic accuracy (88%), with an area under the curve (AUC) of 0.982. The precision (positive predictive value), recall score (sensitivity), and F1 score (a measure of test accuracy) were 83%, 95%, and 89% respectively. A ML algorithm considering all CMR features had a similar diagnostic yield to DL strategy (AUC 0.952 vs. 0.982; p = 0.39). Conclusions: A DL approach evaluating LGE acquisitions displayed a similar diagnostic performance for CA to a MLbased approach, which simulates CMR reading by experienced operators.
- Publication
Journal of Cardiovascular Magnetic Resonance (BioMed Central), 2020, Vol 22, Issue 1, p1
- ISSN
1532-429X
- Publication type
Article
- DOI
10.1186/s12968-020-00690-4