Deep learning to diagnose cardiac amyloidosis from cardiovascular magnetic resonance
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RESEARCH
Deep learning to diagnose cardiac amyloidosis from cardiovascular magnetic resonance Nicola Martini1* , Alberto Aimo2,3, Andrea Barison2,3, Daniele Della Latta1, Giuseppe Vergaro2,3, Giovanni Donato Aquaro3, Andrea Ripoli1, Michele Emdin2,3 and Dante Chiappino1
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
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