Deep Learning to Diagnose Cardiac Amyloidosis from Cardiac Magnetic Resonance Imaging

Background: Cardiac 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 possibly establish the likelihood of CA. Methods: Subjects with suspected CA (n=206 patients; n=100, 49% with unexplained left ventricular – LV – hypertrophy; n=106, 51% with blood dyscrasia and suspected light-chain amyloidosis) underwent 1.5 T CMR. Patients were randomized 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). Tags “amyloidosis present” or “absent” were attributed when the average probability of CA from the 3 networks was ≥50% or