Journal Article

Deep learning to diagnose cardiac amyloidosis from cardiovascular magnetic resonance

Emdin, Michele, Ripoli, Andrea, Della Latta, Daniele, Aimo, Alberto, Martini, Nicola, Chiappino, Dante, Barison, Andrea, Vergaro, Giuseppe, Aquaro, Giovanni Donato

2020

Overview

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 ML-based approach, which simulates CMR reading by experienced operators.

• Publisher: BioMed Central,BioMed Central Ltd,Elsevier
• Subject: Aged / Aged, 80 and over / Algorithms / Amyloid Neuropathies, Familial - diagnostic imaging / Amyloid Neuropathies, Familial - pathology / Amyloid Neuropathies, Familial - physiopathology / Amyloidosis / Angiology / Artificial intelligence / Automation / Biopsy / Bisphosphonates / Blood / Cardiology / Cardiomyopathy, Hypertrophic - diagnostic imaging / Cardiomyopathy, Hypertrophic - pathology / Cardiomyopathy, Hypertrophic - physiopathology / Cardiovascular magnetic resonance / Chambers / Darkening / Deep Learning / Diagnosis / Diagnostic systems / Feature extraction / Female / Gadolinium / Heart / Heart enlargement / Humans / Hypertrophy / Hypertrophy, Left Ventricular - diagnostic imaging / Hypertrophy, Left Ventricular - pathology / Hypertrophy, Left Ventricular - physiopathology / Image enhancement / Image Processing, Computer-Assisted / Imaging / Immunoglobulin Light-chain Amyloidosis - diagnostic imaging / Immunoglobulin Light-chain Amyloidosis - pathology / Immunoglobulin Light-chain Amyloidosis - physiopathology / Learning algorithms / Machine learning / Magnetic resonance / Magnetic Resonance Imaging, Cine / Male / Medical imaging equipment / Medicine / Medicine & Public Health / Myocardium - pathology / Normal distribution / Pleural effusion / Predictive Value of Tests / Radiology / Reproducibility of Results / Scintigraphy / Strategy / Subgroups / Ultrasonic imaging / Ventricle / Ventricular Function, Left / Ventricular Remodeling / Work stations