Deep Learning Supplants Visual Analysis by Experienced Operators for the Diagnosis of Cardiac Amyloidosis by Cine-CMR

Abstract

Background: Diagnosing cardiac amyloidosis (CA) from cine-CMR (cardiac magnetic resonance) alone is not reliable. In this study, we tested if a convolutional neural network (CNN) could outperform the visual diagnosis of experienced operators. Method: 119 patients with cardiac amyloidosis and 122 patients with left ventricular hypertrophy (LVH) of other origins were retrospectively selected. Diastolic and systolic cine-CMR images were preprocessed and labeled. A dual-input visual geometry group (VGG ) model was used for binary image classification. All images belonging to the same patient were distributed in the same set. Accuracy and area under the curve (AUC) were calculated per frame and per patient from a 40% held-out test set. Results were compared to a visual analysis assessed by three experienced operators. Results: frame-based comparisons between humans and a CNN provided an accuracy of 0.605 vs. 0.746 (p < 0.0008) and an AUC of 0.630 vs. 0.824 (p < 0.0001). Patient-based comparisons provided an accuracy of 0.660 vs. 0.825 (p < 0.008) and an AUC of 0.727 vs. 0.895 (p < 0.002). Conclusion: based on cine-CMR images alone, a CNN is able to discriminate cardiac amyloidosis from LVH of other origins better than experienced human operators (15 to 20 points more in absolute value for accuracy and AUC), demonstrating a unique capability to identify what the eyes cannot see through classical radiological analysis.

Deep Learning to Classify AL versus ATTR Cardiac Amyloidosis MR Images

“…Agibetov et al obtained similar results in 82 amyloidosis vs 420 patients with various other heart diseases, with little difference between cine, LGE and T1 mapping images [23]. The classification by CNN and by expert radiologists of cine-MR images of patients with CA vs other causes of LVH showed the clear superiority of CNNs (AUC 0.825 vs. 0.727) [24]. Good performance of CNNs has also been reported for CA with bone scintigraphy and with 18 F Florbetaben [25,26].…”

“…Hyperparameters (200 Epochs, parameters of the image data generator, SGD optimizer, batch size 32, drop-out rate, learning rate 6 × 10 −5 , decay 10 −6 , number of trainable layers) were identical to those previously chosen on similar data sets [24]. Binary cross entropy was used as a loss function.…”

“…Up to now, six studies were devoted to deep learning distinction between cardiac amyloidosis and other pathological conditions, based on echocardiography [21], MRI [22][23][24] and on nuclear imaging [25,26]. Reported AUC of the ROC curve was almost always >90%, depending on the method and the studied population.…”

A Deep Learning Approach to Classify Fabry Cardiomyopathy from Hypertrophic Cardiomyopathy Using Cine Imaging on Cardiac Magnetic Resonance

“…It would be exciting news if we could achieve the same level of accuracy using only one source of imaging data through the robust feature engineering and learning abilities of artificial intelligence (AI). A prior study [36][37][38] compared our medical imaging research findings for cardiac diseases. Madani et al [36] primarily focused on the diagnosis of cardiac diseases using echocardiography images, while Zhou et al [37] and Germain et al [38] concentrated on the diagnosis of HCM and cardiac amyloidosis using cardiac cine images.…”

Editorial on Special Issue “Artificial Intelligence in Image-Based Screening, Diagnostics, and Clinical Care of Cardiopulmonary Diseases”

“…Germain et al [ 43 ] analyzed whether CNN models could supersede the performance of experienced clinicians in diagnosing Cardiac Amyloidosis (CA) using Cine-Cardiovascular cine magnetic resonance (Cine-CMR) images. This disease results in the accumulation of amyloid fibrils in cardiac tissues that might lead to progressive cardiomyopathy.…”