Differential diagnosis of common etiologies of left ventricular hypertrophy using a hybrid CNN-LSTM model

Background: Differential diagnosis of left ventricular hypertrophy (LVH) is often obscure on echocardiography and requires numerous additional tests. We aimed to develop a deep learning algorithm to aid in the differentiation of common etiologies of LVH (i.e., hypertensive heart disease [HHD], hypertrophic cardiomyopathy [HCM], and cardiac amyloidosis [CA]) on echocardiographic images. Methods: Echocardiograms in 5 standard views (parasternal long-axis, parasternal short-axis, apical 4-chamber, apical 2-chamber, and apical 3-chamber) were obtained from 930 subjects: 112 with HHD, 191 with HCM, 81 with ALCA, and 546 normal subjects. The study population was divided into training (n=620), validation (n=155), and test sets (n=155). A convolutional neural network-long short-term memory (CNN-LSTM) algorithm was constructed to independently classify the 3 diagnoses on each view, and the final diagnosis was made by an aggregate network based on the simultaneously predicted probabilities of HCM, HCM, and ALCA. Diagnostic performance of the algorithm was evaluated by the area under the receiver operating characteristic curve (AUC), and accuracy was evaluated by the confusion matrix. Results: The deep learning algorithm was trained and verified using the training and validation sets, respectively. In the test set, the average AUC across the five standard views was 0.962, 0.982, and 0.996 for HHD, HCM, and CA, respectively. The overall diagnostic accuracy was significantly higher for the deep learning algorithm (92.3%) than for echocardiography specialists (80.0% and 80.6%). Conclusions: We developed a deep learning algorithm for the differential diagnosis of 3 common LVH etiologies (HHD, HCM, and ALCA) by applying a hybrid CNN-LSTM model and aggregate network to standard echocardiographic images.