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

• Published in: Scientific reports Vol. 12; no. 1; pp. 20998 - 12
• Main Authors: Hwang, In-Chang, Choi, Dongjun, Choi, You-Jung, Ju, Lia, Kim, Myeongju, Hong, Ji-Eun, Lee, Hyun-Jung, Yoon, Yeonyee E., Park, Jun-Bean, Lee, Seung-Pyo, Kim, Hyung-Kwan, Kim, Yong-Jin, Cho, Goo-Yeong
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 05.12.2022; Nature Publishing Group; Nature Portfolio
• Subjects: 692/308; 692/4019; Algorithms; Amyloidosis; Cardiomyopathy; Cardiomyopathy, Hypertrophic - complications; Cardiomyopathy, Hypertrophic - diagnostic imaging; Cardiovascular diseases; Deep learning; Diagnosis, Differential; Differential diagnosis; Echocardiography; Echocardiography - adverse effects; Etiology; Heart diseases; Heart Diseases - diagnosis; Humanities and Social Sciences; Humans; Hypertension; Hypertrophy; Hypertrophy, Left Ventricular - diagnostic imaging; Hypertrophy, Left Ventricular - etiology; Long short-term memory; multidisciplinary; Neural networks; Neural Networks, Computer; Population studies; Science; Science (multidisciplinary); Training; Ventricle
• ISSN: 2045-2322; 2045-2322
• DOI: 10.1038/s41598-022-25467-w

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 light-chain cardiac amyloidosis [ALCA]) on echocardiographic images. 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. 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%). In the present study, 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. The high diagnostic performance of our deep learning algorithm suggests that the use of deep learning can improve the diagnostic process in patients with LVH.