Related Experiment Video
Updated: Aug 18, 2025
Evaluation of Left Ventricular Structure and Function using 3D Echocardiography
Published on: October 28, 2020
Differential diagnosis of common etiologies of left ventricular hypertrophy using a hybrid CNN-LSTM model
In-Chang Hwang1,2, Dongjun Choi3, You-Jung Choi4
1Cardiovascular Center, Seoul National University Bundang Hospital, 82 Gumi-Ro-173-Gil, Bundang, Seongnam, Gyeonggi, 13620, South Korea. inchang.hwang@gmail.com.
Insights
A new deep learning algorithm accurately differentiates causes of left ventricular hypertrophy (LVH), including hypertensive heart disease (HHD), hypertrophic cardiomyopathy (HCM), and light-chain cardiac amyloidosis (ALCA), improving diagnostic accuracy over human specialists.
Area of Science:
- Cardiology
- Artificial Intelligence
- Medical Imaging
Background:
- Differential diagnosis of left ventricular hypertrophy (LVH) on echocardiography is challenging, often necessitating extensive testing.
- Accurate differentiation of LVH etiologies like hypertensive heart disease (HHD), hypertrophic cardiomyopathy (HCM), and light-chain cardiac amyloidosis (ALCA) is crucial for patient management.
Purpose of the Study:
- To develop and evaluate a deep learning algorithm for differentiating common causes of LVH using standard echocardiographic images.
- To compare the diagnostic performance of the deep learning algorithm against expert echocardiography specialists.
Main Methods:
- A hybrid convolutional neural network-long short-term memory (CNN-LSTM) algorithm was developed using echocardiograms from 930 subjects.
- The algorithm analyzed five standard echocardiographic views to classify HHD, HCM, and ALCA.
- Diagnostic performance was assessed using the area under the receiver operating characteristic curve (AUC) and confusion matrix analysis.
Main Results:
- The deep learning algorithm achieved high average AUC values: 0.962 for HHD, 0.982 for HCM, and 0.996 for ALCA in the test set.
- The algorithm demonstrated a significantly higher overall diagnostic accuracy of 92.3% compared to echocardiography specialists (80.0% and 80.6%).
Conclusions:
- A deep learning algorithm utilizing a CNN-LSTM model and aggregate network can effectively differentiate common LVH etiologies from echocardiographic images.
- This AI-driven approach shows potential to enhance diagnostic accuracy and streamline the diagnostic process for patients with LVH.
Abstract:
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.
