Dual-flow convolutional neural network for automatic measurement of left ventricular ejection fraction and global longitudinal strain in echocardiography.
Authors
Affiliations (9)
Affiliations (9)
- Department of Ultrasound Medicine, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
- Clinical Research Center for Medical Imaging in Hubei Province, Wuhan, China.
- Hubei Province Key Laboratory of Molecular Imaging, Wuhan, China.
- Media and Communication Lab (MC lab), Electronics and Information Engineering Department, Huazhong University of Science and Technology, Wuhan, China.
- Department of Cardiovascular Ultrasound, The First Affiliated Hospital of China Medical University, Shenyang, China.
- Department of Ultrasound Medicine, The Second Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, China.
- Department of Ultrasound Medicine, The Affiliated Hospital of Inner Mongolia Medical University, Hohhot, China.
- Department of Cardiovascular Ultrasound, The First Hospital of Jilin University, Changchun, China.
- Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
Abstract
Left ventricular ejection fraction (LVEF) and global longitudinal strain (GLS) are essential for the diagnosis, clinical decision-making, and prognosis of cardiovascular disease. However, accurate assessments of LVEF and GLS by echocardiography are hampered by inter-observer variability, time-consuming, and labor-intensive. This study aimed to develop an automated method to accurately and rapidly assess LVEF and GLS. Based on the datasets of 500 patients (1,500 videos) from the internal center and 363 patients (1,089 videos) from four external centers, we successfully developed a dual-flow convolutional neural network called Echo-DFCNN, which allowed for synchronous acquisition of LVEF and GLS. We evaluated the performance of the Echo-DFCNN in a cardiac magnetic resonance (CMR) validation dataset composed of 67 patients. On the internal test dataset, the AI and manual measurements of LVEF demonstrated a median absolute error of 3.02% and a mean absolute error of 3.94%. AI-predicted LVEF showed good agreement with manually measured LVEF, with an ICC of 0.927, a bias of 0.89%, and a LOA of -10.91 to 12.69. For GLS, the median absolute error and mean absolute error between AI and manual measurements were 1.43% and 1.83%. AI-predicted GLS exhibited high agreement with manually measured GLS (ICC = 0.913; bias = -1.22%, LOA = -5.12 to 2.68). In addition, Echo-DFCNN maintained good performance when applied to external validation datasets. In the CMR validation dataset, the AI model showed good agreement with CMR measurements for both LVEF and GLS. Echo-DFCNN achieves simultaneous and precise assessment of LVEF and GLS in the study cohorts, demonstrating its potential for robust performance across a wide range of cardiac functions, different image qualities, and machine types.