1. A combined system with convolutional neural networks and transformers for automated quantification of left ventricular ejection fraction from 2D echocardiographic images
- Author
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Mingming Lin, Liwei Zhang, Zhibin Wang, Hengyu Liu, Keqiang Wang, Guozhang Tang, Wenkai Wang, and Pin Sun
- Subjects
Lightweight model ,Transformer ,Left ventricular ejection fraction ,Device integration ,Echocardiography ,Medical technology ,R855-855.5 - Abstract
Background: Accurate measurement of left ventricular ejection fraction (LVEF) is crucial in diagnosing and managing cardiac conditions. Deep learning (DL) models offer potential to improve the consistency and efficiency of these measurements, reducing reliance on operator expertise. Objective: The aim of this study was to develop an innovative software-hardware combined device, featuring a novel DL algorithm for the automated quantification of LVEF from 2D echocardiographic images. Methods: A dataset of 2,113 patients admitted to the Affiliated Hospital of Qingdao University between January and June 2023 was assembled and split into training and test groups. Another 500 patients from another campus were prospectively collected as external validation group. The age, sex, reason for echocardiography and the type of patients were collected. Following standardized protocol training by senior echocardiographers using domestic ultrasound equipment, apical four-chamber view images were labeled manually and utilized for training our deep learning framework. This system combined convolutional neural networks (CNN) with transformers for enhanced image recognition and analysis. Combined with the model that was named QHAutoEF, a ‘one-touch’ software module was developed and integrated into the echocardiography hardware, providing intuitive, real-time visualization of LVEF measurements. The device's performance was evaluated with metrics such as the Dice coefficient and Jaccard index, along with computational efficiency indicators. The dice index, intersection over union, size, floating point operations per second and calculation time were used to compare the performance of our model with alternative deep learning architectures. Bland-Altman analysis and the receiver operating characteristic (ROC) curve were used for validation of the accuracy of the model. The scatter plot was used to evaluate the consistency of the manual and automated results among subgroups. Results: Patients from external validation group were older than those from training group ((60±14) years vs. (55±16) years, respectively, P < 0.001). The gender distribution among three groups were showed no statistical difference (43 % vs. 42 % vs. 50 %, respectively, P = 0.095). Significant differences were showed among patients with different type (all P < 0.001) and reason for echocardiography (all P
- Published
- 2025
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