A multi-task deep learning approach for real-time view classification and quality assessment of echocardiographic images.

ABSTRACT: High-quality standard views in two-dimensional echocardiography are essential for accurate cardiovascular disease diagnosis and treatment decisions. However, the quality of echocardiographic images is highly dependent on the practitioner's experience. Ensuring timely quality control of echocardiographic images in the clinical setting remains a significant challenge. In this study, we aimed to propose new quality assessment criteria and develop a multi-task deep learning model for real-time multi-view classification and image quality assessment (six standard views and "others"). A total of 170,311 echocardiographic images collected between 2015 and 2022 were utilized to develop and evaluate the model. On the test set, the model achieved an overall classification accuracy of 97.8% (95%CI 97.

SUBMITTER: Li X 

PROVIDER: S-EPMC11372079 | biostudies-literature | 2024 Sep

REPOSITORIES: biostudies-literature