The multi-task deep learning model achieved an overall view classification accuracy of 97.8% and a mean absolute error of 6.54 for image quality assessment on the test set.
Does a multi-task deep learning model accurately classify echocardiographic views and assess image quality in real-time compared to expert annotations?
A multi-task deep learning model can accurately and rapidly classify echocardiographic views and assess image quality, potentially standardizing clinical image acquisition and improving AI-assisted diagnosis.
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 97.7-98.0) and a mean absolute error of 6.54 (95%CI 6.43-6.66). A single-frame inference time of 2.8 ms was achieved, meeting real-time requirements. We also analyzed pre-stored images from three distinct groups of echocardiographers (junior, senior, and expert) to evaluate the clinical feasibility of the model. Our multi-task model can provide objective, reproducible, and clinically significant view quality assessment results for echocardiographic images, potentially optimizing the clinical image acquisition process and improving AI-assisted diagnosis accuracy.
Li et al. (2024) studied Echocardiographic imaging (n=170,311). Multi-task deep learning model vs. Expert annotations was evaluated on Overall view classification accuracy (95% CI 97.7-98.0). The multi-task deep learning model achieved an overall view classification accuracy of 97.8% and a mean absolute error of 6.54 for image quality assessment on the test set.