Abstract:

Objective To Explore the feasibility and value of deep learning technology for quality control of echocardiography images. Methods A total of 180985 echocardiography images collected from Sichuan Provincial People's Hospital between 2015 and 2022 were selected to establish the experimental dataset. Two task models of the echocardiography standard views quality assessment method were trained， including intelligent recognition of seven types of views （six standard views and other views） and quality scoring of six standard views. The predictions of the models on the test set were compared with the results of the sonographer's annotation to assess the accuracy， feasibility， and timeliness of the runs of the two models. Results The overall classification accuracy of the standard views recognition model was 98.90%， the precision was 98.17%， the recall was 98.18% and the F1 value was 98.17%， with the classification results close to the expert recognition level； the average PLCC of the six standard views quality scoring models was 0.933， the average SROCC was 0.929， the average RMSE was 7.95 and the average MAE was 4.83， and the prediction results were in strong agreement with the expert scores. The single-frame inference time after deployment on the 3090 GPU was less than 20 ms， meeting real-time requirements. Conclusion The echocardiography standard views quality assessment method can provide objective and accurate quality assessment results， promoting the development of echocardiography image quality control management towards real-time， objective， and intelligent.

Key words: echocardiography, deep learning, quality control, view recognition, quality assessment