Speeding up echocardiographic examination by automated generation of 2D imaging views from 3D volumetric data using machine learning

Abstract Background 3D echocardiographic data contain infinite 2D echocardiographic planes generated by multiplanar reconstruction, but the process is labor-intensive and time-consuming. Artificial intelligence (AI) utilizing cutting-edge machine learning techniques may improve the accuracy and efficiency of the process. In the study, we developed and validated an AI software that automatically reconstructs 7 2D views recommended by the American Society of Echocardiography from 3D echocardiographic data. Method Convolutional neural network (CNN) architecture utilizing Spatial Configuration Net was used as the machine learning model. We identify a minimum of 3 key feature landmarks on each 2D cross-sectional plane, with the 3D coordinates representing 2D cross-sectional planes accordingly. Subsequently, these landmarks are accurately labeled within a 3D space. 31 landmarks were identified across 7 2D cross-sectional planes and a total of 472 3D volume data were labelled. The performance of the model is evaluated by Root Mean Square Error (RMSE) of the predicted landmark coordinates. Results The mean RMSE over 31 predicted landmarks evaluated in testing is 2.70% for the input 3D volume dimensions. The average time used to generate the 7 2D imaging views was 19.91±3.19 seconds (figure). Conclusion The AI algorithm can efficiently convert 3D echocardiographic data into guideline-recommended 2D images, which shortens the length of echo examination but still preserves the accuracy.AI generation of 2D views from 3D echo