To explore the feasibility of echocardiographic image quality evaluation by deep learning based Multi-scale Image Quality Evaluation Transformer network (MUSIQ).

We selected 70181 echocardiographic apical four-chamber images of 457 patients who underwent echocardiographic examination at Sichuan Provincial People's Hospital from December 2021 to April 2022. According to the quality of the image section, two senior specialists scored the images from 0 (very poor) to 5 (excellent). The images were divided into a training set, a verification set, and a test set in a ratio of 8∶1∶1. Then, we used the MUSIQ models for network training and verification, and selected the model with the best comprehensive performance in the verification set. The prediction scores of the MUSIQ model in the test set were compared with the scores given by professional doctors, and the diagnostic performance of the model was then evaluated in terms of precision, recall, and F1 scores.

The average precision, recall, and F1 scores of the model in predictions for scores between 0 and 5 were 0.941, 0.941, and 0.941, respectively, all of which were all at a high level. The inference time of the model on GPU was 18 ms for single frame images with a size of 600×800, which meets the requirement of real-time processing.

The intelligent assessment of apical four-chamber cardiac image quality by MUSIQ based on deep learning has a high degree of consistency with ultrasonographers' manual scoring results and is highly feasible. As the method does not make any antecedent assumptions for apical four-chamber cardiac sections, it can be extended to any standard ultrasound section in principle, which is conducive to the standardization of intelligent assessment of ultrasound image quality.