Automatic Myocardial Contrast Echocardiography Image Quality Assessment Using Deep Learning: Impact on Myocardial Perfusion Evaluation

Objective The image quality of myocardial contrast echocardiography (MCE) is critical for precise myocardial perfusion evaluation but challenging for echocardiographers. Differences in quality may lead to diagnostic heterogeneity. This study was aimed at achieving automatic MCE image quality assessment using a deep neural network (DNN) and investigating its impact on myocardial perfusion evaluation. Methods The Resnet-18 model was used for training and testing on internal and external data sets. Quality assessment involved three aspects: left ventricular opacification (LVO), shadowing, and flash adequacy; the quality score was calculated based on image quality. This study explored the impact of the DNN-based quality score on perfusion evaluation (normal, delay or obstruction) by echocardiographers (two seniors, one junior and one novice). Additionally, the effect of the score difference between re-scans on perfusion evaluation was investigated. Results The time cost for DNN prediction was 0.045 s/frame. In internal validation and external testing, the DNN achieved F1 and macro F1 scores >90% for quality assessment and had high intraclass correlation coefficients (0.954 and 0.892, respectively) in sequence quality scores. The proportion of segments deemed uninterpretable increased as the DNN-based quality score decreased. The agreement of perfusion assessment between one senior and others decreased as the quality score decreased. And the greater the score difference between the re-scans, the lower was the agreement on perfusion assessment by the same echocardiographer. Conclusion This study determined the effectiveness of DNN for real-time automatic MCE quality assessment. It has the potential to reduce the variability in perfusion evaluation among echocardiographers. The image quality of myocardial contrast echocardiography (MCE) is critical for precise myocardial perfusion evaluation but challenging for echocardiographers. Differences in quality may lead to diagnostic heterogeneity. This study was aimed at achieving automatic MCE image quality assessment using a deep neural network (DNN) and investigating its impact on myocardial perfusion evaluation. The Resnet-18 model was used for training and testing on internal and external data sets. Quality assessment involved three aspects: left ventricular opacification (LVO), shadowing, and flash adequacy; the quality score was calculated based on image quality. This study explored the impact of the DNN-based quality score on perfusion evaluation (normal, delay or obstruction) by echocardiographers (two seniors, one junior and one novice). Additionally, the effect of the score difference between re-scans on perfusion evaluation was investigated. The time cost for DNN prediction was 0.045 s/frame. In internal validation and external testing, the DNN achieved F1 and macro F1 scores >90% for quality assessment and had high intraclass correlation coefficients (0.954 and 0.892, respectively) in sequence quality scores. The proportion of segments deemed uninterpretable increased as the DNN-based quality score decreased. The agreement of perfusion assessment between one senior and others decreased as the quality score decreased. And the greater the score difference between the re-scans, the lower was the agreement on perfusion assessment by the same echocardiographer. This study determined the effectiveness of DNN for real-time automatic MCE quality assessment. It has the potential to reduce the variability in perfusion evaluation among echocardiographers.