Limitations
This study had several limitations. It was a single-center study due to its data collected from the same medical system. In this study, we enrolled patients who underwent coronary angiography with typical myocardial ischemia-related symptoms or positive results of examinations. In addition, a single echocardiogram vendor and post-processing algorithm were applied. All of those may increase the instability of the model, resulting in low generalization of the results. Therefore, further studies with multi-center data will be considered, in which the method of an adaptive learning process could be included, enabling the model to update when inputting new samples automatically. The semi-automatic speckle tracking analysis was another limitation of our study. Because of this, the different physicians' subjective opinions may also influence the final prediction. The difficulty to recognize the epicardial or endocardial border by EchoPac (in case of processing low-quality images) was also a critical issue in our model, which could bring certain biases to the results. To address this issue, it is necessary to develop an automatic image quality control and tracing technique to analyze echocardiogram data. To effectively reduce subjective errors, some efforts should be made to reduce user intervention in image feature extraction and classification analysis. Because our overall follow-up time was limited, we have only discovered a few trends thus far. We will continue to follow up with these patients. A risk stratification assessment model will be established based on the prognosis of those patients.