Abstract
The primary cause of coronary heart disease is abnormal myocardial circulatory perfusion. Microcirculation is the key link of myocardial oxygen supply and plays a major role in myocardial blood supply. We intended to employ myocardial load contrast enhanced ultrasound (MCSE) coupled with artificial intelligence to accurately evaluate the microcirculation of patients. (1) Based on the convolutional neural network, the framework of myocardial vessel extraction was constructed to extract myocardial vessels and accurately monitor the myocardium. (2) From the perspective of visual perception, the salient region algorithm was proposed to identify the perfusion signals according to the texture features and grayscale features, and the quantitative indexes of myocardial perfusion were obtained. (3) Combined with the imaging features and clinical features, the early diagnosis, efficacy evaluation, and risk stratification of coronary heart disease microcirculation disorders were evaluated. The results of this study lead to effectively evaluating and predicting myocardial microcirculation with AOM reaching 84% and the algorithm can aid medical professionals in diagnosis and therapy.
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Acknowledgements
This work is supported by Key project of Shaanxi international science and technology cooperation foundation (2022kwz-19), Xi'an Science and technology project (21YXYJ0105). Postdoctoral Science Foundation of China (2020M682144) and Science and Technology Rising Star of Shaanxi Youth (2021KJXX-61).
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Tian, M., Zheng, M., Qiu, S. et al. A prediction model of microcirculation disorder in myocardium based on ultrasonic images. J Ambient Intell Human Comput 14, 7319–7330 (2023). https://doi.org/10.1007/s12652-022-04440-5
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DOI: https://doi.org/10.1007/s12652-022-04440-5