Muzi Li^1,4, Hong Meng^1,4,*, Liang Zhang², Yuzi Zhou³, Chao Liang⁴, Zhiling Luo⁴, Hao Wang^1,*

Congenital Heart Disease, Vol.17, No.3, pp. 231-244, 2022, DOI:10.32604/chd.2022.019480 - 03 May 2022

Abstract Background: Children are at risk of extubation failure after congenital heart disease surgery. Such cases should be identified to avoid possible adverse consequences of failed extubation. This study aimed to identify ultrasound predictors of successful extubation in children who underwent cardiac surgery. Methods: Children aged 3 months to 6 years who underwent cardiac surgery (if they were intubated for >6 h and underwent a spontaneous breathing trial) were included in this study. Results: We included 83 children who underwent surgery for congenital heart disease. Transthoracic echocardiography and lung ultrasound were performed immediately before spontaneous breathing trials. Upon… More >

Wenqian Li¹, Xing Deng^1,2,*, Haijian Shao¹, Xia Wang³

CMES-Computer Modeling in Engineering & Sciences, Vol.129, No.1, pp. 65-98, 2021, DOI:10.32604/cmes.2021.016981 - 24 August 2021

Abstract The COVID-19 has resulted in catastrophic situation and the deaths of millions of people all over the world. In this paper, the predictions of epidemiological propagation models, such as SIR and SEIR, are introduced to analyze the earlier COVID-19 propagation. The deep learning methods combined with transfer learning are familiar with classification-detection approaches based on chest X-ray and CT images are presented in detail. Besides, deep learning approaches have also been applied to lung ultrasound (LUS), which has been shown to be more sensitive than chest X-ray and CT images in detecting COVID-19. In the… More > Graphic Abstract