Cardiovascular disease is one of the important factors that threaten the health of residents, ranking the first among various causes of death, so the monitoring and diagnosis of human cardiovascular health is particularly important. Compared with traditional brachial artery pressure, central arterial pressure (CAP) has a higher correlation with the occurrence of many cardiovascular events. The measurement of CAP can more accurately reflect the real situation of human blood pressure, and provide an important basis for diagnosis and disease prevention. Therefore, the realization of high-precision, high-generalization ability and low-cost non-invasive measurement of CAP has always been the research focus in this field. This article combines the relevant literature in China and abroad to summarize the current status of CPA measurement, introduces related research progress from two aspects, namely parameter measurement and waveform measurement, and discusses the characteristics of the existing methods and the future development.

Lung diseases such as lung cancer and COVID-19 seriously endanger human health and life safety, so early screening and diagnosis are particularly important. computed tomography (CT) technology is one of the important ways to screen lung diseases, among which lung parenchyma segmentation based on CT images is the key step in screening lung diseases, and high-quality lung parenchyma segmentation can effectively improve the level of early diagnosis and treatment of lung diseases. Automatic, fast and accurate segmentation of lung parenchyma based on CT images can effectively compensate for the shortcomings of low efficiency and strong subjectivity of manual segmentation, and has become one of the research hotspots in this field. In this paper, the research progress in lung parenchyma segmentation is reviewed based on the related literatures published at domestic and abroad in recent years. The traditional machine learning methods and deep learning methods are compared and analyzed, and the research progress of improving the network structure of deep learning model is emphatically introduced. Some unsolved problems in lung parenchyma segmentation were discussed, and the development prospect was prospected, providing reference for researchers in related fields.
COVID-19 ; Humans ; Lung/diagnostic imaging* ; Machine Learning ; SARS-CoV-2 ; Tomography, X-Ray Computed