Research Overview of Internet of Things Technology in Medical Engineering / 中国医疗器械杂志

Internet of Things plays a vital role in the field of healthcare. Smart medical devices, innovative sensors and lightweight communication protocols are making the Internet of Medical Things possible. This paper summarizes the research progress of Internet of Things technology in medical engineering from two aspects of health monitoring system and ingestible sensor monitoring equipment. The health monitoring system is analyzed from heart disease monitoring, diabetes monitoring and brain nerve monitoring. The medical equipment that can absorb sensors is represented by capsule endoscope. This paper further summarizes the relevant situation of smart hospital, and finally discusses the challenges and countermeasures of the Internet of Things technology in medical engineering, in order to lay the foundation and provide ideas for the research of the Internet of Things technology in medical engineering.

Segmentation of retinal vessels by fusing contour information and conditional generative adversarial / 生物医学工程学杂志

The existing retinal vessels segmentation algorithms have various problems that the end of main vessels are easy to break, and the central macula and the optic disc boundary are likely to be mistakenly segmented. To solve the above problems, a novel retinal vessels segmentation algorithm is proposed in this paper. The algorithm merged together vessels contour information and conditional generative adversarial nets. Firstly, non-uniform light removal and principal component analysis were used to process the fundus images. Therefore, it enhanced the contrast between the blood vessels and the background, and obtained the single-scale gray images with rich feature information. Secondly, the dense blocks integrated with the deep separable convolution with offset and squeeze-and-exception (SE) block were applied to the encoder and decoder to alleviate the gradient disappearance or explosion. Simultaneously, the network focused on the feature information of the learning target. Thirdly, the contour loss function was added to improve the identification ability of the blood vessels information and contour information of the network. Finally, experiments were carried out on the DRIVE and STARE datasets respectively. The value of area under the receiver operating characteristic reached 0.982 5 and 0.987 4, respectively, and the accuracy reached 0.967 7 and 0.975 6, respectively. Experimental results show that the algorithm can accurately distinguish contours and blood vessels, and reduce blood vessel rupture. The algorithm has certain application value in the diagnosis of clinical ophthalmic diseases.

The Development of Symptom Interference and Symptom Clusters of HCC / 中国实用护理杂志

The hepatocellular carcinoma (HCC) is one of the most common malignant tumors.Patients with hepatobiliary cancer suffer from multiple symptoms,such as physical symptoms and psychology symptoms.In recent years,along with the further research of symptoms,the concept of symptom clusters occurred.Those two issues have always been the hot topics in recent years.Through the efforts of experts at home and abroad,there has been a part of progress in this area.However,the results are not the same.There are lots of disputes in the numbers、sequence、incidences of the symptom clusters.The paper will discuss this issue from symptom experience and symptom clusters in order to provide the reference and basis for symptom management.