The hydrodynamic behavior of the perfusion process (cleaning) of the liver endovascular before the operation was studied to provide a theoretical guidance to the relative operations. A straight and a curved first-class vascular entity model with foreign matter and the control equations of turbulence liquid in vessel was established. With the physical parameters of a medical infusion liquid measured, an estimation method of perfusion parameters as an example, the perfusion velocity was proposed. The simulation was performed by changing technical parameters of the perfusion. Based on the control equations of turbulent liquid in vessel and the preliminarily calculated results using the vessel model, the results fitted the values of the real operation. The simulation results showed clearly the fluid dynamics behavior around the foreign matter, for example the swirling flow. The results also showed the distribution of velocity of the fluid and the wall pressure of the vessels. With the increasing velocity of the entrance perfusion, the pressure and the velocity field were increased in the two types of the vessel model. The negative wall pressure and recirculation region appeared and located in the foreign matter. Because of influence of the shape, the fluid dynamics behavior in the curved vessel model was more complicated than that in the straight vessel model. The swirling flow and the phenomenon of stagnation of the perfusion fluid were more likely to appear in the curved vessel than in the straight vessel. The most important conclusion of this paper is that the appropriate perfusion velocity can be esti-mated using the methods proposed in this paper.
Currently, the development of deep learning-based multimodal learning is advancing rapidly, and is widely used in the field of artificial intelligence-generated content, such as image-text conversion and image-text generation. Electronic health records are digital information such as numbers, charts, and texts generated by medical staff using information systems in the process of medical activities. The multimodal fusion method of electronic health records based on deep learning can assist medical staff in the medical field to comprehensively analyze a large number of medical multimodal data generated in the process of diagnosis and treatment, thereby achieving accurate diagnosis and timely intervention for patients. In this article, we firstly introduce the methods and development trends of deep learning-based multimodal data fusion. Secondly, we summarize and compare the fusion of structured electronic medical records with other medical data such as images and texts, focusing on the clinical application types, sample sizes, and the fusion methods involved in the research. Through the analysis and summary of the literature, the deep learning methods for fusion of different medical modal data are as follows: first, selecting the appropriate pre-trained model according to the data modality for feature representation and post-fusion, and secondly, fusing based on the attention mechanism. Lastly, the difficulties encountered in multimodal medical data fusion and its developmental directions, including modeling methods, evaluation and application of models, are discussed. Through this review article, we expect to provide reference information for the establishment of models that can comprehensively utilize various modal medical data.
Objective To study the relationship between central retinal thickness and retinal vascular filling state of patients with non-proliferative diabetic retinopathy (NPDR). Methods A total of 248 diabetic patients without retinopathy or with NPDR in the hospital were enrolled in the study. Only the right eye of these patients were examined by optical coherence tomography (OCT), fundus fluorescein angiography (FFA), color Doppler flow imaging (CDFI). Patients with central retinal edema, hemorrhage and exudation were excluded from this study. Central retinal thickness was measured by OCT at the points of 1 mm, 1 to 3 mm, and 3 to 6 mm from the fovea. The patients were divided into retinal thickness normal, thinning and thickening groups according to their central retinal thickness. The normal range of central retinal thickness was defined as 216.4-304.9 μm in this study. The arm retinal circulation time and retinal arterial phase and venous phase (A-V) fluorescence filling time were recorded by FFA examination. The peak systolic velocity (PSV), pulsatility index (PI) and resistance index (RI) of ophthalmic artery (OA), central retinal artery (CRA) and posterior ciliary artery (PCA) were measured by CDFI examination. The retinal fundus vascular filling state and ocular hemodynamic indexes were compared between different groups. Results The arm retinal circulation time of retinal thickness normal, thinning, thickening groups was (10.42±0.51), (10.36±0.64), (12.94±0.46) seconds respectively; the retinal A-V fluorescence filling time was (9.15±1.36), (6.36±1.15), (13.56±2.04) seconds. The difference of the arm retinal circulation time was statistically significant between the thickening and normal groups (t=1.93,P=0.04), and between the thickening and thinning groups (t=4.49,P=0.00). The retinal A-V fluorescence filling time was statistically significant between the thinning and normal groups (t=2.13,P=0.03), and between the thickening and normal groups (t=2.49,P=0.02), and between the thickening and thinning groups (t=5.38,P=0.00).The difference of PSV (t=3.290, -5.520, -4.900), PI (t=-4.310,-5.230, -4.390) and RI (t=4.970, 6.160, 5.990) of OA, CRA and PCA was statistically significant between the thickening and thinning groups (P＜0.05). Conclusion Central retinal thickness can affect the retinal vascular filling state of diabetic patients without retinopathy or with NPDR.
Wearable monitoring, which has the advantages of continuous monitoring for a long time with low physiological and psychological load, represents a future development direction of monitoring technology. Based on wearable physiological monitoring technology, combined with Internet of Things (IoT) and artificial intelligence technology, this paper has developed an intelligent monitoring system, including wearable hardware, ward Internet of Things platform, continuous physiological data analysis algorithm and software. We explored the clinical value of continuous physiological data using this system through a lot of clinical practices. And four value points were given, namely, real-time monitoring, disease assessment, prediction and early warning, and rehabilitation training. Depending on the real clinical environment, we explored the mode of applying wearable technology in general ward monitoring, cardiopulmonary rehabilitation, and integrated monitoring inside and outside the hospital. The research results show that this monitoring system can be effectively used for monitoring of patients in hospital, evaluation and training of patients’ cardiopulmonary function, and management of patients outside hospital.
The intensive care unit (ICU) is a highly equipment-intensive area with a wide variety of medical devices, and the accuracy and timeliness of medical equipment data collection are highly demanded. The integration of the Internet of Things (IoT) into ICU medical devices is of great significance for enhancing the quality of medical care and nursing, as well as for the advancement of digital and intelligent ICUs. This study focuses on the construction of the IOT for ICU medical devices and proposes innovative solutions, including the overall architecture design, devices connection, data collection, data standardization, platform construction and application implementation. The overall architecture was designed according to the perception layer, network layer, platform layer and application layer; three modes of device connection and data acquisition were proposed; data standardization based on Integrating the Healthcare Enterprise-Patient Care Device (IHE-PCD) was proposed. This study was practically verified in the Chinese People’s Liberation Army General Hospital, a total of 122 devices in four ICU wards were connected to the IoT, storing 21.76 billion data items, with a data volume of 12.5 TB, which solved the problem of difficult systematic medical equipment data collection and data integration in ICUs. The remarkable results achieved proved the feasibility and reliability of this study. The research results of this paper provide a solution reference for the construction of hospital ICU IoT, offer more abundant data for medical big data analysis research, which can support the improvement of ICU medical services and promote the development of ICU to digitalization and intelligence.
Internet of Things (IoT) technology plays an important role in smart healthcare. This paper discusses IoT solution for emergency medical devices in hospitals. Based on the cloud-edge-device architecture, different medical devices were connected; Streaming data were parsed, distributed, and computed at the edge nodes; Data were stored, analyzed and visualized in the cloud nodes. The IoT system has been working steadily for nearly 20 months since it run in the emergency department in January 2021. Through preliminary analysis with collected data, IoT performance testing and development of early warning model, the feasibility and reliability of the in-hospital emergency medical devices IoT was verified, which can collect data for a long time on a large scale and support the development and deployment of machine learning models. The paper ends with an outlook on medical device data exchange and wireless transmission in the IoT of emergency medical devices, the connection of emergency equipment inside and outside the hospital, and the next step of analyzing IoT data to develop emergency intelligent IoT applications.