中国医疗器械杂志最新文献

Objective: Strengthen the legal, compliant, and rational use of medical equipment and further guide the rationalization of medical behaviors.

Methods: By utilizing the Internet of Things (IoT) and image analysis technology, collect real-time operation data of the equipment, establish a real-time running database for medical equipment, and cooperate with the 12 key links of the "whole life" of the equipment and the 8+6 management system framework to implement lean management of the efficiency, benefit, and effectiveness of medical equipment usage.

Results: It realizes the improvement of the quality and efficiency of medical equipment, cost reduction and cost control, and provides data support for scientific decision-making.

Conclusion: This study innovates the management model for the entire life cycle of medical equipment, providing a scientific approach to the management of hospital equipment.

From the perspective of the performance evaluation, concerns of the range of pathogens, establishment of enterprise reference material, reaction system study and analytical performances evaluation of central nervous system infection pathogen metagenome sequencing reagent are briefly described, including study methods and quality control requirements. This study is intended to increase the research and development efficiency of products, and contribute to the development of associated industry.

Electroencephalogram (EEG) is a non-invasive measurement method of brain electrical activity. In recent years, single/few-channel EEG has been used more and more, but various types of physiological artifacts seriously affect the analysis and wide application of single/few-channel EEG. In this paper, the regression and filtering methods, decomposition methods, blind source separation methods and machine learning methods involved in the various physiological artifacts in single/few-channel EEG are reviewed. According to the characteristics of single/few-channel EEG signals, hybrid EEG artifact removal methods for different scenarios are analyzed and summarized, mainly including single-artifact/multi-artifact scenes and online/offline scenes. In addition, the methods and metrics for validating the performance of the algorithm on semi-simulated and real EEG data are also reviewed. Finally, the development trend of single/few-channel EEG application and physiological artifact processing is briefly described.

In magnetic resonance examination, the interaction between implants and the radio frequency (RF) fields induces heating in human tissue and may cause tissue damage. To assess the RF-induced heating of implants, three steps should be executed, including electromagnetic model construction, electromagnetic model validation, and virtual human body simulations. The crucial step of assessing RF-induced heating involves the construction of a test environment for electromagnetic model validation. In this study, a hardware environment, comprised of a RF generation system, electromagnetic field measurement system, and a robotic arm positioning system, was established. Furthermore, an automated control software environment was developed using a Python-based software development platform to enable the creation of a high-precision automated integrated test environment. The results indicate that the electric field generated in this test environment aligns well with the simulated electric field, making it suitable for assessing the RF-induced heating effects of implants.

The management of in vitro diagnostic (IVD) reagents in hospitals often faces issues such as the lack of a unified coding system, unclear consumption patterns, and unknown cost-to-income ratios. It is necessary to employ information systems to achieve comprehensive, detailed, and traceable management of IVD reagents. An information management system for IVD reagents based on unique coding is introduced, which integrates admission, acceptance, and consumption processes through unique codes. The system calculates the income per experimental item based on the consumption of IVD reagents and the charge for each experimental item. The system enhances the efficiency of the IVD reagent supply chain management and promotes detailed oversight of IVD reagent usage.

Additive manufacturing (3D printing) technology aligns with the direction of precision and customization in future medicine, presenting a significant opportunity for innovative development in high-end medical devices. Currently, research and industrialization of 3D printed medical devices mainly focus on nondegradable implants and degradable implants. Primary areas including metallic orthopaedic implants, polyether-ether-ketone (PEEK) bone implants, and biodegradable implants have been developed for clinical and industrial application. Recent research achievements in these areas are reviewed, with a discussion on the additive manufacturing technologies and applications for customized implants. Challenges faced by different types of implants are analyzed from technological, application, and regulatory perspectives. Furthermore, prospects and suggestions for future development are outlined.

An analog PID controller-based galvanometer scanner is widely used by fractional laser medical equipment (FLME) to scan lasers across tissue surfaces, achieving the desired therapeutic effect. This type of driver, primarily composed of passive components and operational amplifiers, can only accept commands from the central controller of the FLME, with a simple hardware circuit-based fault diagnosis; thus, the safety of the FLME is compromised. To address these issues, the failure mechanisms of galvanometers and their impact on the safety of FLME are thoroughly analyzed first. Then, an adaptive limit protection method, a coil open circuit fault diagnosis, a communication timeout protection based on two handshakes, and a galvanometer control timeout protection are proposed, respectively, based on a digital driver platform, to supplement the deficiencies in the original fault diagnosis and protection system. This ensures the safety of the FLME. Finally, the effectiveness of the proposed strategies is validated through experiments.

The development of portable medical devices cannot be separated from safe and efficient batteries. Accurately predicting the remaining life of batteries can greatly improve the reliability of batteries, which is of great significance for portable medical devices. This article focuses on the high dependence of the BP neural network algorithm on initial weights and thresholds, as well as its tendency to fall into local minima. The Northern Goshawk Optimization (NGO) algorithm is used to optimize the BP neural network and to test the 18650 lithium battery data under different ambient temperatures (4, 24, 43°C) typical of medical equipment. The experimental results show that the NGO algorithm can significantly improve the prediction accuracy of the BP neural network under various temperature conditions, achieving accurate and effective prediction of the remaining battery life.

PPG (photoplethysmography) holds significant application value in wearable and intelligent health devices. However, during the acquisition process, PPG signals can generate motion artifacts due to inevitable coupling motion, which diminishes signal quality. In response to the challenge of real-time detection of motion artifacts in PPG signals, this study analyzed the generation and significant features of PPG signal interference. Seven features were extracted from the pulse interval data, and those exhibiting notable changes were filtered using the dual-sample Kolmogorov-Smirnov test. The real-time detection of motion artifacts in PPG signals was ultimately based on decision trees. In the experimental phase, PPG signal data from 20 college students were collected to formulate the experimental dataset. The experimental results demonstrate that the proposed method achieves an average accuracy of (94.07±1.14)%, outperforming commonly used motion artifact detection algorithms in terms of accuracy and real-time performance.

The study provides an overview of the development status of sleep disorder monitoring devices. Currently, polysomnography (PSG) is the gold standard for diagnosing sleep disorders, necessitating multiple leads and requiring overnight monitoring in a sleep laboratory, which can be cumbersome for patients. Nevertheless, the performance of PSG has been enhanced through research on sleep disorder monitoring and sleep staging optimization. An alternative device is the home sleep apnea testing (HSAT), which enables patients to monitor their sleep at home. However, HSAT does not attain the same level of accuracy in sleep staging as PSG, rendering it inappropriate for screening individuals with asymptomatic or mild obstructive sleep apnea-hypopnea syndrome (OSAHS). The study suggests that establishing a Chinese sleep staging database and developing home sleep disorder monitoring devices that can serve as alternatives to PSG will represent a future development direction.