中国医疗器械杂志最新文献

At present, research on the efficacy of local physical cooling devices is mainly based on clinical observation, but there is relatively little research on evaluating the effectiveness of local cold therapy cooling and the penetration depth. This study is based on the research of the structure and morphology of local muscle tissue in the human body, as well as the heat transfer characteristics and mechanisms of the human body. A simulation phantom of human muscle tissue under temperature cycling was created, and the differences in evaluating the effectiveness of local cold therapy between the human body and the simulation phantom were compared. This provides a new evaluation method for evaluating the cooling effectiveness of local physical cooling equipment.

Neural oscillatory changes play a critical role in pain and analgesia research. Previous studies on pain-related neural oscillations have primarily utilized electroencephalogram (EEG) power spectral analysis, revealing a strong correlation between alpha ( α) power and subjective pain perception. However, alpha power may be influenced by the baseline of the power spectrum, making it difficult to accurately capture the true changes in alpha oscillations. This study employed power spectral analysis and further applied a power spectral parameterization method, which decomposed the power spectrum into periodic and aperiodic components, to compare EEG α power in 50 primiparous women who underwent severe pain during the first stage of labor before and after epidural analgesia. The results indicated no significant differences in α power between pre- and post-analgesia conditions. However, following power spectral parameterization, the aperiodic component of the EEG significantly decreased after analgesia, whereas the periodic component of α power showed a significant increase. This study not only validates the effectiveness and validity of the power spectral parameterization method in analgesia research but also uncovers the differential regulatory mechanism by which analgesia modulates the periodic and aperiodic components of α oscillations.

Cleaning process validation is an important guarantee for implantable medical devices to ensure product cleanliness. In the manufacturing process, implantable devices usually need to control pollution through cleaning process to achieve a certain cleanliness to ensure the effectiveness of sterilization and product safety. This paper discusses the cleaning process validation of the manufacturing process of the implantable devices based on relevant regulations and technical standards, and proposes the influencing factors and principles to be considered in the cleaning process validation, so as to provide technical reference for the design of the cleaning process validation.

Objective: To enhance the reliability of medical equipment, this study aims to develop a failure cause diagnosis model and provide rational suggestions for efficient equipment use.

Methods: Combine fault tree analysis (FTA) to identify basic events causing equipment failure and calculate their prior probabilities. Obtain conditional probability tables for each node through expert assessment. Integrate triangular fuzzy number theory with Bayesian network (BN) to construct a fuzzy Bayesian network (FBN) for posterior probability inference and sensitivity analysis.

Results: Using endoscopes as the subject, the analysis shows that the model accurately calculates the endoscope failure probability at 0.385%, and identifies the key causes: improper cleaning ( X5, posterior probability 0.36064), untimely fault detection ( X8, posterior probability 0.23571), irregular transportation ( X6, posterior probability 0.11344), and natural aging ( X10, posterior probability 0.11377). Sensitivity analysis also confirms their influence weights (mutual information values are 0.00749, 0.00591, 0.00202, 0.00174).

Conclusion: The model can accurately perform quantitative analysis and rapid fault location of medical equipment failures, enabling effective preventive measures.

Medical robotic arm often encounters multi-source and nonlinear errors during the calibration process, making it difficult for traditional mathematical modeling methods to fully characterize system error features, thereby limiting further improvement in calibration accuracy. In this study, a robotic arm parameter error identification model is established, and a calibration method based on an adaptive long short-term memory (ALSTM) neural network is proposed. The method incorporates a particle swarm optimization (PSO) algorithm to optimize the weights of each layer of the LSTM neural network, enabling more effective fitting of robotic arm kinematic errors and ultimately yielding more accurate Denavit-Hartenberg (D-H) parameters. To validate the proposed approach, 110 sets of experimental data are collected using the HSR-JR680 robotic arm calibration system. Experimental results demonstrate that the ALSTM model reduces the root mean square error (RMSE) by 23.07%-80.39% compared to traditional calibration methods, and shortens the convergence time by 32.44% compared to a standard LSTM model. The optimized D-H parameters obtained meet the high-precision calibration requirements of medical robotic arm, confirming the effectiveness of the proposed method.

The nuclear magnetic resonance (NMR) thermal control subsystem described in this paper is a newly developed platform-based thermal management solution. It presents critical challenges in system architecture design and core component selection. Therefore, it is necessary to carry out sufficient reliability design and verification work in the design and development stage to ensure its high quality and high reliability. Firstly, based on the statistical and analytical calculation of the client installation data of the benchmark product, the reliability metric of the new research subsystem is determined, and its usage and environmental profile are defined. Secondly, for the reliability metric to be verified, the test based on reliability growth and reliability demonstration is planned and implemented. A customized test platform is built, relevant reliability test parameters are designed, and a long-term test monitoring and recording mechanism is established to ensure the high-quality implementation of reliability tests. Finally, a detailed analysis is carried out on the design defects and failure modes exposed during the reliability test, and corresponding control measures are proposed to achieve the engineering goal of improving product robustness throughout its life cycle.

The peripheral pulmonary lesions are located far from the central airway and close to the pleura, so it is a challenge for clinical diagnosis of their nature through biopsy. Therefore, the Ion robot-assisted bronchoscopy system which has started its commercialization in China is proposed to diagnose and treat peripheral pulmonary lesions. The Ion system can be used for navigation, registration, biopsy and treatment. In this paper, the structural principle of the Ion system is expounded, and its technical advantages such as shape perception, slender catheter and flexible operation are summarized. It represents the latest development direction of the diagnosis and treatment of peripheral pulmonary lesions. Then, the clinical application and development status of the Ion system are analyzed and discussed in detail. Finally, the development trend of the robot-assisted bronchoscopy system is prospected, which provides new ideas for realizing the "integrated and one-stop" diagnosis and treatment services for peripheral pulmonary lesions based on this system.

Electrical impedance tomography (EIT) represents an emerging medical functional imaging technology, which operates by applying safe-to-human excitation currents through surface-mounted electrodes, measuring boundary voltages between electrodes, and selecting appropriate image reconstruction algorithms to visualize resistivity in tomographic cross-sections. Compared to traditional medical imaging techniques, EIT offers non-invasive and radiation-free operation, high sensitivity to tissue resistivity changes, and superior temporal resolution, meeting the real-time requirements of clinical dynamic condition monitoring. This paper comprehensively reviews the research status of brain EIT technology, systematically summarizes its advantages and technical limitations in perioperative applications, and prospectively forecasts future development directions of perioperative brain EIT based on current research foundations and clinical application demands, with the aim of providing methodological references for further optimization and clinical promotion of this technology.

As representatives of the third generation of biomedical materials, hydrogels exhibit revolutionary potential in tissue engineering, precision drug delivery, and smart medical devices due to their ability to construct bionic microenvironments. However, the clinical translation of hydrogels is still limited by multidimensional challenges, including biocompatibility, scalable production, and regulatory complexity. This paper systematically reviews the design innovations, functionalization strategies, and translational bottlenecks of hydrogel materials, integrates the latest technological trends, such as 4D printing and AI-driven design, and proposes a collaborative optimization pathway encompassing materials, technology, clinical applications, and policy. By introducing local Chinese innovation cases and monitoring scientific advancements, this study offers solutions that possess both academic significance and practical guidance for the clinical translation of hydrogels.

Objective: This study aims to develop a regional collaborative hierarchical diagnosis and treatment model based on the "Internet+" approach, to address issues such as the uneven distribution of transcranial magnetic stimulation (TMS) treatment resources, information silos, and low patient accessibility in regional medical institutions.

Methods: This model establishes standardized business and information protocols, creating a real-time TMS treatment resource database, develops a regional TMS treatment management platform, and integrates with the Xiamen Health Medical Cloud Platform for collaborative operation.

Results: This model enables the internal communication of TMS treatment information within hospitals and sharing across medical institutions, optimizing the rational allocation of TMS treatment resources.

Conclusion: The model effectively optimizes the allocation of TMS treatment resources, significantly enhances the accessibility and quality of medical services, provides valuable insights for hierarchical diagnosis and treatment of other therapeutic models, and contributes to the development of a more organized and efficient hierarchical diagnosis and treatment system.