中国医疗器械杂志最新文献

Nursing beds can be widely used in medical institutions and households to provide medical care for patients or disabled individuals. With the increasing demand for nursing, nursing beds are developing towards intelligence and comfort. In this study, the backrest lifting mechanism of nursing beds is selected as the research object. The standards for backrest angle adjustment and the basis for comfort judgment are reviewed, and the research on various adjustment configurations are sorted in order to provide assistance to relevant research institutions in understanding current technologies and assist users in making choices.

The control strategy of rehabilitation robots should not only adapt to patients with different levels of motor function but also encourage patients to participate voluntarily in rehabilitation training. However, existing control strategies usually consider only one of these aspects. This study proposes a voluntary and adaptive control strategy that solves both questions. Firstly, the controller switched to the corresponding working modes (including challenge, free, assistant, and robot-dominant modes) based on the trajectory tracking error of human-robot cooperative movement. To encourage patient participation, a musculoskeletal model was used to estimate the patient's active torque. The robot's output torque was designed as the product of the active torque and a coefficient, with the coefficient adaptively changing according to the working mode. Experiments were conducted on two healthy subjects and four hemiplegic patients using an ankle rehabilitation robot. The results showed that this controller not only provided adaptive the robot's output torque based on the movement performance of patients but also encouraged patients to complete movement tasks themselves. Therefore, the control strategy has high application value in the field of rehabilitation.

To comprehensively meet the test requirements for the common mode rejection ratio (CMRR) across different ECG-particular standards, this paper presents the design of an ECG CMRR automatic test system. The hardware component primarily consists of a test signal generation module, an automatic control network (which includes a resistance-capacitance network control module and a polarization voltage control module), and a noise level switching module. The software portion enables automatic control and user interaction. Experimental results indicate that the system is stable, reliable, and highly automated, capable of satisfying the test requirements of various ECG-particular standards, thus demonstrating a promising application prospect.

In response to the issue that traditional lung nodule detection models cannot dynamically optimize and update with the increase of new data, a new lung nodule detection model-task incremental meta-learning model (TIMLM) is proposed. This model comprises of two loops: the inner loop imposes incremental learning regularization update constraints, while the outer loop employs a meta-update strategy to sample old and new knowledge and learn a set of generalized parameters that adapt to old and new data. Under the condition that the main structure of the model is not changed as much as possible, it preserves the old knowledge that was learned previously. Experimental verification on the publicly available lung dataset showed that, compared with traditional deep network models and mainstream incremental models, TIMLM has greatly improved in terms of accuracy, sensitivity, and other indicators, demonstrating good continuous learning and anti-forgetting capabilities.

Objective: To explore the diagnostic value of micropure imaging (MI) combined with strain elastography (SE) in correcting artificial intelligence (AI) S-Detect technology for benign and malignant breast complex cystic and solid masses.

Methods: The S-Detect diagnosis results were corrected based on the manifestations of MI and SE of the 145 breast complex cystic and solid masses. Postoperative pathological results were used as the gold standard to calculate the diagnostic sensitivity, specificity, and accuracy before and after correction. Additionally, receiver operating characteristic (ROC) curves were drawn for both groups, and the areas under the curves were compared.

Results: There were 80 benign and 65 malignant pathological results. After the correction of S-Detect, the diagnostic sensitivity, specificity, and accuracy, as well as the areas under the ROC curves, were all improved compared to before the correction.

Conclusion: Combining MI and SE to correct the diagnostic results of S-Detect can help improve the diagnostic efficacy of breast complex cystic and solid masses.

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.