中国医疗器械杂志最新文献

This research utilizes a deep learning-based image generation algorithm to generate pseudo-sagittal STIR sequences from sagittal T1WI and T2WI MR images. The evaluations include both subjective assessments by two physicians and objective analyses, measuring image quality through SNR and CNR in ROIs of five different tissues. Further analyses, including MAE, PSNR, SSIM, and COR, establish a strong correlation between the generated STIR sequences and the gold standard, with Bland-Altman analysis indicating pixel consistency. The findings indicate that the deep learning-generated STIR sequences not only align with but potentially surpass the gold standard in terms of image quality and clinical diagnostic capabilities. Moreover, the approach demonstrates promise for clinical implementation, offering reduced scan time and enhanced imaging efficiency.

Needle-free jet injectors refer to a kind of medical device that uses a specific device to form a small, high-speed jet of medication to pierce the human skin, thereby achieving the delivery of medication into the human body without the use of needles. In the past few decades, needle-free jet injectors have undergone many changes with the development of healthcare systems and advancements in related technologies. In this article, the history, research status, and clinical application of needle-free jet injectors are introduced. The principles of different driving modes for needle-free jet injectors are briefly summarized, and their respective advantages and the existing problems are summarized. Combining the current research status and market application, the technical problems faced by the development of needle-free jet injectors are analyzed. Under the background of intelligent and automatic development of medical equipment, the future development and opportunities for needle-free jet injectors are prospected.

Objective: A wearable wireless chest patch monitoring terminal is designed to realize the acquisition, processing, and wireless transmission of ECG, respiration, and body temperature signals.

Methods: The analog front-end ADS1292R, which integrates respiratory impedance and ECG front-end, is utilized to collect human ECG and respiratory signals. The body temperature is collected using a low-power, high-precision digital temperature sensor MAX30208. A filter algorithm for signal processing and wireless transmission is designed through a low-power nRF52840 Bluetooth SoC with an Arm Cortex-M4F kernel.

Results: The experimental results show that the designed monitoring terminal can monitor the ECG, respiration, and body temperature parameters of the human body in real-time and send the monitoring results via Bluetooth, with a continuous working time of more than 13 hours.

Conclusion: The wearable wireless chest patch monitoring terminal features good portability, long standby time, and high measurement accuracy, and it has promising application prospects in the fields of family health monitoring, mobile medical treatment, and smart healthcare.

ECG signals and sleep monitoring parameters complement each other and can be used for qualitative diagnosis of sleep apnea syndrome and cardio-related diseases. However, due to the limitations of the instrument volume and the detection environment, it is often challenging to integrate these two functions in practical applications. In this paper, a 12-lead dynamic electrocardiograph integrated with sleep monitoring is designed. The system's volume is reduced by combining the integrated ECG simulation front end with a miniature sensor. The system achieves the extraction, conditioning, and calculation of 12-lead ECG signals and sleep-related parameters and writes the data to a memory card in real time, which offers convenience for users and doctors in the diagnostic process.

Objective: In order to address the issues of inconvenience, high medical costs, and lack of universality associated with traditional knee rehabilitation equipment, a portable intelligent wheelchair for knee rehabilitation was designed in this study.

Methods: Based on the analysis of the knee joint's structure and rehabilitation mechanisms, an electric pushrod-driven rehabilitation institution was developed. A multi-functional module was designed with a modular approach, and the control of the wheelchair body and each functional module was implemented using an STM32 single-chip microcomputer. A three-dimensional model was established using SolidWorks software. In conjunction with Adams and Ansys simulation software, kinematic and static analyses were conducted on the knee joint rehabilitation institution and its core components. A prototype was constructed to verify the equipment's actual performance.

Results: According to the prototype testing, the actual range of motion for the knee joint swing rod is 15.1°~88.9°, the angular speed of the swing rod ranges from -7.9 to 8.1°/s, the angular acceleration of the swing rod varies from -4.2 to 1.6°/s², the thrust range of the electric pushrod is -82.6 to 153.1 N, and the maximum displacement of the load pedal is approximately 1.7 mm, with the leg support exhibiting a maximum deformation of about 1.5 mm.

Conclusion: The intelligent knee joint rehabilitation wheelchair meets the designed functions and its actual performance aligns with the design criteria, thus validating the rationality and feasibility of the structural design.

Biodegradable heart occluder uses a biodegradable medical polymer material to replace or completely replace the metal material. After completing the repair function of the heart defect, the device is gradually degraded and safely absorbed by the human tissue. So it may minimize the risk of long-term complications that traditional metal heart occluder causing in the body after implantation. Based on the quality management concept of the entire lifecycle of medical device, this article briefly introduces design and development, as well as the product realization of biodegradable heart occluder. It analyzes the main risk points in design and production, and corresponding suggestions have been put forward. These suggestions are combined with the medical device good manufacturing practice and the appendix of implantable medical equipment to provide a reference for regulators and industry professionals.

This study aims at developing a dataset for determining the presence of carotid artery plaques in ultrasound images, composed of 1761 ultrasound images from 1165 participants. A deep learning architecture that combines bilinear convolutional neural networks with residual neural networks, known as the single-input BCNN-ResNet model, was utilized to aid clinical doctors in diagnosing plaques using carotid ultrasound images. Following training, internal validation, and external validation, the model yielded an ROC AUC of 0.99 (95% confidence interval: 0.91 to 0.84) in internal validation and 0.95 (95% confidence interval: 0.96 to 0.94) in external validation, surpassing the ResNet-34 network model, which achieved an AUC of 0.98 (95% confidence interval: 0.99 to 0.95) in internal validation and 0.94 (95% confidence interval: 0.95 to 0.92) in external validation. Consequently, the single-input BCNN-ResNet network model has shown remarkable diagnostic capabilities and offers an innovative solution for the automatic detection of carotid artery plaques.

Objective: Photoelectric volumetric tracing (PPG) exhibits high sensitivity and specificity in flap monitoring. Deep learning (DL) is capable of automatically and robustly extracting features from raw data. In this study, we propose combining PPG with 1D convolutional neural networks (1D-CNN) to preliminarily explore the method's ability to distinguish the degree of embolism and to localize the embolic site in skin flap arteries.

Methods: Data were collected under normal conditions and various embolic scenarios by creating vascular emboli in a dermatome artery model and a rabbit dermatome model. These datasets were then trained, validated, and tested using 1D-CNN.

Results: As the degree of arterial embolization increased, the PPG amplitude upstream of the embolization site progressively increased, while the downstream amplitude progressively decreased, and the gap between the upstream and downstream amplitudes at the embolization site progressively widened. 1D-CNN was evaluated in the skin flap arterial model and rabbit skin flap model, achieving average accuracies of 98.36% and 95.90%, respectively.

Conclusion: The combined monitoring approach of DL and PPG can effectively identify the degree of embolism and locate the embolic site within the skin flap artery.

Sleep disordered breathing (SDB) is a common sleep disorder with an increasing prevalence. The current gold standard for diagnosing SDB is polysomnography (PSG), but existing PSG techniques have some limitations, such as long manual interpretation times, a lack of data quality control, and insufficient monitoring of gas metabolism and hemodynamics. Therefore, there is an urgent need in China's sleep clinical applications to develop a new intelligent PSG system with data quality control, gas metabolism assessment, and hemodynamic monitoring capabilities. The new system, in terms of hardware, detects traditional parameters like nasal airflow, blood oxygen levels, electrocardiography (ECG), electroencephalography (EEG), electromyography (EMG), electrooculogram (EOG), and includes additional modules for gas metabolism assessment via end-tidal CO ₂ and O ₂ concentration, and hemodynamic function assessment through impedance cardiography. On the software side, deep learning methods are being employed to develop intelligent data quality control and diagnostic techniques. The goal is to provide detailed sleep quality assessments that effectively assist doctors in evaluating the sleep quality of SDB patients.

Objective: The prediction of RR intervals in hypertensive patients can help clinicians to analyze and warn patients' heart condition.

Methods: Using 8 patients' data as samples, the RR intervals of patients were predicted by long short-term memory network (LSTM) and gradient lift tree (XGBoost), and the prediction results of the two models were combined by the inverse variance method to overcome the disadvantage of single model prediction.

Results: Compared with the single model, the proposed combined model had a different degree of improvement in the prediction of RR intervals in 8 patients.

Conclusion: LSTM-XGBoost model provides a method for predicting RR intervals in hypertensive patients, which has potential clinical feasibility.