Objective Design an integrated system based on the open-ended coaxial probe method for real-time measurement of the dielectric properties of biological tissues. Analyze the accuracy of the system in measuring tissue dielectric properties and study the impact of probe pressure on the measurement of dielectric properties. Methods Based on components such as a microcontroller, pressure sensor, and signal processing circuit, construct a device for measuring reflection coefficient and probe pressure; develop host computer software with calibration and tissue dielectric property calculation functions to display the probe pressure and the dielectric properties of the measured tissue in real-time. After building the measurement system, the dielectric properties of methanol, n-propanol, liver phantom, glioma and normal brain tissue were measured, and the dielectric properties of the system were verified according to the standard parameters such as dispersion coefficient, average percentage error and relative difference. Results The average dispersion coefficients of the relative dielectric constant and conductivity of methanol and n-propanol in the frequency sweep range are 0.0026 and 0.0827, respectively. The average percentage errors were 0.85 % and 16.01 %, respectively. The average dispersion coefficients of the relative dielectric constant and conductivity of isopropyl alcohol in the frequency sweep range are 0.0028 and 0.0078, respectively. The average error percentages were 2.43 % and 14.38 %, respectively. The relative differences of dielectric constants between glioma tissues and normal brain tissues at 64MHz, 128MHz and 298MHz frequencies were 57.28 %, 48.97 % and 48.85 %, respectively. The relative differences of electrical conductivity were 73.61 %, 69.9 % and 66.26 %, respectively. Conclusion This system can be used for the real-time measurement of the dielectric properties of biological tissues in vitro, while also providing a reference for studying the impact of probe pressure on dielectric property measurement. It holds promise to become an auxiliary tool for clinical diagnosis and treatment in the future.

This paper presents a comparative study of the registration requirements for in vitro diagnostics (IVD) in China and ASEAN. Since the implementation of the "Belt and Road" initiative, economic cooperation between China and ASEAN has become increasingly close, particularly in the field of medical devices. By analyzing the similarities and differences in regulations, product classification, registration filing materials, and registration paths for IVD in China and ASEAN, the paper proposes suggestions for promoting cooperation in IVD registration between the two regions. The study indicates that while China and ASEAN share common regulatory objectives, there are significant differences in the details of implementation. Strengthening cooperation in product registration standardization and regulatory mutual recognition between the two regions will help facilitate market access, enhance product safety and efficacy, and promote the development of the health industry in both regions.

Objective Based on real clinical data research, systematically analyze the failures of digestive endoscopy equipment and propose targeted preventive maintenance strategies, aiming to improve equipment utilization efficiency, reduce maintenance costs, and ensure medical safety. Methods A retrospective analysis was conducted, selecting 183 cases of endoscope failure data from the digestive endoscopy center of a tertiary hospital between January 2021 and December 2024. The chi-square test was used to comprehensively analyze failure rates, failure phenomena, causes, locations, and maintenance levels. Results The overall failure rate of the digestive endoscope was 0.16%, with significant differences in distribution (P＜0.001). The most frequent failure phenomena were angle issues (0.11%) and component wear/damage issues (0.11%); among the causes of failure, aging accounted for the highest proportion (38.96%); high-frequency failure locations were concentrated in the insertion section (35.65%) and operating section (33.18%), and the location distribution showed statistical differences (P＜0.05); maintenance levels were predominantly in class D (62.30%), with significant differences in maintenance level distribution (P＜0.001); high-frequency failures of digestive endoscopes primarily occurred in the 1-3 years and 7-9 years usage periods. Conclusion Based on the analysis of real data on endoscope failure maintenance, a comprehensive optimization plan is proposed, covering standardized training, multi-level maintenance systems, targeted maintenance for high-frequency failures, information management, improvement of endoscope management systems, and extending service life, providing a theoretical basis for reducing the clinical endoscope failure rate.

Objective High-frequency irreversible electroporation (HF-IRE), an emerging minimally invasive and non-thermal ablation technology, demonstrates potential in neuro-oncology due to its precise targeted ablation and neuroprotective advantages. However, standardized treatment parameters remain lacking. This study aims to evaluate the safety and partial efficacy of HF-IRE in ablating orthotopic brain tumors in rats, providing critical parameter references for clinical translation. Methods Orthotopic brain tumor models were established in rats, and HF-IRE treatment was administered at three electric field intensities (900, 1200, and 1500 V/cm). Tumor volume was dynamically monitored via magnetic resonance imaging (MRI), while hematoxylin-eosin (H&E) staining and TdT-mediated dUTP nick-end labeling (TUNEL) were employed to assess histopathological damage and apoptosis in brain tissues, systematically evaluating safety and efficacy. Results Electric field intensity positively correlated with tumor inhibition rates (82%, 95.6%, and 95.8%, respectively). However, the 1500 V/cm group exhibited significant histological damage, including cerebral edema and neuronal vacuolization. The 1200 V/cm group achieved a 95.6% tumor inhibition rate with only reversible mild edema and scattered apoptotic bodies, showing significantly reduced neural damage compared to the high-intensity group. Conclusion 1200 V/cm represents the optimal balanced parameter for HF-IRE in brain tumor treatment, achieving a significant tumor suppression rate (>90%) while minimizing adverse effects and neuroinjury risks associated with HF-IRE.

To address the complexity of fault types and low efficiency of manual diagnosis in medical equipment power supplies, an intelligent fault identification method based on an improved Convolutional Neural Network (CNN) is proposed. A total of 30,000 time-series signal samples covering four types (normal, switching transistor fault, PWM chip fault, and MCU fault) were collected to construct a multi-channel dataset. The CNN model incorporates batch normalization, L2 regularization, a squeeze-and-excitation (SE) attention module, and the AdamW optimizer to enhance feature extraction efficiency and robustness. Model performance was evaluated using accuracy, recall, and F1-score, and robustness was tested under noise interference and data loss conditions. Experimental results show that the improved CNN achieved an accuracy of 99.2%, a recall of 98.9%, and an F1-score of 99.0% on the test set, outperforming SVM and conventional CNNs by 2.0% to 7.7%. Under Gaussian noise with variance σ² = 0.1 and up to 30% feature loss, the model maintained an accuracy of approximately 93%. The proposed method demonstrates high precision, strong robustness, and deployability, offering technical support for intelligent maintenance of medical equipment.

Objective To construct the prediction model of chronic rotator cuff injury degree based on MR Water-lipid separation quantitative index to provide reference for the subsequent diagnosis and treatment of rotator cuff injury. Methods A retrospective study was conducted on 169 patients with chronic rotator cuff injury who underwent MR examination in our hospital from January 2021 to January 2024. According to the severity of the injury, they were divided into a partial tear group (123 cases) and a complete tear group (46 cases); Compare the clinical characteristics of the tear group and the severe tear group. Multivariate analysis of predictive factors for the degree of chronic rotator cuff injury. Clinical efficacy analysis of MR related parameters for predicting the degree of chronic rotator cuff injury. Results There were statistically significant differences (P<0.05) in the maximum diameter, acromion type, and fat fractions of the supraspinatus and infraspinatus muscles of the humeral tuberosity between the two groups. The results of logistic multivariate analysis showed that the maximum diameter of cystic changes in the greater tuberosity of the humerus, as well as the fat fractions of the supraspinatus and infraspinatus muscles, were independent predictors of the degree of chronic rotator cuff injury (P<0.05). A column chart model was constructed using the maximum diameter of the humeral tuberosity cystic change, the fat fraction of the supraspinatus muscle, and the fat fraction of the infraspinatus muscle. The ROC curve was used to predict the degree of chronic rotator cuff injury using the maximum diameter of the humeral tuberosity cystic change, the fat fraction of the supraspinatus muscle, the fat fraction of the infraspinatus muscle, and the column chart. The areas under the curves were 0.725, 0.734, 0.729, and 0.864, respectively. Conclusion The quantitative indexes of fat fraction of supraspinatus muscle and infraspinatus muscle showed better efficacy than traditional MR Indexes in predicting of chronic rotator cuff injury degree. The column chart prediction model constructed using the above factors shows good performance in predicting the severity of tearing in patients, and is worth further analysis in the work.

Objective To analyze the current status and development trends of biomaterials in rehabilitation. Methods Literatures with the theme of biomaterials in rehabilitation were retrieved from the Web of Science core database. The R language was used to visualize analysis, including publication time, cited references, authors, keywords, institutions, and countries/regions. Results A total of 1965 articles, 9946 authors, 5629 keywords, and 76259 references were extracted. Research on biomaterials in rehabilitation has shown a fluctuating upward trend, with the United States and China taking the lead. Global scientific research focused on biomaterials, tissue engineering, bone regeneration, biocompatibility, hydrogels, etc. Conclusion China's research in this field is developing rapidly, and more results will be produced in the future. This study uses R language analysis tools to examine biomaterials research in rehabilitation from a metrological perspective, providing information to inform future decisions regarding the development of this field.

To enhance the objectivity and accuracy of syndrome differentiation in traditional Chinese medicine and address the issues of reliance on expert experience and strong subjectivity, the research focuses on constructing an intelligent diagnosis and treatment model based on multimodal clinical data such as tongue images, pulse images, and symptom texts. Firstly, an improved convolutional recurrent neural network is designed to extract the spatial features of the tongue image, process the temporal features of the pulse image and the symptom text sequence. Introduce the spatial reconstruction unit and adaptively capture the irregular pathological features such as tongue cracks and ecchymosis through the dynamic deformable convolution kernel; Design the channel reconstruction unit and screen the key semantic channels by combining depth-separable convolution and compression-incentive mechanisms; A three-layer cross-modal attention mechanism is adopted to dynamically integrate multi-modal features, and a three-level syndrome type priority matching algorithm is integrated to simulate the priority syndrome differentiation process of the main symptoms in traditional Chinese medicine. The results show that the differentiation accuracy rate reaches 88.69%, which is superior to the support vector machine and recurrent neural network. In the tongue image classification task, the AUC of the mirror tongue reached 0.9663, and the generalization accuracy of the test set remained stable at 68.48%. The receiver operation characteristic curve and the precision-recall curve verified the performance balance of the model. The conclusion proves that this model effectively solves the problems of extracting and fusing multimodal features in traditional Chinese medicine, significantly improves the level of objective syndrome differentiation, and provides a reliable tool for intelligent clinical diagnosis and treatment.

Objective To investigate the impact of magnetic resonance imaging (MRI) combined with musculoskeletal ultrasound on the assessment and correlation of musculoskeletal function adjacent to the dorsal root ganglion (DRG) in patients with zoster-associated pain (ZAP). Methods Included patients with ZAP from June 2020 to July 2023 in our hospital, divided into postherpetic neuralgia group (n=45) and herpes zoster neuralgia group (n=64), comparing the general data, MRI, and musculoskeletal ultrasound parameters between the two groups. Compared the Oswestry Disability Index (ODI) and Visual Analog Scale (VAS) scores of both groups. Conducted multiple linear regression analysis of MRI parameters and adjacent musculoskeletal functional indicators of the DRG. Used multivariate logistic regression analysis to identify independent risk factors affecting postherpetic neuralgia in patients. Constructed a restricted cubic spline model to analyze the dose-response relationship between MRI, musculoskeletal ultrasound parameters, adjacent musculoskeletal function of the DRG, and postherpetic neuralgia. Plotted the receiver operating characteristic curve (ROC) and calibration curve to assess the predictive accuracy of the model. Results The differences between the two groups of patients in terms of hypertension history and smoking are statistically significant (P<0.05). Patients with postherpetic neuralgia had significantly higher values in the surface volume ratio of the DRG, maximum 2D diameter pillar, median, large area high grayscale emphasized parameters, musculoskeletal ultrasound score (MUS), pain numerical rating scale score (NRS), ODI, and VAS compared to patients with herpes zoster neuralgia (P<0.001). The results of the multifactor logistic regression analysis showed that hypertension, smoking, surface volume ratio, maximum 2D diameter pillar, median, short-range low grayscale emphasis, large area high grayscale emphasis, MUS, and NRS are independent influencing factors for postherpetic neuralgia in patients (P<0.05). The results of the restricted cubic spline model analysis showed that there is a nonlinear dose-response relationship between MRI, musculoskeletal ultrasound parameters, adjacent muscle and bone function of the DRG, and the risk of developing postherpetic neuralgia. The ROC curve results showed an AUC of 0.859 (95% CI: 0.813~0.902), and the calibration curve Brier score was 0.087, indicating a high predictive ability of the model. Conclusion MRI combined with musculoskeletal ultrasound imaging features can assess the musculoskeletal function adjacent to the DRG in ZAP patients. The MRI and musculoskeletal ultrasound parameters are correlated with the functional parameters of the adjacent musculoskeletal structures to the DRG, and the model shows high discriminative ability and accuracy.

ObjectiveThis study aims to systematically analyze the yield fluctuations of the Fastlab 2 automatic synthesis system in the actual production of [18F]FDG for the first time through the Statistical Process Control (SPC) method, identify the key influencing factors, and put forward specific and operational quality optimization suggestions. Methods A retrospective analysis was conducted on the production data of 43 batches of [18F]FDG in our center within the past 3 months. The X-bar and R control charts were used to evaluate the process control of the yield, and the coefficient of variation (RSD) was combined to evaluate the volatility. Parameter traceability (such as vacuum pressure, hydrolysis temperature, and operational compliance) was conducted for 3 failed batches (with a yield of 0%) and 3 low-yield batches (<40%). Results The success rate of the Fastlab 2 system was 93.02%. The yield of successful batches (excluding 3 failures) ranged from 31% to 91%, with an average yield of 81.6% and an RSD of 18.64%. The X-bar chart and R chart show that most batches are within the control limit, and the overall production process is stable. However, some local batches have abnormal vacuum pressure (such as 190 mbar vs.) Fluctuations in yield were caused by excessive hydrolysis temperature (standard 600 mbar), high hydrolysis temperature (93℃ vs. standard 85-88℃), and operational deviations (improper installation of ferrules and failure to conduct water bag checks). The failed batches were mainly related to vacuum faults of the equipment (2 times) and operational errors (1 time). Conclusion: The Fastlab 2 system has a good ability of automated synthesis, but the fluctuation of yield still needs attention. By optimizing the startup process confirmation of the equipment, improving the installation process of reagent cartridges, strengthening the training of operators and standardizing the management of the experimental environment, the consistency of the system and the production success rate can be effectively enhanced, providing a referenceable optimization path for similar radiopharmaceutical production units.

ObjectiveThis study based on the "structure-process-outcome" three-dimensional quality model, aims to establish a sensitive indicator system applicable to the management quality assessment of hemodialysis machines, providing a quantitative and objective monitoring basis for improving the management quality of hemodialysis machines. MethodsIn the research process, first, the framework of the indicator system was initially constructed by combining literature research and qualitative interviews; then, two rounds of expert consultations were carried out using the Delphi method; finally, the weights of various indicators were determined through the Analytic Hierarchy Process. ResultsThe constructed indicator system covers 3 first-level indicators, 6 second-level indicators, and 21 third-level indicators. Among them, the sensitive indicators with the top three weights are the intact rate of hemodialysis machine equipment (0.1001), the implementation rate of regular maintenance of hemodialysis machines (0.1000), and the failure rate of hemodialysis machine equipment (0.0430). The effective recovery rates of the two-round questionnaires both reached 100%. The expert authority coefficients were 0.836 and 0.862 respectively, and Kendall's W coordination coefficients were 0.542 and 0.568 respectively (P < 0.001). ConclusionThe indicator system constructed based on the three - dimensional quality model can provide strong support for the standardized management of hemodialysis machines. It can be popularized and applied in the hemodialysis rooms and intensive care units of hospitals at all levels in China that use hemodialysis machines, promoting the standardization process of hemodialysis machine management.

The linear accelerator Vital Beam occupies a central position in tumor radiotherapy, and its imaging accuracy is crucial for the effectiveness of radiotherapy. This article systematically elaborates on the standardized operation process of image calibration for the Varian Vital Beam linear accelerator, with a focus on the key steps of kilovolt KV mode calibration, Air Norm and HU calibration for cone beam CT. Based on long-term quality control data (January 2023 and July 2024), the impact of strict execution of the calibration process for 10 months before and after on parameters such as image quality and HU value consistency was quantitatively evaluated. The study used the Catphan 504 phantom for detection. The results showed that the spatial resolution of the calibrated image remained at a high level with a slight increase of 14.29, and the overall low contrast resolution improved by 18.34%; The standard deviation (SD) of HU value was improved by 3.03% for the overall noise of the water model before calibration, 4.58% for the density accuracy of key materials such as polytetrafluoroethylene (Teflon), 2.79% for local noise, and 53.94% for uniformity, significantly improving image accuracy. In addition, the corrective effect of image calibration was analyzed based on typical fault cases. Strictly following the calibration process is the key to ensuring high-precision implementation of image-guided radiotherapy. This study provides comprehensive technical reference and basis for radiotherapy workers, which is of great significance for improving the quality and safety of radiotherapy.

Aiming at the core issues in pediatric emergency triage, such as strong disease dynamics, high insidiousness of symptoms, high rate of missed diagnosis of severe cases due to high concurrent resources, unbalanced resource allocation and low clinical trust. The research utilizes the dual-actor module to optimize the proximal strategy optimization algorithm. By dynamically adjusting the triage strategy, the processing ability of time series data is enhanced. Combined with the non-dominated ranking genetic algorithm, an efficient and accurate intelligent triage classification model is constructed. The research results show that the average return rate of the improved proximal strategy optimization algorithm with the introduction of the dual-actor module can reach 88.5%, and the number of environmental interactions that achieve the highest return rate is only 50 times. In addition, the missed diagnosis rate and over-triage rate of the intelligent triage and classification model for pediatric emergency proposed in the study were at least 2.8% and 1.5% respectively. After adding noise, the area under the precision recall curve of this triage model only decreased by 0.041. The result data show that this model, through the collaboration of dual algorithms, significantly improves the efficiency and accuracy of pediatric emergency triage, effectively solves the problems of missed diagnosis of severe cases and misallocation of resources, and provides a practical solution for the intelligent emergency system.

ObjectiveTo explore the influence of maintenance strategies for micro-injection pumps on the operating time of the equipment and the maintenance nodes. Methods A retrospective analysis was conducted on the fault data of 50 randomly selected microinjection pumps in our hospital in 2023. They were divided into two groups according to the maintenance strategies: the control group adopted traditional passive maintenance, and the experimental group adopted active preventive maintenance. Taking the operating time and status of two groups of equipment as events, the Kaplan-Meier method was used to conduct survival analysis on the fault data under different maintenance strategies, and the survival rate and survival time of the equipment under each strategy were presented through charts. Meanwhile, three chi-square test methods were adopted to test the statistical differences in the survival functions of the two groups of samples (P), and the survival function curves of different maintenance strategies were compared. ResultsThe median survival time for equipment in the experimental group was 49 weeks, while it was only 27 weeks for the control group, indicating that the maintenance strategy adopted by the experimental group significantly improved the survival rate, extended the service life, and reduced the risk of failure. Additionally, through analyzing the survival function curve, it is evident that the performance status of equipment in the experimental group significantly declined after 33 weeks, providing a scientific basis for preventive maintenance of medical equipment. ConclusionAnalyzing the fault status of micro-infusion pumps under different maintenance strategies using the Kaplan-Meier method is feasible. Proactive maintenance strategies can effectively enhance equipment performance and lifespan, ensuring good operational conditions. Combined with the survival function curve, key time points can be clearly identified, allowing for preventive maintenance measures to be taken, thereby improving the reliability and safety of injection pumps.

Muscle loss has been shown to be closely associated with an increased risk of post-transplant complications in transplant recipients. Imaging diagnosis is currently a common method for the diagnosis of sarcopenia through the use of a series of imaging methods, including dual-energy X-ray absorptiometry, DXA, computed tomography (CT), magnetic resonance imaging (MRI), ultrasonic (US), and AI-assisted diagnostic techniques can accurately assess muscle mass, Thus diagnosing sarcopenia. This article will summarize and analyze the research progress on the correlation between sarcopenia and poor prognosis after transplantation assessed by different imaging methods, aiming to provide a reliable reference for clinicians to select the most appropriate diagnostic method.

With the continuous development of the smart healthcare system, artificial intelligence has been widely applied in the field of medical care. As an important department for surgical treatment and rescue, the risk management in the operating room is particularly crucial. At present, there are few reports on the relevant research of artificial intelligence in the risk management of operating rooms. Therefore, this study aims to summarize the current application status of artificial intelligence in the risk management of operating rooms at home and abroad, and analyze five high-risk factors including the handover of surgical patients, surgical safety verification, intraoperative pressure ulcers, perioperative hypothermia and pathological specimen management. To clarify the current research status and limitations at home and abroad, explore and look forward to the development direction of intelligent technologies in the above-mentioned risk management in China in the future, with the aim of providing a reference for the further development of artificial intelligence technology in the field of operating room risk management.

Objective High-resolution flat-panel detectors experience significant image quality degradation in low-dose X-ray acquisition modes. Research into self-supervised domain adaptation methods aims to significantly enhance the imaging performance of flat-panel detectors in real-world low-dose imaging scenarios. Methods First, a large-scale animal joint dataset is acquired using a flat-panel detector to pre-train the source-domain model. Through iterative knowledge transfer and style generalization learning, the source model is then adapted to the target domain, requiring only a limited set of unlabeled human joint scans for target model training. The proposed method is systematically evaluated for its efficacy in enhancing 2D projection imaging and cone-beam CT (CBCT) reconstruction quality under low-dose exposure conditions. Results The experimental results indicate that using a self-supervised domain adaptation approach to denoise low-dose observational data collected by large flat-panel detectors significantly enhances imaging performance. First, the quality of two-dimensional projection imaging improved significantly: the signal-to-noise ratio (SNR) of the projection images from two test sets increased by 6.66 dB and 9.95 dB in low-dose exposure mode. Second, the quality of cone-beam CT (CBCT) reconstruction was also optimized: noise and artifacts in the reconstructed images were significantly suppressed, and the standard deviation of CT values decreased from a baseline of 327 HU to 60 HU, a reduction of 81.7%. Conclusion The self-supervised domain adaptation learning method effectively enhances the imaging performance of flat-panel detectors under low-dose acquisition conditions, thereby expanding their applicability in a wider range of low-dose X-ray applications.

Objective To address the issues of low protection efficiency, insufficient security, and high cost of cross device deployment in medical device data, a new data protection model is proposed to enhance the security and economy of the entire data lifecycle. Methods The model adopts a two-layer consensus network to optimize the Byzantine fault-tolerant algorithm, achieves efficient consensus decision-making through layered collaboration, and combines advanced encryption standard algorithms to construct a data encryption layer, forming a consensus and encryption fusion model. Based on the MDPCD and MedOdyssey datasets, this study compares traditional encryption models, single consensus models, and mainstream blockchain solutions to validate their anti attack capabilities, communication efficiency, device compatibility, and other dimensions. Results The experimental results show that the optimized Byzantine fault-tolerant algorithm has only 231 communication times and an average latency as low as 986ms at the maximum node size, with a performance improvement of 53%. The medical device data protection model constructed achieved a maximum score of 10 in the three security indicators of anti Byzantine attack, dynamic key update, and differential privacy support in the MedOdyssey dataset. It also achieved high compatibility in low, medium, and high computing devices, with a deployment cost of only $95, which is 40% -62% lower than the comparative model. ConclusionThe above results indicate that the algorithm model can significantly improve the efficiency and security of data protection for various medical devices, while also having a positive effect on reducing the cost of data protection.

Hepatobiliary surgery—often called the “Mount Everest” of the surgical disciplines—poses exceptional challenges owing to its intricate anatomical layers, high operative risks, and frequent postoperative complications. Under the traditional paradigm, surgeons rely heavily on personal experience, which can lead to inaccuracies in tumor localization, vascular identification, and intraoperative decision-making. In recent years, however, intelligent technologies have achieved breakthroughs in medical-image analysis, three-dimensional visualization reconstruction, and clinical-prediction modeling, offering new opportunities to enhance both the efficiency and safety of hepatobiliary operations. Nevertheless, clinical translation remains limited by heterogeneous data sources, insufficient algorithm interpretability, and a lack of large-scale, multicenter validation. Here, we review the latest advances of these technologies in key phases of care—preoperative radiomics assessment, intraoperative augmented-reality navigation, and postoperative prognostic prediction—and propose an “end-to-end intelligent surgery” framework. This framework integrates the preoperative, intraoperative, and postoperative stages into a cohesive, collaborative system. We analyze existing challenges and explore frontier directions, including federated learning and human–machine collaborative decision-making, with the goal of promoting standardized implementation, fostering deep integration of precision surgery and smart healthcare, and ultimately improving patient outcomes.

As the core medical equipment in medical rescue and transport, the design and performance of medical stretches are directly related to the treatment efficiency, safety and comfort of the wounded and patients. Although the current mainstream stretchers have their own advantages in portability, terrain adaptability, or operation performance, they still have different defects: foldable stretchers are easy to malfunction and difficult to sterilze, wheeled stretchers rely on flat roads and are bulky, shopled-type stretchers have limited load bearing and poor comfort, vacuum stretchers have insufficient portability, and basket stretchers have limited application scenarios. Moreover, the current level of intelligence of medical stretters is low, which is difficult to meet the requirements of real-time monitoring and precise disposal of modern rescue. This paper systematically analyzes the type characteristics, material performance bottlenecks and intelligent development trends of medical stretches, focusing on the integrated application of Internet of things and artificial intelligence technology in intelligent modules such as vital signs monitoring, path navigation, remote diagnosis and treatment. It is hoped to provide a useful reference for optimizing the reliability of the stretder structure, shortening the treatment response time, and constructing a cooperative rescue mode.

ObjectiveCompare the characteristics and effects of four management methods for inpatient medical consumables medical insurance violations, and explore the problems in the supervision of medical consumables medical insurance. Methods The medical consumables usage data of hospitalized medical insurance patients during January 2024 to January 2025 were extracted from a third-level class-A hospital in Zhejiang Province. Divide the data into 58 evenly spaced cycles on a weekly basis. Using Multi-stage Interrupted Time Series Analysis to compare the medical insurance violation rate and self payment violation rate under different management modes, with the switching time of different management modes as the interruption point. Results Under the four modes, the medical insurance violation rates were 2.36%, 1.25%, 0.13%, and 0.01%, respectively, while the self payment violation rates were 0.30%, 4.32%, 3.63%, and 0.35%, respectively.The Pop-up Approval Mode can significantly reduce the rate of medical insurance violations among hospitalized patients, but it will significantly increase the workload of discrimination, leading to an increase in the rate of self funded violations. Intelligent Discrimination Reminder Mode better than the other three management modes, From the perspective of reducing the violation rate of medical insurance and self payment, and reducing the workload of discrimination, the intelligent discrimination reminder mode. Conclusions The information reform of medical institutions and the monitoring of medical expense can ensure efficient management of medical consumables in violation of regulations during hospitalization. To eradicate violations of medical consumables, it still depends on the improvement of rules by the medical insurance management department and the establishment of a full-process management mechanism for medical institutions. The high-pressure supervision of the medical insurance management department is critical for the safe and stable of the medical insurance fund, but we need to prevent the moral damage caused to patients by the gradual transfer of risks of medical insurance violations.

Osteoporosis is highly prevalent and has become the fourth most common chronic disease globally, placing a substantial burden on healthcare systems. Currently, the main treatment approaches for osteoporosis encompass pharmacotherapy, exercise therapy, and rehabilitation programs. However, these conventional treatment methods are often accompanied by challenges, including low treatment adherence and suboptimal therapeutic efficacy. Digital therapy, as an emerging form of medical intervention, leverages technologies such as virtual reality, artificial intelligence, and wearable devices through application software to play a critical role in the treatment, prevention, and health education of osteoporosis. Despite significant progress in related research, a comprehensive synthesis and systematic organization of these findings are still insufficient. Consequently, this study aims to present an overview of digital therapy, investigate its application forms in osteoporosis, assess its effectiveness, analyze existing challenges and potential solution strategies, and provide guidance for the future development and application of digital therapy in osteoporosis.

In the treatment of cervical cancer, painless brachytherapy is crucial for enhancing the quality of life for patients. Painless strategies include preoperative pharmacological and non-pharmacological interventions to alleviate anxiety, intraoperative anesthesia selection to ensure comfort, and postoperative pain control to reduce suffering. This article reviews the research and application of painless brachytherapy, highlighting the importance of pain management during the treatment process.

Objective At present, there is no daily quality control standard for CZT cardiac specific camera at home and abroad. This paper verifies the system stability of the cadmium zinc telluride (CZT) specific cardiac camera by retrospectively analyzing its daily QC data, and explores its influencing factors and solutions.Methods According to the user manual, the daily QC data and the temperature and humidity of operation room of D-SPECT from 2017 to 2023 were collected. Nonparametric Spearman rank correlation test and multiple linear regression analysis were performed between the quality control indicators and the temperature and humidity of the operation room, and the activity of the quality control rod source to explore their influencing factors. The nonparametric binary classification Mann-Whitney U test was used to explore the impact of replacing the rod source.Results The daily QC results within six years were all qualified. The operation room temperature and rod source activity are related to daily QC indicators, and humidity is related to some indicators. (P<0.05) The rod source activity and temperature and humidity have influence on some daily QC indexes. Replacing the rod source has a significant impact on the quality control index. (P<0.05)Conclusion The system performance of the D-SPECT CZT cardiac specific camera remains stable over six years of use, and the stability of clinical application can be guaranteed under strict compliance with operating specifications. During daily use, it is necessary to pay close attention to changes in the operation room environment and the use status of the rod source to ensure that machine can run normally.

Objective To design a complex disease research platform based on microservices architecture and explore its application value in precision medicine.Methods The study employed the Spring Cloud Alibaba microservices framework, encapsulating complex disease-related data and analytical workflows into public and private microservices, including modules for transcriptomic data analysis, epigenetics data analysis, and others.Results After platform implementation, project initiation time was reduced by 52.6% compared to traditional methods, with statistically significant differences (P < 0.001). Project completion rate increased from 70% (14/20) to 90% (16/18).Conclusion The microservices-based research platform can be effectively applied to clinical and fundamental studies of complex diseases, demonstrating significant value for precision medicine and offering insights for broader adoption.

With the continuous development of communication technology, the rapid progress of Internet of Things technology is profoundly affecting the medical field, especially in the interconnection of medical equipment, which shows great potential. By building the Internet of Things for medical equipment, it can significantly improve the efficiency of information flow and integration within hospitals, achieve real-time collection of patient health data and medical environment parameters, provide scientific basis for clinical practice through deep analysis of data, optimize diagnosis and treatment processes, and improve the quality and efficiency of medical services. The interconnection of medical equipment based on IoT technology is conducive to the refined management of medical equipment, realizing real-time monitoring, intelligent scheduling, and efficient utilization of equipment, thereby significantly reducing the operating costs of hospitals. The interconnection of medical devices based on the Internet of Things plays an important role in the construction of smart hospitals. Therefore, this article will elaborate on the value of building the Internet of Things for medical devices, analyze and compare the value and current situation of medical device interconnection models based on the Internet of Things, explore the development direction of medical device interconnection under the Internet of Things technology, in order to provide forward-looking thinking and strategic suggestions for the development of smart healthcare, and promote the widespread application and deep integration of medical device Internet of Things technology in the medical industry.

Objective To explore the intelligent management indicator parameters corresponding to the key links in the entire life cycle management process of emergency life support equipment and determine the key indicator parameters for the intelligent management of the entire life cycle of emergency life support equipment. Methods In this study, an intelligent management model for emergency life support equipment was determined by integrating the methods of literature review, field visits and investigations, and brainstorming. The key indicator parameters in this model were identified through the Delphi expert consultation method and questionnaire survey method. Results From the five first-level indicator dimensions of tendering and procurement management, equipment benefit analysis, equipment operation analysis and management, equipment quality control, maintenance management, and clinical use evaluation of the equipment, an intelligent management model for emergency life support equipment with 32 important indicators was ultimately formed. Five indicators, namely plan demonstration, tender parameters, measurement plans for the equipment, maintenance management of the equipment, equipment optimization configuration, and operation training, were commonly familiar to experts and unanimously regarded as highly important indicator parameters. Conclusion The results of this study provide a powerful reference basis for colleagues to carry out intelligent management work for emergency life support equipment and will further advance the intelligent management process of emergency life support equipment.

Objective To conduct an in-depth analysis of the causes of the "system overheating" alarm malfunction of the CARDIOSAVE Hybrid intra-aortic balloon pump (IABP) of the MAQUET brand, summarize maintenance experience, and put forward improvement measures for equipment management to enhance the operational efficiency and reliability of the equipment. Methods Retrospectively analyze the maintenance processes of 6 cases of the "system overheating" alarm malfunction of this model of IABP pump. Analyze the malfunction mechanism in detail by combining the equipment system structure and working process. Meanwhile, count the usage status of the existing IABP equipment in the hospital, including information such as the using departments, cumulative usage time, service life and number of malfunctions. Results Six malfunction causes and corresponding maintenance solutions were summarized. It was found that the failure rate of the high-temperature alarm caused by problems with the negative and positive pressure filters was relatively high. There were significant differences in the usage frequencies of IABP equipment among different departments, and there was still room for optimization in the equipment configuration of the whole hospital. The service life of the equipment was positively correlated with the number of malfunctions, which conformed to the law of the equipment failure curve. Conclusion Equipment manufacturers should optimize equipment design, expand monitoring scope and add monitoring indicators. Medical institutions should manage equipment scientifically, including setting maintenance cycles and exploring sharing models, etc. so as to reduce the failure rate and improve the comprehensive benefits of IABP equipment.

Objective This study aims to explore the development and practice of a quality control management model based on distributed quality control devices. Methods By analyzing the challenges in medical equipment quality control, a distributed quality control device was specifically developed using infant incubators as an example. Subsequently, a three-tier quality control management model was established based on this device. The model was piloted in the pediatric department of the China-Japan Friendship Hospital and compared with traditional quality control devices and management models through a Comparative Effectiveness Research (CER). Results The quality control management model utilizing the distributed device for infant incubators improved quality control efficiency by over 71%, achieved 100% equipment coverage and 7×24-hour real-time daily quality control monitoring, reduced costs by nearly 50%, and standardized quality control processes and criteria. Conclusion The innovative model systematically addresses four major pain points of traditional quality control—low efficiency, narrow coverage, high cost, and inconsistent standards—providing a more efficient, economical, and reliable solution for medical equipment quality control.

Objective To address the current issues of fragmented performance assessments and weak quality regulation due to the lack of a standardized evaluation system for electroacupuncture therapy devices, this study aims to establish a scientifically robust and universally applicable evaluation index system to provide theoretical support for industry standardization.Methods The Delphi method and Analytic Hierarchy Process (AHP) were adopted to construct the evaluation index system. A preliminary framework was developed through literature analysis and expert consultations, followed by two rounds of Delphi surveys and quantitative analysis using AHP.Results A comprehensive evaluation index system was finalized, comprising 3 primary indicators, 7 secondary indicators, and 26 tertiary indicators. This system was applied to conduct systematic testing and comparative analysis of 286 electrotherapy devices (13 models from 8 manufacturers) in clinical practice.Conclusion The evaluation index system developed in this study demonstrates strong scientific validity and broad applicability, serving as standardized criteria for performance assessment, product development, and quality regulation of electroacupuncture therapy devices.

The SPD management model for medical consumables has significantly improved the efficiency and quality of the management of medical consumables. Its management model has developed rapidly in China, but there is a lack of unified standards for the management model. The SPD models applied in the management of medical consumables mainly include the self-operated and self-built model of hospitals, the operation outsourcing model led by hospitals, the centralized distribution service model, etc., and the management models are still constantly innovating. For large general hospitals, due to the large volume and diverse types of medical consumables involved and the high complexity of consumable management, more attention should be paid to the impact of the SPD management model on the consumable management, exploring the advantages and disadvantages of the SPD management model and forming relevant standards.

Objective This study aims to enhance the refined management of medical consumables through the reform of the Supply, Processing and Distribution (SPD) service model in hospitals. By integrating data across departments, the reform strengthens precision management practices throughout the entire circulation process of medical consumables. Methods Connect data interfaces to link with HIS, HRP, EMR, and other information systems. A medical consumables supply management system was established, incorporating functions including standardized coding, inventory management, clinical traceability, intelligent replenishment, and financial settlement integration. Through logistics data monitoring and analysis at each usage stage, full lifecycle traceability management of medical consumables was achieved. Results Through the reform of SPD service and supported by big data evidence, achieved rationalized supervision of medical consumables and promoted their clinically appropriate application. Conclusion The results demonstrate that the in-hospital reform of medical consumables supply chain services meets the practical needs of refined management for hospital consumables management departments.

Objective To design a multi-supplier SPD (Supply, Processing, and Distribution) management platform to solve the problem of multi-supplier management in hospital consumables management and optimize the full-process management. Methods Adopt the model of "self-built platform and connection with supplier systems", through multi-level system architecture design, build a unified management process, data standards and assessment system, achieve seamless connection with the hospital system, cover the links of access, distribution, use and settlement, and be equipped with intelligent devices and analysis tools. Results As of August 2024, the platform has completed the construction of main archives for 108,000 items of materials, launched 5,540 types of consumables, accumulated 61,248 orders, with a delivery amount of 364 million yuan, a settlement amount of 571 million yuan, and invoice verification of 299 million yuan. This has effectively prevented errors and omissions in accounting, facilitated "0 inventory" management, reduced inventory costs, and improved delivery efficiency. Optimize supply chain management to enhance the satisfaction of medical staff and patients. Conclusion The SPD platform has enhanced the refined level of consumable management, formed a management closed loop, and significantly improved the operational efficiency and medical quality of the hospital. In the future, the system will continue to be optimized, the application of RFID technology will be expanded, and the management accuracy and efficiency will be enhanced.

Objective To address the current management of non-sterile orthopedic consumables, the process is restructured to achieve full-process supervision, effective traceability, and unified usage and billing of consumables. Improve billing accuracy and efficiency, reduce non sterile consumables disinfection costs.Methods Digital technology is utilized to build a digital platform for orthopedic management, creating a "one-item-one-code" management model for non-sterile orthopedic consumables, and introducing an advanced image recognition system to reshape the management process. This extends regulatory work to in-hospital warehousing, enabling precise acceptance of each implant, in-hospital traceability, and accurate billing. Conclusion Through the trial operation of the new model, the management of non sterile orthopedic consumables has been optimized, resulting in a 62.5% increase in billing efficiency and accuracy. This has achieved 100% traceability of non sterile consumables, reduced sterilization frequency, lowered disinfection costs by 87.5%, and saved overall costs.

Objective To track the changes in tissue properties during the operation of the ultrasonic scalpel and to monitor the dynamic changes in tissue characteristics during the cutting process of the ultrasonic scalpel in real time, with the aim of providing a more optimized energy regulation output algorithm. Methods In accordance with the principle of bioimpedance, the impedance matching algorithm of ultrasonic transducers was employed to measure the tissue impedance of the contact portion of the knife head. Signal acquisition and processing were conducted to fit the tissue parameters and generate a tissue model. During the three stages of "start - denaturation - separation" in the cutting process of the ultrasonic scalpel, the load impedance data of the knife head was collected in real time by using the impedance matching algorithm of the ultrasonic transducer (with a sampling frequency of 2000 times per second). The impedance curves were fitted through in vitro pig tissue experiments (n = 100 cuts per setting), and tissue-specific characteristics were extracted by integrating signal smoothing, peak analysis, and other methods. The experimental design encompassed five current output settings (170 - 350 mA) to systematically explore the coupling mechanism of energy - impedance - tissue type. Results The curves of tissue impedance changes during the cutting process of some pig tissues were fitted, validating the feasibility of this method. The changes in impedance recovery of pig stomach and small intestine throughout the cutting cycle were significantly different (p < 0.05), indicating that there were differences in impedance changes among different tissue parts. The consistency of impedance recovery changes in the same tissue part was mainly manifested in the insignificant differences in the period of peak appearance, peak size, and data change trend after peak appearance (p > 0.05), suggesting that the impedance recovery data changes of the same tissue part in different individuals exhibited a high degree of convergence. As the energy output of the ultrasonic scalpel host increased, the trends of impedance recovery data changes in pig stomach and small intestine tissues tended to be gentle, and the peaks decreased, indicating that the tissue load of the knife head would vary under different energy output settings of the ultrasonic scalpel. By extracting this change characteristic, the ultrasonic scalpel host could identify the type of tissue being cut at each output setting, thereby enabling the setting of the energy output adjustment module of the ultrasonic scalpel host for different tissue types for each setting. Conclusion The tissue impedance curve can provide a basis for the host of ultrasonic scalpel to identify tissue type and adjust the energy output of the host. This study can provide effective data support for improving the performance of domestic ultrasonic scalpel and optimizing the clinical effect of ultrasonic scalpel.

Hepatocellular carcinoma (HCC) is the main subtype of primary liver cancer, and due to its insidious early symptoms and rapid progression, the five-year survival rate of patients is only 18%. Currently, it remains a major challenge in the global cancer treatment field. In recent years, spectral CT has shown great potential as an emerging medical imaging method in oncology, with great potential for accurate diagnosis, treatment guidance, and prognosis evaluation of tumor diseases. In the context of precision medicine, the deep integration of artificial intelligence and radiomics, combined with multi parameter quantitative analysis of spectral CT, provides new technical support for early screening, dynamic evaluation of therapeutic efficacy, and accurate prognosis prediction of HCC. This article aims to systematically explain the basic principles of spectral CT imaging and its clinical applications in hepatocellular carcinoma, while also reviewing the rapidly developing fields of radiomics and artificial intelligence in recent years.

Objective To explore the SPD management strategy for operating room consumables based on the integration of medical and engineering perspectives, and to evaluate its practical effects in a tertiary hospital in Jiangsu Province.Methods Guided by the concepts of medical-engineering integration and SPD lean management, an SPD management strategy for operating room consumables was developed and implemented. The management effectiveness was assessed by comparing indicators such as consumables management efficiency per surgery, abnormal situations in barcode-controlled consumables management, and satisfaction of operating room medical staff before and after the application of the SPD model.Results After implementing the SPD model, the preparation time, delivery time, response time for temporary requisitions, and return time of consumables per surgery in the operating room of the tertiary hospital in Jiangsu were significantly reduced (P<0.001).The implementation of daily clearance management for barcode-controlled consumables has effectively reduced losses, missed charges, and incorrect charges(P<0.05).The satisfaction of medical staff significantly improved (Χ2=26.825,P< 0.001).Conclusion The SPD management strategy based on the medical-engineering perspective significantly optimizes the operating room consumables management process, reduces operational costs, improves management efficiency, and enhances the quality and efficiency of medical institutions. It holds broad potential for promotion.

Objective To compare and analyze the results of composite field and large field in dose verification of Portal Dosimetry (PD) for fixed-field intensity modulated radiotherapy (IMRT) plans of cervical cancer, and to provide reference and guidance for medical physicists to carry out dose verification work. Methods A total of 21 fixed-field IMRT plans from 21 cervical cancer patients were selected for this study. Each treatment plan included 7 large fields, and each large field consisted of a pair of split fields. The Eclipse was used to create two kinds of PD dose verification plans from treatment plans, one kind containing only split fields and the other kind containing only large fields. And then the above two kinds of verification plans were carried out using a Clinac iX linac. Data of gamma passing rate were directly obtained for large fields by using the PD software module. And data of gamma passing rate for each composite field was obtained from corresponding pair of split fields by using the composite field menu tool. Value of G was used to represent data of gamma passing rate. Parameter settings of 3mm/3% and a threshold of 10% were used in gamma analysis. Statistical analysis and comparative study were used for G values of composite fields, large fields, and split fields. The rank-sum test was used to compare G values of the two types of composite field and large field. Results A total of G values of 147 composite fields and 147 large fields were obtained, with median values of 99.2% and 98.3%, respectively. The rank-sum test of G values of the two types of fields showed that, the difference was statistically significant (P<0.05). The median difference in G values between composite field and large field is 0.9%. The median values of G values for split field 1 (SF1), split field 2 (SF2), and all split fields (SF) are 99.7%, 99.9%, and 99.8%, respectively. The G values of SF1, SF2 and SF are generally higher than corresponding values of the composite field and the large field. Conclusion By and large, values of gamma passing rate of the composite field are bigger than corresponding values of the large filed. It could be considered to use the composite filed to obtain better dose verification results.

Objective The purpose of this study was to achieve the goal of comprehensive management of high-value consumables in the oral clinic through fine management. Methods The characteristics of high-value consumables in the oral clinic were sorted out, and the personalized information needs of the SPD system were formulated to achieve the goal of scanning all high-value consumables in the outpatient department. A three-level inventory management system was established, and it was linked to the performance system. An RBRVS (Resource-Based Relative Value Scale) model was established to distribute the performance of the oral clinic specialty, and the cost and benefits were refined. Results The time for medical staff to claim consumables and the time for warehouse inventory were significantly shortened (P < 0.05), the errors in registering high-value consumables and the differences in billing were significantly smaller than before implementation (P < 0.05), and the satisfaction of medical staff was significantly higher than before implementation (P < 0.05). The material ratio of the department from January to July 2024 was reduced from 15.07% of the previous year to 14.18%. It saved hospital costs, reduced the risk of consumable management, motivated doctors, and achieved comprehensive management of high-value consumables in the oral clinic. Conclusion By sorting out the "life nodes" of high-value consumables according to their entire life cycle and using the SPD model for fine management of high-value consumables in the oral clinic, high-value consumables in the oral clinic can truly achieve comprehensive management, improving the efficiency and level of hospital management, reducing consumable costs, and thus reducing the economic burden on patients. It is worthwhile for peers to learn from it.

Objective To study the variation patterns of cortical functional connectivity property in mental workload tasks with different types of visual information. Methods Working memory tasks with different information types and various mental loads were designed based on N-Back paradigm. EEG signals from 18 normal adults were acquired when tasks were being performed. EEG source localization based on sLORETA were carried out and the cortical EEG functional networks in the Theta and Alpha frequency bands based on the Pearson correlation were constructed. The network node and global indices were calculated and analyzed by statistic methods. Results Six nodes and four groups of functional connections in the two frequency bands network showed significant changes with cognitive load in the four tasks (p<0.05). With the increment of task loads, the average characteristic path length of the theta-band network significantly decreased from 2.8427 ± 0.0083 to 2.7751 ± 0.0051, while the average clustering coefficient and global efficiency significantly increased from 0.3384 ± 0.0018, 0.4232 ± 0.0015 to 0.3620 ± 0.0020, 0.4432 ± 0.0017, respectively. Conclusion The cortical functional connectivity indices can effectively represent the changes of mental workload in working memory tasks with different information types.