Objective To Design and develop an asset inventory management system based on industrial Internet of Things (IoT) technology to achieve rapid inventory of hospital assets and improve the completene ss of asset records. Methods Using Eclipse 2023 software as the development tool, the system is designed in Java language and adopts a B/S architecture. It uses MySQL database as the backend management software for the system design. The core functional modules include login management, user management, master data management, inventory management, and data processing. Results Black-box testing was conducted, and the system achieved a 100% pass rate in terms of performance. Before and after the application of the system, the average time required for inventory counting was significantly reduced from 120 hours to 16 hours，the results of the sample t-test indicated a significant difference (p < 0.001). Additionally, the average accuracy rate of inventory counting increased from 85% to 98%, with a significant difference (p < 0.01). Conclusion The study not only addresses the core pain point of "discrepancy between records and reality" in hospital asset management, but also, through the deep integration of a standardized master data system and Internet of Things (IoT) technology, provides digital tools for refined asset operation and management. It is of great significance in helping medical institutions optimize resource allocation, activate state-owned assets, and promote the digital transformation of hospitals.

As the core component of sleep health, the design of the pillow directly affects the biomechanical balance of the cervical spine and sleep quality. In the realm of sleep accessories, customized pillows have emerged as a response to the limitations of traditional, standardized options. The fixed morphology and materials of conventional pillows often result in a lack of alignment with individual user needs. The emergence of customized pillows, however, signifies an effort to optimize the user's sleep experience through personalized design. However, contemporary research endeavors continue to encounter several challenges, including the inadequate analysis of biomechanical mechanisms, the restricted generalizability of high-precision modeling, and the substantial expense associated with smart materials. In this paper, we integrate the extant research on customized pillow technology and engage in a discourse on technological development, application scenarios, and future challenges. This analysis elucidates the methodology for customizing personalized pillows to meet users' needs. This is achieved by quantifying the anatomical features of the human head and neck, as well as the stress conditions, to improve sleep quality and health management performance. In the future, it is hoped that the customized pillow will be upgraded from static adaptation to real-time intelligent regulation, in order to provide a more efficient and popularized solution for the management of personalized sleep health.

Objective To construct and validate a deep learning model based on chest X-ray images to automatically identify the position of peripherally inserted central venous catheters (PICCs) and their tips, thereby improving the accuracy and efficiency of catheter assessment. Methods A total of 1,040 CXR images of PICC patients collected retrospectively from Zhejiang Cancer Hospital in 2023 were used, including 800 for training and 240 for validation. An additional 236 images from Taizhou Cancer Hospital were used as an external test set. The test set was independently annotated by multiple physicians, with the interpretation by a thoracic radiology specialist serving as the reference standard. A modified U-Net architecture was adopted, incorporating multi-scale feature fusion and attention mechanisms to achieve catheter segmentation, vertebral recognition, and tip localization. Inference time and tip localization accuracy were recorded, and model performance was compared with manual interpretation for consistency.Results In the test set, the model’s predictions showed no statistically significant difference from the reference standard in terms of accuracy and scores (P>0.05), with good consistency (ICC = 0.907). When the acceptable error range was set to ±1.5, ±1.0, and ±0.5 vertebral segments, the tip localization accuracies were 97.4%, 95.1%, and 77.1%, respectively. The average inference time was (3.48 ± 0.62) seconds. ConclusionThe proposed model can automatically identify PICC and its tip location on CXR images with high accuracy, good consistency with expert interpretation, and efficient inference speed, demonstrating strong potential as a clinical decision support tool.

Anterior cruciate ligament (ACL) injury is a common sports injury of the knee joint, with an increasing incidence rate year by year, seriously affecting the patient's motor function and quality of life. The traditional rehabilitation model has limitations such as high time cost for patients, poor compliance, and difficulty for doctors to supervise and adjust the plan in a timely manner. Due to the intelligent and real-time monitoring features of digital health technology, it provides a new direction for ACL injury rehabilitation. This article reviews the application forms, methods and effects of various digital health technologies such as intelligent wearables and artificial intelligence in ACL patients, analyzes existing problems such as inconsistent technical standards and low patient acceptance, and proposes development strategies such as optimizing technical standards and promoting multi-center research, aiming to provide a reference for the application and development of digital health technology in the field of ACL injury rehabilitation, and help improve rehabilitation quality and the efficiency of medical resource utilization.

Objective This study aimed to identify risk factors associated with postoperative mortality in patients with Stanford type A aortic dissection (TAAD) and to develop a nomogram-based predictive model to assist clinicians in early identification of high-risk patients, thereby optimizing treatment strategies and reducing postoperative mortality. Methods A total of 1,521 patients who underwent surgical treatment for TAAD between January 1, 2019, and July 31, 2024, were retrospectively enrolled from multiple cardiovascular surgery centers in Jiangsu Province. Based on postoperative outcomes, patients were divided into a survival group (n = 1,406) and a mortality group (n = 115). Feature selection was performed using LASSO regression, recursive feature elimination (RFE), and univariate filtering. Independent risk factors for postoperative mortality were identified using univariate and multivariate logistic regression analyses. A nomogram prediction model was constructed based on the identified independent predictors using the Cox regression method via the rms package in R. Model performance was evaluated using calibration curves, receiver operating characteristic (ROC) curves, precision-recall (PR) curves, and SHAP value analysis. Results Age [OR = 1.05, 95% CI (1.02–1.08), P < 0.01], length of hospital stay [OR = 0.92, 95% CI (0.88–0.95), P < 0.01], initial ICU stay duration [OR = 1.11, 95% CI (1.01–1.21), P < 0.05], treatment center [OR = 0.75, 95% CI (0.62–0.92), P < 0.05], mechanical ventilation >24 hours [OR = 3.38, 95% CI (1.48–7.73), P < 0.01], and acute kidney injury [OR = 7.99, 95% CI (2.85–22.3), P < 0.01] were identified as independent risk factors for postoperative mortality. The developed nomogram demonstrated good discrimination with a concordance index (C-index) of 0.78 [95% CI (0.73–0.809), P < 0.01]. The ROC curve showed an area under the curve (AUC) of 0.90, the PR curve indicated an average precision (AP) of 0.89, and the calibration curve showed good agreement (calibration score = 0.89) between predicted and observed outcomes. Conclusion The nomogram developed in this study provides an effective and intuitive tool for predicting postoperative mortality risk in TAAD patients. It enables early identification of high-risk individuals during the perioperative period, thereby facilitating personalized treatment planning and improving clinical outcomes.

Objective To study and analyze the adequacy, completeness, and accuracy of licensing information (e.g., product registration certificates and product instruction manuals) for reminders of safe and effective clinical use of electric anastomoses. Methods By searching domestic and international medical device regulatory official website, national adverse event monitoring system, carry out domestic electric kissing device registrant questionnaire, regularize multi-source data, identify potential risks in the licensed information, analyze and study the risk factors and put forward preventive and control measures. Results There are risks that the licensed information (scope of application, contraindications, precautions, possible complications or adverse events, etc.) is inadequate, incomplete and inaccurate in terms of clinical alerts. The main reason is that the registrant has insufficient understanding of domestic and international regulations, risk management, clinical evaluation, specification and labeling management, design and development process and quality system, especially in pre-market registration and post-market monitoring regulations. Risk prevention and control measures include that the registrant should implement the main responsibility to regularly improve the content of the instructions, and at the same time, strengthen the training for pre-market registration and post-market monitoring, and enhance the supervision, review and monitoring of the safety content in the licensed information. Conclusion Deficiencies in the safety profile of licensed information may lead to clinical overuse and patient harm. Risks can be front-loaded and harms can be minimized through registrant improvement of specifications and enhanced regulation, review, and monitoring.

Objective This study aimed to design an intelligent guidance system from the perspective of cancer patients' medical experiences, addressing the real-world pain point of information asymmetry between doctors and patients during the radiotherapy process. The goal was to optimize the entire logic of seeking medical care for radiotherapy and improve the patient experience with radiotherapy. Methods The intelligent guidance system was based on a B/S (Browser/Server) architecture, utilizing WeChat Mini Programs as the access entry point across multiple terminals. It implemented three core functions: comprehensive data management throughout the radiotherapy process, intelligent Q&A, and personalized recommendations. Results A statistical analysis was conducted to compare the patient-side experience efficiency and healthcare provider-side work efficiency before and after the implementation of the intelligent guidance system. Paired-sample t-tests were employed for both patient-side and healthcare provider-side data.For the patient-side outcomes, the results demonstrated significant reductions in queuing time:Positioning fixation time decreased from 42.6±5 min to 27.2±4 min (t = 7.25, p < 0.01);Simulation localization time decreased from 34.4±5 min to 21.5±5 min (t = 6.93, p <0.01); Verification of repositioning time decreased from 38.6±5 min to 25.3±3 min (t = 8.12, p < 0.01);Radiotherapy implementation time decreased from 33.5±5 min to 19.8±5 min (t = 9.04, p < 0.01).For the healthcare provider-side outcomes, the results showed significant improvements:Daily repetitive work duration decreased from 3.1±0.8 h to 1.7±0.6 h (t = 4.76, p < 0.01);Patient inquiry calls decreased from 38.6 times/day to 12.4 times/day (t = 5.32, p < 0.01). Conclusion The intelligent guidance system not only enhanced medical service efficiency but also helped mitigate the risk of conflicts arising from miscommunication between healthcare providers and patients. While improving the radiotherapy experience for patients, it also contributed to optimizing internal resource allocation in medical institutions and fostering a more harmonious physician-patient relationship, representing a beneficial exploration in healthcare management.

This study aimed to evaluate the consistency of environmental magnetic field baseline measurements, analyze the effects of typical hospital electromagnetic interference sources (color Doppler ultrasound systems, computed tomography equipment, magnetic resonance imaging devices, and power substations) on the ambient magnetic field of magnetocardiography (MCG) systems, and determine the minimum compliant point for environmental magnetic field conditions. The consistency was evaluated using Bland-Altman analysis and intraclass correlation coefficient. . Distance-field strength relationships were established using polynomial fitting to calculate minimum ambient magnetic field compliance points for different interference sources (magnetic field variation <5000 nT, spectral peak <0.1 nT, noise <15 pT). The cumulative interference effect of multiple CT scanners operating simultaneously was tested under a triple-shielding system (μ-metal, active compensation, and environmental control).The trend of ambient magnetic field variations measured by the fluxgate gradiometer is consistent with the MCG monitoring data (ICC >0.75). The minimum ambient magnetic field compliance points for color Doppler ultrasound, single CT scanner, MRI equipment, and power substations were 0.35 m, 4.7 m, 9.15 m, and 16.57 m, respectively. A positive correlation exists between the electromagnetic interference (EMI) strength of the device and its operating power. Simultaneous operation of multiple CT scanners increased interference, but the triple-shielding system effectively suppressed it (magnetic field variation <5000 nT, noise <15 pT). The fluxgate gradiometer can serve as a reliable supplement to MCG. The fluxgate gradiometer can serve as an effective complement to magnetocardiography (MCG) systems. This study provides critical data support for MCG facility siting, equipment layout, and shielding design, advancing the transition of MCG from traditional shielded rooms to broader clinical applications.

With the rapid development of medical imaging technology, CT imaging plays an important role in disease diagnosis. However, conventional energy-integrating detector CT (EID-CT) has limitations, especially in detecting small lesions and complex structures, due to image quality and radiation dose constraints. Photon-counting CT (PCCT), as an emerging technology, is based on the direct photon detection and energy resolution capabilities of semiconductor materials (such as cadmium telluride CdTe, silicon Si, etc.), effectively addressing the shortcomings of traditional CT. This paper aims to systematically review the technical advantages, clinical application progress, and future development directions of PCCT. In addition to focusing on its application effects and advantages in cardiovascular, neurological, thoracic, abdominal, and musculoskeletal imaging, this paper will also explore the technical challenges faced by PCCT, such as equipment costs, data processing complexity, and limitations to clinical adoption. It is hoped that this review will provide a reference for medical imaging and clinical practitioners and promote the widespread application of this technology in clinical practice.

Objective To improve the safety and fine management level of the combination of hospital uninterruptible power supply (UPS) and lithium iron phosphate battery through intelligent management system. Methods A set of intelligent management system based on cloud side architecture was designed. The data was collected in real time through the Internet of things device (collection end) and uploaded to the local monitoring host (side end). The data was analyzed and visualized at the side and uploaded to the cloud, so as to realize the centralized management of equipment, personnel, risk and operation and maintenance. By comparing the number of risk prevention, emergency response efficiency and economic cost before and after the system went online, the application effect was analyzed. Results The hospital achieved 100% digital monitoring of 44 ups and 26 sets of lithium iron phosphate battery systems. Since the system went online (the research period is 2 years), 76 UPS system risks have been prevented in advance. The average emergency response time is 2.34 minutes, and the average on-site emergency disposal time is 9.53 minutes. The input of manual patrol inspection is reduced from 3 people/day to 3 people/quarter. In addition, through the strategy of lithium iron phosphate system discharging in peak section and charging in valley section, it can also achieve the effect of saving electricity charges through the difference between peak and valley electricity prices. Conclusion The system effectively improves the safety and management efficiency of the combination of ups and LiFePO4 battery, and meets the needs of the hospital for high reliability and efficient management of the power supply system.

Objective This study aims to analyze the application effectiveness of a life support medical equipment sharing and borrowing system based on the Internet of Things. Methods Implement an innovative equipment management plan in the equipment department of a hospital in Shenzhen, and build a shared borrowing system for life support medical equipment. During the research process, 106 devices were selected as the research subjects, including 55 monitors, 15 invasive ventilators, 5 non-invasive ventilators, 20 infusion pumps, 3 defibrillators, 6 nutrition pumps, 1 ECMO (extracorporeal membrane oxygenation), and 1 electrocardiograph. The equipment and personnel remained unchanged before and after application, and were stored in a centralized location. Before the system application, it was in a purely manual state from June 2022 to August 2023, serving as the control group; The time after application is from September 2023 to November 2024. For the observation group, data analysis was conducted to compare the equipment borrowing time, equipment borrowing error rate, calculation and audit personnel work error rate, shift handover time, and equipment final maintenance qualified units before and after the platform application. Results The construction of equipment sharing platform and centralized management platform within the hospital has significantly improved the management level of equipment. After the application of the system, the equipment borrowing time and shift handover time were significantly reduced, the borrowing error rate, calculation and audit personnel error rate were significantly reduced, and the number of qualified equipment terminal maintenance was significantly increased (P<0.05). Conclusion Building a life support medical equipment sharing and borrowing system based on the Internet of Things is an effective way to improve equipment management level and operational efficiency. The borrowing time of devices after applying the system is significantly shortened, and the deployment error rate is significantly reduced; The automation level of equipment borrowing work has been improved, the error rate has been reduced, and work efficiency has been enhanced; The handover process for equipment borrowing work is smoother, reducing unnecessary time consumption; The standardization level of equipment borrowing work has been improved, and the final maintenance work of equipment has been better guaranteed, improving the reliability and service life of equipment. Worth promoting and using.

Objective This study aims to explore the application pathways and practical effectiveness of data intelligence technologies (such as big data analytics, machine learning, and automated identification) in the full-process hospital consumables management—including procurement, inventory, usage, and settlement—under the implementation of the volume-based procurement (VBP) policy. The goal is to provide new insights for achieving refined management in hospitals. Methods In the procurement prediction module, big data analytics were employed to deeply mine historical procurement records and clinical usage trends. A predictive model was constructed using machine learning algorithms to achieve accurate forecasting for procurement needs. Results After system implementation, the allocation time for routine medical consumables was significantly reduced from the original 5–10 minutes to under 1 minute, resulting in an efficiency improvement of 80–90%. The overall efficiency of the consumables management process increased by approximately 82%, with inventory turnover days reduced by 25% and the rate of expired and overstocked items decreased by 40%. Timeliness in consumables acquisition on the clinical side improved markedly, and departmental satisfaction rose from 78% to 93%. Conclusion The data-intelligence-based consumables VBP management system significantly enhances hospitals' operational responsiveness and resource allocation efficiency under the VBP policy. With features such as traceability, high efficiency, and low risk, it serves as a key enabler for the transformation of modern hospital consumables management and holds strong potential for wider application.

Objective To design and develop an online medical image annotation training platform based on the Django framework, addressing the challenges of annotator shortages, low efficiency, and high costs in AI-driven healthcare. Methods The platform backend was built using the Django framework, integrated with HTML, CSS, and JavaScript for frontend interaction. RESTful APIs ensured data communication, while Token-based authentication enhanced security. The assessment module utilized Python's shapely library to evaluate annotation accuracy via the Intersection over Union (IoU) algorithm. A multimodal annotation tool, developed with JavaScript Canvas API, supported 2D/3D image labeling (e.g., bounding boxes and polygons). A dynamic task allocation algorithm optimized annotation workflows. Clinical and imaging students underwent online training and assessments, after which the platform automatically assigned annotation tasks and provided feedback to task publishers and affiliated institutions.Results During the trial operation period of the platform (June 2024 May 2025), the annotation efficiency of the trainees who received training increased by 23.63% compared to the traditional training mode, and the average annotation accuracy reached 85.5%, which was 73.5% higher than the untrained group. The standardized curriculum of radiology experts has increased the clinical rationality rate by 54.16%. The annotation consistency (IoU ≥ 0.85) of the platform on different modal data reached 88.89%. The task allocation algorithm has increased the completion rate of high difficulty tasks from 68% to 89%. The standardized assessment pass rate for platform students is 90%, providing effective personnel reserves for medical AI enterprises.Conclusion The Django-based platform enhances annotator expertise, addresses the demand for large-scale medical image annotation, and delivers high-quality data support for AI-driven healthcare applications.

Objective Aiming at the problems of slow response speed, low temperature control accuracy, and inconvenient operation of traditional medical contrast agent temperature control devices, a medical contrast agent temperature control system that can be controlled through an APP is developed to achieve portable control of contrast agent temperature.Methods The system is designed based on the STM32F103C8T6 microcontroller, and uses multi-source sensor fusion technology to monitor the temperature of the contrast agent in real time. Combined with fuzzy PID control algorithm, it achieves high-precision constant temperature control of ± 0.2℃. At the software level, a Wi-Fi communication mechanism has been constructed, and an APP has been developed to achieve temperature setting, real-time monitoring, and abnormal alarm functions. Results The experimental results demonstrate that Under an ambient temperature of 25℃, the system heats 1000mL of contrast agent to the target temperature of 37℃ with an average regulation time within 90 seconds. During the steady-state phase, it demonstrates a maximum temperature fluctuation of 0.139℃ and a standard deviation of 0.014℃, meeting medical-grade temperature control standards. Conclusion The research achievement provides reliable temperature protection for medical examinations such as CT enhanced scanning and angiography, which helps to improve image contrast and reduce the risk of vascular spasm caused by cold stimulation. It has significant clinical promotion value.

Objective To study the construction and application of evaluation scale for clinical use of high-value medical consumables in order to provide a scientific method for clinical use evaluation of high-value medical consumables that have been admitted to the medical institutions. Methods Based on literature analysis and Delphi method, a three-level index pool was constructed to evaluate the clinical use of high-value medical consumables. Then, the weights of each level of indicators were determined by analytic hierarchy process, and each index was quantified in turn. Finally, the evaluation scale was constructed. A case study was carried out in a third class hospital. Results The evaluation scale consisted of 4 first-level indicators,9 second-level indicators and 24 third-level indicators. The weights and scores of each indicator were determined. The example proved that the evaluation scale was scientific and practical. Conclusion The evaluation of clinical use of high-value medical consumables based on evaluation scale was scientific and practical. This method provided scientific basis and methodological reference for the managers of medical institutions to improve the management level of high-value medical consumables, improve medical quality and control medical cost.

Objective To evaluate the accuracy of dose delivery in carbon ion therapy for ocular melanoma based on offline positron emission tomography/computed tomography (PET/CT) nuclide imaging, and to validate its reliability in clinical treatment planning. Methods This study included 34 ocular melanoma patients treated with carbon ion therapy. Offline PET imaging was performed immediately after treatment to obtain β⁺ nuclide activity distribution. By comparing PET activity distribution with the dose distribution predicted by the treatment planning system (TPS), the dose conformity index (DCI) and PET conformity index (PCI) were calculated to assess three-dimensional (3D) dose delivery characteristics. The ratio of PCI to DCI (dose delivery index, DDI) was used to quantify the accuracy of dose delivery. Results At the 95% isodose level, DCI₉₅% and PCI₉₅% were 59.6%±14.7% (P<0.01) and 58.1%±14.5% (P<0.01), respectively. At the 90% isodose level, DCI₉₀% and PCI₉₀% were 43.0%±11.2% (P<0.01) and 42.0%±11.0% (P<0.01), respectively. The dose delivery accuracy indices were DDI₉₅%=99.5%±8.6% and DDI₉₀%=97.5%±11.8%, with statistically significant differences (P<0.01). The results demonstrated good consistency between PET-derived nuclide distribution and planned dose distribution within the target volume, indicating that PET activity effectively reflects relative dose distribution. Moreover, results demonstrated that both post-treatment transfer time to the PET suite and target location (left vs. right eye) significantly impacted DDI values. Conclusion Offline PET nuclide imaging can serve as an effective tool for dose verification in carbon ion therapy for ocular melanoma, particularly in assessing target dose deposition with high accuracy. This study provides important insights for online dose monitoring in carbon ion therapy and the correlation between dose and PET activity.

In order to solve the problems of insufficient data security, weak protection of patient privacy and low efficiency of cross-institutional business collaboration in traditional hospital business systems, an optimization mechanism based on hierarchical chain-cloud architecture and smart contracts is studied and proposed to improve hospital data protection and business process optimization. The optimized system integrates the advantages of blockchain and cloud databases, and realizes the automatic execution of key business processes such as medical record authorization and claims review through smart contracts. The experimental results show that the average access time of the optimized system is 0.49±0.07 seconds, and the illegal access detection rate reaches 98.67%. The response times for medical record authorization and claims review were 0.27±0.04s and 0.85±0.06s respectively, and the traceability reached 99.92% (p<0.001). The crash rate of the optimized system in high-concurrency scenarios is controlled at 4.63%, and the recovery time does not exceed 2.28 seconds. The research verified the advantages of the proposed optimization mechanism in terms of efficiency, security and scalability, providing a practical reference for the construction of the new generation of medical information systems.

Objective To propose a multivariate nonlinear prediction model for thyratron lifespan based on accelerator workload, aiming to achieve lifespan prediction of key components through dynamic workload monitoring, thereby providing a reference for preventive maintenance of linear accelerators. Methods By integrating and analyzing 4,485 sets of historical operational data from four Varian linear accelerators in Zhejiang Cancer Hospital between 2022 and 2024, a multivariate nonlinear model was developed with daily energy load and daily beam-on time as independent variables. The model was tested using 484 real-world operational datasets and evaluated using goodness-of-fit (R²) and mean absolute percentage error (MAPE). Results The model demonstrates strong model performance, with a goodness-of-fit of R²=0.913 and residuals conforming to a normal distribution. Predictions for 26.4% of test data showed errors below 10%, and the MAPE was 21.9%. Conclusion This model provides a quantitative tool for assessing thyratron failure risks, which can optimize preventive maintenance strategies for Varian linear accelerators, ensure continuity of radiotherapy, and reduce downtime losses.

Objective Utilizing a depth camera to extract and simulate the real-time movement of the surface markers on the patient's thermoplastic mask in real time, to assist in position monitoring during radiotherapy. Methods The transformation relationships among the iso coordinate system, the depth camera coordinate system, the DICOM coordinate system and the TPS coordinate system are obtained by the calibrated module, thereby achieving the unification of the multi-coordinate system. The pointcloud data collected by the depth camera is fitted in real time to identify the motion trajectory of the markers and convert it to the unified coordinate space. Singular value decomposition (SVD) registration analysis is carried out with the coordinates of the markers in the simulation CT image, and then the six-dimensional pose error is calculated. Finally, the real-time monitoring of body position during radiotherapy is achieved. In this experiment, a preset human error was introduced into the dummy model to verify the measurement accuracy of the system. The calculated error obtained from the experiment was compared with the set error, and the mean and standard deviation were statistically analyzed. The paired t-test was used to evaluate the significance of the difference between the two groups of data. Meanwhile, calculate the Pearson correlation coefficient between the two groups of errors, analyze the degree of correlation, and record the time required for the system operation.Results The errors of the system in the left-right, head-foot, and front-back directions were (0.199±0.07), (0.202±0.058), and (0.166±0.045) cm respectively; the errors in the yaw, pitch, and roll directions were (0.169±0.14), (0.117±0.095), and (0.242±0.139) ° respectively. The six-dimensional errors calculated by the system were highly correlated with the actual movement data, and the time taken for one monitoring was approximately 0.226 s. Conclusion Depth cameras can accurately simulate the markers on the patient's thermoplastic mask and register them with the marks in the simulation CT, thereby achieving accurate and real-time position monitoring during radiotherapy, which has certain clinical feasibility.

Objective The distribution and use difference of DR equipment in the medical imaging department of public hospitals in the province were investigated. Methods Electronic questionnaire investigated the medical imaging department of secondary and above public hospitals (western comprehensive and specialty), including the number of DR equipment, brand, whether it is equipped with double flat panel detectors, use, etc. Using SPSS 27.0 software, the basic situation of the provincial public western comprehensive and specialized hospitals and the details of DR equipment in the medical imaging department were analyzed differently. Results A total of 140 valid questionnaires were collected, That is, 140 public hospitals in the province, More DR equipment, more DR equipment, more complete and more versatile, Statistically significant difference (p<0.05), Not statistically significant in brand (p>0.05), DR equipment in secondary or above public general hospitals and tertiary public specialized hospitals tends to be imported brands, The DR equipment brand owned by the secondary public specialist medical imaging department, Domestic and imported are almost equivalent; Compared with the tertiary public western comprehensive medical imaging department, it prefers the suspension double-plate DR equipment with high examination efficiency and strong ease ofuse, and the difference is significant (p<0.05).Conclusion Analyze the configuration of DR equipment in secondary and above, grasp the status and demand of DR equipment, and comprehensively, clarify the advantages and disadvantages, helps the hospital improve the medical service level and efficiency, and meet theclinical demand and patient expectation; further understand the cost performance, clinical recognition and market share of domestic DR equipment providing a reference for advancing the research and development of domestic medical imaging equipment.

Objective The oropharyngeal cavity is not a regular spatial structure. In the face of difficult airways, how to quickly deliver the guide strip from the incisors to the vicinity of the glottis, accurately perceive its position, and then quickly bypass the epiglottis and pass through the glottis to enter the trachea remains a difficult pain point to solve. Using the oropharyngeal airway guidance designed in this paper and the magnetic positioning navigation technology, the aim is to accurately and quickly complete the tracheal intubation operation while reducing soft tissue injury. Methods This magnetic positioning and navigation tracheal intubation system consists of a flexible oropharyngeal airway, a flexible magnetic positioning device, a flexible magnetic guidance strip and a navigation magnet. First, insert the soft material oropharyngeal airway into the oral cavity, adjust its position, and fully expose the epiglotis and larynx. The flexible magnetic guide strip is inserted along the soft material oropharyngeal airway, so that the guide strip will not bend or turn around from the incisors to the larynx during blind insertion. The magnetic positioning module can track and monitor the tip position of the flexible magnetic guide strip outside the body. When the magnetic guide strip reaches the vocal cavity area, the magnetic force of the external navigation magnet attracts and navigates the magnet at the tip of the magnetic guide strip, causing it to bend in a directional manner and enter the trachea. Then, insert the tracheal intubation along the magnetic guide strip and remove the magnetic guide strip. The soft oropharyngeal airway can remain in the oral cavity as a dental pad and a fixed catheter. On the dummy , two students without a medical background conducted blind tracheal intubation experiments 50 times each. The learning time, the success rate of one intubation, and the time spent on intubation were counted to evaluate the advantages of this system during the intubation process. Results With the guidance of the oropharyngeal airway and the assistance of the magnetic positioning navigation system, the average first-attempt success rate of blind tracheal intubation on a manikin reached 94%. Among the participants, 86% achieved a success rate of 90% or higher, and 98% achieved 80% or higher, demonstrating good consistency and reliability. In terms of intubation time, all participants were able to complete the procedure within 37 seconds. Among the total of 470 successful intubations, 89.1% were completed within 30 seconds, and 51.4% were completed within 25 seconds. Conclusion Based on the oropharyngeal airway guidance and magnetic positioning navigation technology showing advantages in tracheal intubation, it is expected to effectively increase the one-time success rate of tracheal intubation in difficult airways, shorten the operation time, and have low dependence on the operator. It has good universality and application prospects, providing useful inspirations for the development of intelligent medical robots for tracheal intubation and is worthy of further research and clinical promotion and application.

Cardiac arrest remains a major global public health challenge. The core mechanisms underlying its high mortality and disability rates involve prolonged cerebral exposure to a "no-flow" state and ischemia-reperfusion injury triggered by hypoperfusion during resuscitation. Real-time acquisition of cerebral perfusion and oxygenation parameters during resuscitation is therefore critical for improving outcomes. Regional cerebral oxygen saturation, a non-invasive and continuous monitoring indicator of cerebral tissue oxygenation, enables bedside dynamic assessment through near-infrared spectroscopy technology. This approach has been widely adopted in critical care, rehabilitation medicine, and surgical settings. In the context of cardiopulmonary resuscitation, rSO₂ monitoring provides real-time visualization of cerebral oxygen supply-demand balance, offering crucial evidence for optimizing chest compression quality, predicting return of spontaneous circulation, evaluating cerebral blood flow metabolism, guiding targeted temperature management, assessing neurological prognosis, and informing extracorporeal cardiopulmonary resuscitation decisions. These applications underscore its unique clinical value in CPR. This review systematically summarizes recent technological advancements and multi-scenario applications of rSO₂ monitoring in the CPR field, aiming to provide evidence-based guidance for optimizing resuscitation strategies and improving patient prognoses.

Objective Analttic Hierarchy Process (AHP) was used to construct an index system based on the performance requirements of ECG monitor accessories in different medical scenarios, so as to guide clinical selection of the most appropriate type of accessories. Methods Six departments of pediatrics, internal medicine, surgery, anesthesia surgery, emergency department and critical care medicine in our hospital were selected as the research objects. The index system was initially established through expert interviews and focus group discussion, and then the weights of each index were determined by AHP and Likert scale. Finally, the evaluation scores of departments before and after the use of attachments were verified through experiments. Results The accuracy and stability of the Mindray original SpO2 probe (A=9.714, A'=5.714) were higher than that of Xiaocai brand (B3=10, B3'=5). The convenience, flexibility and maintainability of the original SpO2 probe of the medical staff were significantly better than that of Xiaocai brand (t=3.458, 4.614, 5.308, P < 0.05), and the comfort feeling of the original SpO2 probe of the patients was also significantly higher than that of Xiaocai brand (t=11.484, P < 0.05). Combined with the index system, it is concluded that the original SpO2 probe (comprehensive score 7.867) is more suitable for internal medicine departments than Xiaocai brand (comprehensive score 6.078). Conclusion The performance requirement index system of monitor accessories based on the characteristics of different medical scenarios was constructed to provide decision-making basis for clinical departments to select the most suitable monitor accessories.

Objective To construct an evaluation index system for centralized procurement of electrophysiological consumables in Diagnosis Related Groups ( DRG ) based on the influence of policy effect on medical treatment, medical insurance and medicine, so as to provide reference for subsequent policy formulation.Methods The initial framework of the index system was developed through literature research and semi-structured interviews. The Delphi method was used to conduct two rounds of consultations with 40 experts, and the direct scoring method of experts was used to determine the weight of indicators at all levels.Results The effective recovery rates of the two rounds of expert consultation questionnaires were 88.89% and 100.00%, respectively. The expert authority coefficient was expressed by familiarity and judgment coefficient. The expert authority coefficient in this survey was 0.85 (Cs = 0.90, Ca = 0.80), indicating that experts had high authority in this field. The Kendall's coordination coefficients (Kendall's W) were 0.2080 and 0.2350, respectively. Finally, the DRG electrophysiological consumables centralized procurement evaluation index system was determined to include 3 first-level indicators, 7 second-level indicators, and 24 third-level indicators. Conclusion The evaluation index system of centralized purchase of DRG electrophysiological consumables constructed in this study is scientific, reliable and operable, which can provide reference for relevant decision-making departments.

Objective  To address inefficiencies in traditional medical consumables management—such as poor information tracking and inadequate supervision—we developed an SPD (Supply-Processing-Distribution) management platform based on the Unique Device Identifier (UDI) system. This aims to enhance management efficiency, healthcare quality, and safety.Methods  Core requirements were identified through user research. The platform employs a UDI-centered architecture with three layers: external data interfacing, internal UDI-based materials management, and internal system integration. A layered technical framework and six functional modules enable closed-loop, full-process management. Performance metrics from 20 clinical departments were compared pre- and post-implementation.Results  After implementation, weekly average time consumption for requisitioning, collection, inventory counting, and billing decreased by 49.6%, 46.2%, 46.3%, and 48.0%, respectively (all P<0.001). The billing error rate for high-value consumables dropped from 3.3% to 1.3% (P<0.001), indicating significant improvements in efficiency and accuracy.Conclusion  By deeply integrating technological innovation with workflow optimization, this platform resolves key challenges in traditional management and provides a replicable solution for standardizing and digitizing medical supplies management.

Objective Taking municipal hospitals in Beijing as an example, an in-depth analysis is conducted on the procurement, operation, maintenance costs, and usage data of both domestic and imported large-scale medical equipment. This analysis aims to provide references and suggestions for equipment evaluation and the healthy development of domestic medical equipment. Methods Data on CT, MRI, and X-ray equipment in use in 22 municipal hospitals in Beijing were collected through research,the chi-square test was used to analyze the cost differences from three aspects: "comparison of procurement prices between domestic and imported products", "comparison of procurement prices between domestic products of domestic and imported brands", and "comparison of equipment maintenance prices", and the actual usage and purchase ratio of imported and domestic equipment products are compared, so as to analyze the difference in equipment availability.. Results The procurement prices of domestic products for "64-slice (inclusive) - 128-slice (exclusive) CT", "1.5T and below MRI", "1.5T and above MRI", "DSA", "DR", and "C-arm" were lower than those of imported products (P < 0.05), with a reduction range of 12.97% - 50.8%. Among "64-slice and below CT", "64-slice (inclusive) - 128-slice (exclusive) CT", "1.5T and below MRI", "DR", and "C-arm", only the procurement prices of domestic brand and imported brand domestic products for C-arm had no significant difference (P = 0.711), while the procurement prices of domestic brand products for the other categories were all lower than those of imported brand domestic products (P < 0.05), with a reduction range of 19.68% - 31.91%. For "64-slice (inclusive) - 128-slice (exclusive) CT", "1.5T and below MRI", "DR", and "C-arm", the full-machine maintenance rates after the warranty period for domestic and imported products were basically the same (P > 0.05). In 2020, the proportion of domestic equipment purchases exceeded that of imported equipment for the first time, and remained stable in the past five years.There was no significant difference in the average usage of domestically produced and imported CT and MRI equipment (P=0.260 and 0.055). Conclusion Overall, the procurement prices and maintenance costs of domestic products are lower than those of imported products, The availability and market acceptance of domestic products are not lower than those of imported products. Medical institutions should strengthen their research and demonstration to reduce equipment procurement and operating costs while meeting clinical needs.

Objective Given the variations in key performance of negative pressure isolation chambers across multiple scenarios, this study presents a research methodology to examine how actual operating conditions affect their critical performance. The proposed approach aims to provide a reference for evaluating the practical operational performance of such equipment. Methods Based on the actual operational scenarios and the technical specifications of YY 1885–2023 Negative-Pressure Isolation Capsules for Transporting Infectious Patients, this study designs test methods for the key performance of isolation capsules under three representative scenarios: Continuous use of portable oxygen concentrators by infectious patients inside the capsule, Temperature fluctuations during multi-scenario transportation, Variations in power supply modes and states. Devices including voltage stabilizers, micro-differential pressure gauges, and anemometers are employed to test and analyze the effects of the above scenarios on the negative pressure, air exchange rate, airflow velocity, and filter integrity of the capsules. Results The key performance of negative-pressure isolation capsules for transporting infectious patients is influenced by the three scenarios: When the oxygen concentrator trigger frequency is low or flow rate is high, the negative pressure fluctuates by −5% to 2.5%, and the airflow velocity changes by −3.8% to 2.3%. Temperature variations affect the negative pressure by −34.4% to 12.5%, airflow velocity by −10.7% to −1.8%, and air exchange rate by −9.5% to 4.8%. Changes in power supply modes/states impact the negative pressure by 2–17 Pa, airflow velocity by 0.03–0.05 m/s, and air exchange rate by 1–5 times/h. Conclusion The proposed research methods are feasible and generate valid test results, aiming to provide references for the revision and upgrading of standards for such equipment.

Objective To address the challenge of intelligent fault diagnosis for the main control board of soft tissue therapy instruments in the absence of circuit diagrams, an efficient and accurate diagnostic method is proposed. Methods Electrical signal data were collected from 45 pins across 7 ports of the main control board, and combined with symptom information including panel displays, button status, and output waveforms to construct a 198-dimensional multimodal feature set. Support Vector Machine Recursive Feature Elimination (SVM-RFE) was applied to select key features, retaining the optimal 60 features to enhance model generalization. An improved Sparrow Search Algorithm (ISSA) incorporating adaptive inertia weight adjustment and hybrid mutation strategy was designed to optimize the SVM kernel parameter γ and penalty factor C, thereby establishing the ISSA-SVM fault diagnosis model. Results Experimental results showed that the ISSA-SVM model outperformed PSO-SVM, GA-SVM, and original SSA-SVM models in classification accuracy (97.62%) and precision (97.68%). The model achieved an average convergence iteration of 34 and a runtime of 28.5 seconds, demonstrating superior optimization efficiency. The recognition accuracy for all seven types of control board states exceeded 95%, significantly improving the precision and robustness of fault identification. Conclusion The proposed ISSA-SVM fault diagnosis method effectively addresses the intelligent diagnosis problem of main control boards in medical equipment without circuit diagrams, offering strong practical value and broad application prospects.

To address the issues of insufficient concurrent processing capacity of traditional electronic medical record systems, as well as inefficiency and security flaws in data access control methods, the research designs an electronic medical record system based on a cloud platform architecture and employs a ciphertext-policy attribute-based encryption algorithm to optimize data access. The effectiveness of the system is evaluated by testing the response time and throughput of the system under different concurrent scenarios, as well as the key generation time, encryption and decryption durations of the ciphertext-policy attribute-based encryption algorithm with different numbers of attributes. The results show that the system has a response time of less than 1200ms and a throughput of 2700 under the 5000-concurrent scenario. At 12K concurrency, the maximum throughput is 3924, with all response times being less than 3000ms. For the ciphertext-policy attribute-based encryption algorithm, the key generation time is only 1247ms when the number of attributes is 25, and the encryption and decryption durations are only 824ms and 901ms respectively when the number of attributes is 20. This indicates that the cloud platform-based electronic medical record system and data access optimization method designed in the research have strong feasibility and practical application potential, effectively solving the drawbacks of traditional systems, providing strong support for the efficient and secure operation of electronic medical record systems, and being of great significance to the informatization construction of the medical industry.

Echocardiography is a core non-invasive tool for diagnosing cardiovascular diseases. However, its dynamic imaging characteristics make it difficult to standardize image acquisition and the analysis process highly dependent on the operator's experience, which leads to significant differences in diagnoses among different physicians and limits the diagnostic efficiency and objectivity. In recent years, the breakthrough progress of artificial intelligence (Artificial intelligence, AI) especially deep learning (Deep learning, DL) technology has provided new opportunities for optimizing the ultrasound workflow. This article mainly reviews the latest research progress and application value of AI technology based on DL in echocardiography image analysis, focusing on the application of AI technology in image segmentation, cardiac function assessment, and cardiovascular disease diagnosis, etc. At the same time, this article also deeply analyzes the current challenges and looks forward to future development directions, aiming to provide a reference basis for clinical physicians to understand the application value of AI tools and achieve more efficient and accurate heart disease diagnosis.

Objective Exploring the Feasibility of Knowledge Graph-Enhanced Deep Learning for AI-Driven Quality Control in Mammography.Methods A retrospective analysis was conducted on 6,961 mammographic studies. A multi-tiered knowledge graph was constructed by synthesizing empirical evidence and domain expertise. Ten board-certified radiologists performed three rounds of annotation guided by this knowledge graph. The annotated data were utilized to train an AI-based quality control (AI-QC) model. An expert adjudication panel established the reference standard for evaluating AI-QC performance. To evaluate the application effectiveness of AI-based quality control, the weighted average precision was adopted, and a paired samples t-test was conducted to compare the precision differences between AI-based and manual quality control. Results  The AI-QC system achieved mean AUC values ≥0.940 across all four standard mammographic views. The right craniocaudal (RCC) view attained the highest AUC of 0.963. Following expert adjudication, the WAP exceeded 0.970 for all views, demonstrating statistically superior performance compared to manual QC. Conclusion The knowledge graph-deep learning integrated approach for mammographic QC demonstrates superior performance to manual methods, providing Objective and effective image quality evaluation with promising clinical applicability.

Objective Based on remote photoplethysmography and 3D convolutional neural networks, an intelligent heart rate monitoring method is constructed to solve the problem of inaccurate detection results in the existing vital sign detection methods. Methods The study proposes to combine remote photoelectric volumetric tracing with 3D convolutional neural network, and introduce singular spectrum analysis and adaptive filtering algorithm to optimize it. The metrics selected for the validity assessment experiments include the squared absolute error between the predicted value and the true value, the root-mean-square error, the Pearson's correlation coefficient, the consistency assessment plot, and the confidence interval. Results The ablation experiments revealed that the proposed algorithm can effectively reduce the mean absolute error and root mean square error between the predicted and true values, and the lowest of the two can reach 0.46 bpm and 1.92 bpm.The confidence intervals of the proposed method are [-2.5, 2.5], and the fit between the predicted value curves and the true heart rate curves is also high. Conclusion he vital sign detection method based on remote photoelectric volumetric tracing with 3D convolutional neural network can accurately predict heart rate values and has significant advantages in long-term vital sign monitoring.

In order to improve the accuracy of predicting the residual risk of urinary stones after ureteroscopic lithotripsy and reduce the postoperative intervention rate, a post lithotripsy stone residual risk prediction model based on optimized Extreme Gradient Boosting (XGBoost) was proposed. This model combines an improved sparrow search algorithm to optimize the XGBoost model, and improves the quality of hyperparameter search by introducing Circle chaotic mapping initialization population, nonlinear decreasing inertia weight, and Levy flight strategy. A comparative experiment was conducted based on the CICIDS 2018 dataset and a real postoperative follow-up dataset from a certain hospital. The experimental results show that the optimized XGBoost model has an AUC value of 0.94 on both the training and testing sets, with a classification accuracy of 92.49%. In normal traffic scenarios, the AUC is as high as 0.96, and the F1 score and recall rate are both 0.91, significantly better than the comparison model. The results indicate that the model can stably predict the risk of residual stones, providing reliable support for preoperative risk assessment and personalized treatment.

Objective To address three major challenges in hysteroscopic image lesion detection—high computational complexity, low recall rate for small lesions (<5mm), and difficulties in deploying models on primary care devices—this study proposes a lightweight real-time detection model. Methods A Multi-Scale Feature Pyramid (MSFP) architecture was constructed to enhance small lesion feature extraction through cross-layer feature fusion. A Depthwise Separable Self-Attention (DSSA) module was designed to decompose feature interactions into intra-channel self-attention and pointwise convolution, significantly reducing parameter count. A Dynamic Region Attention (DRA) mechanism was introduced to dynamically adjust weights in 4×4 grid regions based on lesion distribution probability matrices, improving localization robustness. Gradient-aware pruning and 8-bit integer quantization were combined to achieve efficient model compression. Results On the HS-CMU dataset (3,385 hysteroscopic images), the model achieved an AP@0.5 of 82.1% with only 2.1M parameters and an inference speed of 48 FPS. The recall rate for small lesions (<5mm) improved to 76.3%, and under Gaussian noise (σ=0.1) and motion blur (kernel=15) interference, the recall rate fluctuation remained below 3.5%, significantly outperforming mainstream models. Conclusion The MSFP-DSSA-DRA co-design achieves a balance between accuracy and efficiency, providing a low-cost, highly robust real-time lesion localization solution for primary care settings. This model can assist physicians in improving disease detection rates and holds significant clinical value for widespread adoption.

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.