Blood gas analysis is widely applied in medicine, with a development history of over 200 years. Currently, the commonly used clinical method for blood gas analysis is invasive blood gas detection technology. Although this technology has been well-developed, it still presents numerous issues in clinical practice, such as infection, bleeding, and discontinuous monitoring caused by invasiveness. To address the limitations of invasive techniques, non-invasive blood gas monitoring technology has emerged and continued to evolve. Traditional non-invasive technologies, such as pulse oximeters and capnogram, have limitations in accuracy. Among new technologies, gas chromatography-mass spectrometry, tunable diode laser absorption spectroscopy, and photoacoustic spectroscopy have been gradually applied. This paper elaborated on the development history and detection technologies of blood gas analysis, explored the development of non-invasive blood gas analysis technology, with a particular focus on the application of cavity ring-down spectroscopy based on optics in non-invasive blood gas analysis monitoring technology, and prospected the future development direction of non-invasive blood gas analysis technology, aiming to provide references for the research and development of related equipment.

As a crucial method for monitoring children’s growth and development and diagnosing endocrine diseases, bone age assessment is mainly performed by analyzing X-ray images of the hand and wrist. However, the application of traditional X-ray imaging technology is often affected by background interference, which reduces the accuracy of assessment results. In recent years, deep learning technology has been widely applied in the field of bone age image processing, demonstrating significant advantages due to its ability to automatically identify and extract complex features. This paper reviewed the research progress of deep learning in addressing background redundancy in bone age images, focusing on methods such as region-of-interest extraction, background segmentation technology, and attention mechanisms. These methods can effectively eliminate redundant background information and improve the accuracy and efficiency of bone age assessment. Furthermore, this paper discussed the limitations of existing technologies and future development directions, aiming to provide new research ideas and practical guidance for the field of bone age assessment.

Total knee arthoplasty (TKA) is an effective method to treat severe knee lesions, but postoperative rehabilitation treatment is very important. In recent years, the postoperative rehabilitation treatment of TKA has achieved remarkable results. This paper comprehensively expounded the technical methods, including the innovation and optimization of physical therapy, such as early rehabilitation training, muscle strength training, joint range of motion training, etc., and emphasized the important role of rehabilitation AIDS in patients’ self-care and rehabilitation effect. The research progress of rehabilitation treatment after TKA was reviewed to provide reference for clinical practice and improve the quality of rehabilitation and life of patients.

As a key medical device that provides oxygen-enriched or pure oxygen in a hyperbaric environment, large-scale medical oxygen chambers have been increasingly widely used in clinical medicine. They are mainly used for treating hypoxic diseases, promoting wound healing, and alleviating symptoms of various chronic diseases. Their performance and quality are directly related to treatment efficacy and patient safety. This paper reviewed the key performance indicators of large-scale medical oxygen chambers and their influencing factors, systematically analyzed domestic and international methods for evaluating the performance and quality of oxygen chambers, and focused on discussing the research progress and technical specifications in aspects such as chamber structure, sealing performance, oxygen concentration control, environmental stability, and safety testing. Additionally, combined with industry development trends, it analyzed the deficiencies in performance evaluation and prospected for future research directions and technical breakthroughs, including intelligent detection, standardization system construction, and full-life-cycle quality management, aiming to provide references for the research and development, production, and standardization of medical oxygen chambers.

The left atrium not only regulates left ventricular filling volume but also serves as a key biomarker for cardiovascular diseases. After the occurrence of left ventricular diastolic dysfunction, left atrial function undergoes compensatory changes before alterations in left atrial volume and left ventricular function. Left atrial strain can reflect changes in left atrial function throughout the cardiac cycle. Imaging is the only method for evaluating left atrial strain. Therefore, this article reviewed traditional and novel imaging techniques for assessing left atrial strain, including echocardiography, cardiac magnetic resonance, and cardiac computed tomography. It also introduced the application of radiomics in this field, and analyzed the reproducibility of these imaging techniques as well as the feasibility and limitations of their current clinical applications, aiming to provide appropriate imaging techniques for personalized assessment of left atrial strain in patients, offer reliable diagnostic and therapeutic evidence for clinicians, and facilitate the diagnosis, prognostic evaluation, and formulation of treatment plans for cardiovascular diseases.

The management of medical consumables is a key task in deepening the reform of the medical and health system, and the governance of high-value medical consumables in particular is crucial for standardizing the medical market and safeguarding patients’ rights and interests. Currently, research on the full-life-cycle management of medical consumables has expanded from single-institution logistics management to a multi-dimensional approach, yet significant gaps remain. Specifically, it is difficult to align the intentions of stakeholders, and the interest conflicts among the four parties-government, medical institutions, enterprises, and patients-in areas such as policy implementation, cost control, and quality assurance have not been systematically resolved. This leads to prominent issues including an unclear market operation mechanism and information asymmetry, which hinder the rational allocation of medical resources. Against this backdrop, this paper, from the perspective of stakeholders, identified the four core stakeholder groups, sorted out their research status and interest demands across all stages of the medical consumables’ full life cycle including R&D, production, circulation, usage, and supervision & evaluation, and analyzed the points of interest conflict. Finally, targeted optimization suggestions were proposed, aiming to provide a scientific basis for the formulation of relevant policies, thereby promoting the fair and stable operation of the medical market, reducing interest conflicts, protecting patients’ rights and interests, and advancing the healthy and sustainable development of the medical industry.

Medical auxiliary diagnosis has become a critical research direction in deep learning. By leveraging multi-layer neural networks, deep learning enables the realization of non-linear mapping between inputs and specific outputs through extracting deep features from image data, particularly in capturing lesion features and revealing details that are inaccessible to the naked eye. The applications of deep learning in the field of gastric cancer have covered multiple key aspects, providing multi-dimensional technical support for the precise diagnosis and treatment of gastric cancer. This review mainly focused on the applications of deep learning networks in the precise automatic classification of gastric cancer, the relationship between deep features and genes, and prognostic evaluation, with an emphasis on summarizing the basic ideas and improvements of the models. It also summarized the existing problems and challenges in this field and prospects future research directions, aiming to provide new perspectives and methods for computer-aided diagnosis and offer diverse entry points for the accurate diagnosis and personalized treatment of patients.

Telemedicine, leveraging its advantages of breaking time and space barriers and enabling bidirectional data feedback and sharing, has emerged as a novel medical model for medical communication between patients with diabetic foot ulcer (DFU) and healthcare providers. With the rapid advancement of wearable devices, big data, artificial intelligence, and other technologies, a new wave of telemedicine development has been triggered, which has demonstrated unique strengths in the monitoring and management of DFU. However, due to technical limitations, risks of information and data leakage, and inadequate supporting policies, there remains a gap between practical application and clinical promotion of telemedicine. This paper elaborated on the concept of telemedicine, summarized its application forms in the monitoring and management of patients with DFU, and explored the acceptance of telemedicine among these patients and their relevant stakeholders. It also analyzed the shortcomings and challenges in its promotion, and put forward prospects and considerations, aiming to provide references for clinical research on telemedicine in patients with DFU, thereby alleviating the enormous pressure on resource allocation and operation management of the healthcare system.

The applications of new-generation information technologies such as big data, artificial intelligence, the Internet of Things, block chain, and virtual reality in medical equipment management are continuously innovating, which is expected to promote the transformation of medical equipment quality control models and improve the quality management level of medical institutions. Based on the current status and challenges of medical equipment quality control, this paper summarized the applications of new-generation information technologies in key links of medical equipment quality control, including data collection, intelligent analysis, information traceability, and simulation optimization. It also analyzed relevant cases and prospects for future trends, aiming to facilitate the reengineering and reform of quality management processes in medical institutions and drive medical equipment quality management towards intellectualization, networking, and refinement. Medical institutions should strengthen collaboration, transform innovative achievements, establish a full-process quality and safety traceability mechanism and a co-governance system, and enhance their safety assurance capabilities.

As a key energy device in modern minimally invasive surgery, the radiofrequency-ultrasonic scalpel, featuring the simultaneous delivery of both radiofrequency and ultrasonic energies, boasts significant advantages such as minimal trauma and rapid recovery, thus being widely used in clinical practice. Compared with traditional instruments, it performs better in reducing operation time, intraoperative bleeding, and postoperative complications. Currently, this device has played an important role in multiple fields including general surgery, urology, gynecology, and neurosurgery. Although domestic research and development have achieved remarkable outcomes, with domestic substitution realized in some fields, issues such as reliance on imported core materials and limited expansion in the international market remain. This review mainly summarized the structural principles, application scenarios, and innovation directions of the radiofrequency-ultrasonic scalpel, analyzed its prospects, and put forward improvement suggestions, aiming to provide references for its subsequent development.

Currently, the incidence of breast cancer remains high. As a crucial procedure following mastectomy, breast reconstruction is of great significance to patients’ physical and mental health. However, traditional surgeries are affected by factors such as surgeons’ experience and individual differences, leading to problems like insufficient precision and uncontrollable risks. The application of AI has addressed complex challenges in breast reconstruction for breast cancer patients, including difficulties in meeting high surgical precision requirements, inadequate risk prediction and management, and deficiencies in remote rehabilitation guidance. This has thereby improved patients’ quality of life and satisfaction, reduced the burden on medical staff, and promoted the advancement of clinical medical technology. This article provided an overview of AI, summarized its current applications in breast reconstruction for breast cancer patients, and analyzed existing challenges and limitations, aiming to offer new insights and references for the future clinical application of AI in breast reconstruction.

Knee Osteoarthritis (KOA) is an irreversible degenerative disease. In the early stage, it mainly involves the cartilage, and the pathological changes of cartilage occur much earlier than its morphological changes, making conventional magnetic resonance imaging of limited value. Magnetic resonance (MR) quantitative analysis enables quantitative assessment of early articular cartilage damage based on changes in different components of the cartilage. This paper systematically reviewed the current application status of various MR quantitative imaging methods in the early diagnosis of KOA, and analyzed the advantages and limitations of different MR quantitative methods in terms of sensitivity, specificity, imaging time, and hardware requirements. It is pointed out that improvements should be made in shortening data acquisition time, improving image quality, and reducing radiofrequency energy accumulation. Additionally, the development direction of integrating this field with artificial intelligence was prospected, aiming to provide references for early intervention and efficacy monitoring of KOA.

Pneumonia is a lung infection caused by microorganisms such as bacteria and viruses. In recent years, applying deep learning techniques to classify pneumonia in chest X-ray images has become one of the important research directions in the field of medical image processing and analysis. Compared to traditional classification methods, deep learning techniques, especially convolutional neural networks (CNN), can better extract useful features from pneumonia images and suppress irrelevant information, detecting lung diseases such as pneumonia earlier and providing patients with earlier treatment, thereby improvingtreatment efficacy. A review and summary of CNN methods for classifying pneumonia in chest X-ray images was conducted. Firstly, two datasets widely used for pneumonia image classification and their evaluation criteria were introduced in detail. Then, the existing problems in manual classification of pneumonia images were pointed out. Subsequently, the research progress of CNN based on these two datasets was highlighted. Finally, the current challenges and future prospects for classifying pneumonia in chest X-ray images were discussed.

As the clinical treatment of chronic pain and spasmodic diseases has gained increasing attention, implantable intrathecal drug delivery systems (IDDS) have become one of the key approaches for managing such conditions. In China, IDDS have been gradually applied in clinical practice for treating various refractory pain and spastic disorders, but the systems remain undomesticated, with imported products carrying high prices. This paper introduced the general structure, performance, and therapeutic scope of IDDS, and summarized the research progress in both software and hardware aspects at home and abroad. By reviewing the current clinical application status of IDDS domestically and internationally, and comparing/analyzing market demands with the monopolistic pricing of imported products, this study clarified the value of further domestication research and discussed the significance of domestic development as well as the clinical application prospects in China.

Pre-operative anxiety is an emotional disorder before an operation, and its occurrence is related to surgical stress and pressure. Pre-operative stress-related anxiety increases anaesthetic doses during surgery and is even associated with a decrease in long-term quality of life and survival after surgery. Traditional preoperative education may suffer from problems such as monotony, formality, and abstract content. The emergence and development of virtual reality (VR) technology offer a new avenue for non-drug intervention in anxiety. Currently, a number of studies have been reported on VR-based intervention for surgical anxiety, particularly in children and for certain surgical procedures. This paper aimed to analyse and evaluate previous clinical studies on VR-based preoperative anxiety intervention, clarify its research direction, and provide references for clinical research in terms of expanding application scenarios, enriching intervention contents, and improving the evaluation system.

As a global public health priority, Parkinson’s disease (PD) patient care has garnered significant public attention. Current clinical management of PD patients faces bottlenecks such as traditional diagnosis relying on subjective experience, lack of personalized home rehabilitation programs, and limited monitoring methods for non-motor symptoms. Although mobile health applications (mHealth APPs) have demonstrated advantages in convenience and cost-effectiveness for chronic disease management, domestic applications are still confined to basic nursing interventions via WeChat, with notable gaps in areas such as auxiliary diagnosis, precise assessment, and integrated traditional Chinese and Western medicine interventions. This paper introduced the application of mHealth APPs in PD patient health management, covering four aspects: assisting disease diagnosis, health assessment, health monitoring, and home rehabilitation training. It proposed that mHealth APPs can facilitate early PD detection, improve patients’ quality of life, and enhance the quality of healthcare services, aiming to provide references for domestic research on mobile health management of PD patients.

Impacted mandibular third molars (IMM3) are common and frequently encountered diseases in oral and maxillofacial surgery, and imaging examinations play a crucial role in their diagnosis and treatment. In recent years, significant breakthroughs have been made in the application of artificial intelligence (AI) in stomatology. Specifically, AI based on deep learning (DL) can accurately identify and segment IMM3 and surrounding anatomical structures. This not only improves the efficiency of correct interpretation of oral images but also provides clinicians with a more intuitive and precise basis for diagnosis and treatment. Currently, DL-based AI technology has become a research hotspot in clinical stomatology. Therefore, this article reviewed the recent application progress, existing challenges, and future development directions of DL-based AI in the diagnosis and treatment of IMM3, aiming to provide intelligent theoretical support and practical guidance for IMM3 management.

The ultra-high-definition surgical microscope plays a critical role in modern surgical procedures. Compared to traditional microscopes, the ultra-high-definition surgical microscope provides surgeons with clearer and more detailed views due to its high-resolution imaging technology. This assists in accurately identifying tissue structures and enhances both surgical safety and success rates. This article focused on the technological advancements of ultra-high-definition surgical microscopes, including core technologies such as optical systems, lighting systems, visualization systems, and multi-modal imaging. It also analyzed their applications in various types of surgeries and explores the integration of intelligent assistive technologies such as augmented reality and artificial intelligence. Additionally, the article summarized the challenges faced by current technologies, such as the large size of equipment, lighting control issues, and data transmission limitations, aiming to provide reference for researchers and clinicians, promoting innovative applications of ultra-high-definition surgical microscopes in the medical field.

Positron emission tomography (PET) imaging is of great significance in clinical applications. The automated segmentation of its images is crucial for disease diagnosis and treatment. In recent years, remarkable progress has been made in the research of automated PET image segmentation, especially in the application of deep learning methods. PET image segmentation can accurately isolate and label regions of interest, such as tumors, organs, and other structures, which is of great importance for disease diagnosis, treatment planning, and research. This paper provided an overview of the latest research achievements in PET image segmentation based on neural network systems, analyzed the existing limitations and future development trends, aiming to facilitate the optimization of PET image segmentation algorithms and provide new ideas and references for medical research.

Nitric oxide (NO), as a signaling molecule with critical biological functions, is involved in various physiological processes such as vasodilation, neurotransmission, and immune regulation. As a key device for NO delivery, NO therapeutic devices have demonstrated unique advantages in the treatment of respiratory diseases, cardiovascular diseases, and tumors by precisely controlling gas concentration and release patterns. However, challenges in technical design and clinical application arise from NO’s extremely short half-life, strong concentration dependence, and potential toxicity. This paper reviewed the development history of NO therapeutic devices, covering research progress at home and abroad, technical structures, classification methods, and explored current dilemmas and future development trends, aiming to provide a reference for in-depth research in this field and facilitate broader applications of NO therapeutic devices in medicine.

With the increasing demand for hospital informatization management, problems such as low efficiency of manual entry, difficult traceability, and data silos in traditional medical consumables management have become more and more prominent. The unique device identification (UDI) of medical devices, as a core technology for achieving “one item, one code” whole-life cycle management, has become the key to breaking through the bottleneck of refined hospital management. This paper reviewed the relevant regulations and application status of UDI at home and abroad, as well as the applications of UDI in the refined qualification management, order management, inventory management, usage management, and traceability management of medical consumables in hospitals, aiming to provide references for the informatization and refined management of hospitals, as well as the whole-life cycle management and development of medical consumables.

The extracellular matrix (ECM), a pericellular network composed of various proteins, polysaccharides, and other components, is essential for maintaining the function of normal tissues and organs. Abnormal deposition and modification of ECM may lead to pathological conditions, such as the occurrence and progression of fibrosis and tumors. As a novel non-invasive parameter for quantifying ECM, the extracellular volume fraction (ECV) can reflect the microenvironment of tumors and evaluate the degree of tissue fibrosis. It holds significant clinical value for clinicians in assessing the severity of abdominal organ fibrosis, predicting the differentiation of abdominal tumor tissues, differentiating tumor types, and evaluating treatment efficacy. This article reviewed the measurement of CT-ECV and its applications in abdominal diseases, aiming to provide new technologies and perspectives for the diagnosis and treatment of abdominal diseases and improve the level of clinical management.

Traditional Chinese medicine rehabilitation is a new specialty that combines China’s national conditions and adapts to the needs of rehabilitation development. With the emergence of forward-looking technologies such as artificial intelligence, machine learning, and deep learning, the cross-integration of multiple disciplines and intelligent applications have also achieved good results in facilitating the development of traditional Chinese medicine rehabilitation. To promote the modernization of traditional Chinese medicine, fully integrate traditional Chinese medicine rehabilitation with artificial intelligence, and enable the field of traditional Chinese medicine to reach a new height based on modern science and technology, this paper discussed the combination of traditional Chinese medicine rehabilitation and artificial intelligence technology from the two perspectives of traditional Chinese medicine rehabilitation assessment and treatment, aiming to provide a frontier direction and theoretical support for the further development of intelligent traditional Chinese medicine rehabilitation.

Spinal cord injury is a serious complication of spinal fracture. It is a trauma-induced neurological disorder, which can cause motor, sensory, sphincter and autonomic nerve dysfunction below the injury site, leading to severe disability. At present, although the technology of spinal internal fixation is very mature, the treatment methods for the recovery of spinal cord injury are limited and the efficacy is not exact. Research has found that cell transplantation can, to a certain extent, increase the regenerative potential of the nerve and promote the recovery of the nervous system, and the combination of transplanted cells loaded on a biological scaffold and transplanted together at the injury site can significantly increase the survival rate of the cells after transplantation and promote the recovery of spinal cord injury. However, there are differences in the degree of repair of spinal cord injury between different combination treatments, and the selection of an appropriate combination treatment is of great value in promoting the recovery of spinal cord function after injury. This article provided a review of the role of biological scaffold and cell transplantation in the repair of spinal cord injury, and summarized the current research progress of the combination of bioengineering technology and cell transplantation, aiming to provide literature information for clinical research and further basic research, and offering new ideas for the development and application of these approaches in the repair of spinal cord injury.

Mass spectrometry technology has gradually assumed a significant position in the field of clinical testing. This is due to its characteristics such as high sensitivity, high specificity, good reproducibility, high throughput and high efficiency. In particular, liquid chromatography-tandem mass spectrometry (LC-MS/MS) technology, which embodies these qualities, has been widely applied in clinical testing. This paper commenced with a concise elucidation of the principal structural components and technical characteristics of LC-MS/MS. Subsequently, it comprehensively elaborated on four key aspects, namely, its application scenarios, regulatory aspects, development trends, and innovation frontiers. Moreover, it meticulously identified the opportunities and challenges encountered in the advancement of LC-MS/MS technology. Finally, the future development trajectory of mass spectrometry was forecasted. It was expounded that with the in-depth and extensive progression of research, the salutary development of mass spectrometry could be further propelled.

With the continuous development of network informatization, digital therapeutics has been deeply integrated into the medical field. As a means of digital health management, digital therapeutics has been widely used in the management of various diseases in European and American countries. In recent years, corresponding clinical studies have also been carried out in China, and digital therapeutics is widely applied in the diagnosis and treatment of lumbar disc herniation. This article reviewed the origin of the concept and current situation of digital therapeutics, as well as its application effects and value in the treatment of lumbar disc herniation, and pointed out the current challenges, aiming to provide a reference for the development of digital therapeutics - related research in China.

Coronary revascularization is an effective method for the treatment of coronary heart disease. Fractional flow reserve (FFR) is the gold standard for the invasive assessment of ischemia caused by coronary artery lesions. However, it requires additional guidewires and vasodilation, which limits its widespread clinical application. In recent years, new technologies such as FFR derived from coronary CT angiography (CT-FFR) and quantitative flow fraction (QFR) have developed rapidly. These technologies can further evaluate the situation of myocardial ischemia and the degree of coronary artery stenosis while avoiding the use of additional guidewires and vasodilation. This paper reviewed the principles, application values, and limitations of FFR, CT-FFR, and QFR from multiple aspects, aiming to provide a reference for clinicians to select reasonable technologies in the diagnosis and treatment of coronary heart disease.

In recent years, the application of ultrasonic elastography technology for detecting the mechanical properties of tissues and organs has stepped into a stage of rapid clinical popularization. Users should first understand its technical advantages and master its operation methods skillfully. At the same time, they should also be clear about its limitations, so as to achieve standardized and correct clinical use and maximize its value. In this paper, the classification of ultrasonic elastography was briefly described first. Then, the wide application value of ultrasonic elastography in the diagnosis of four typical diseases, namely breast tumors, liver fibrosis, prostate cancer, and thyroid nodules, was emphasized. The characteristics of traditional clinical diagnosis methods, the technical advantages and limitations of different ultrasonic elastography methods, and ultrasonic elastography methods’ important role in promoting the development of medical imaging were discussed.

Parkinson’s disease (PD) is a common neurodegenerative disease, and its early diagnosis and identification are challenging. With the rapid development of machine learning technology, its application in PD diagnosis has received increasing attention. Compared with traditional diagnostic methods, machine learning technology shows the advantages of multimodality, non-invasive, less variation and personalization in PD diagnosis, but at this stage, the non-specificity of early PD symptoms, the high quality of the PD labelled data and privacy issues still limit the application of machine learning techniques to some extent. This paper reviewed the research progress of machine learning technology in PD diagnosis in recent years. Future research needs to further improve the data quality, construct models with more generalisation ability, and explore biological explanations, etc., so that the close integration of machine learning technology and clinical medicine can provide more accurate diagnosis and better treatment options for PD patients.

Wearable health devices, as an emerging electronic device, boast functions such as monitoring, recording, and providing real-time feedback, thereby offering improved support for the recovery of cardiac function in heart rehabilitation patients. This paper reviewed the applications of wearable health devices, including heart rate monitors, electrocardiogram monitors, health smartwatches, pedometers, and accelerometers in cardiac rehabilitation. Through a comprehensive analysis of the advantages and limitations of each type of device, it elucidated their practical applications in cardiac rehabilitation. Additionally, this paper proposed future directions for development, aiming to enhance healthcare professionals’ understanding of wearable health devices to provide patients with more personalized and effective cardiac rehabilitation solutions and foster the widespread application of wearable health devices in clinical practice.

With the improvement of public awareness of mental health and income levels, the demand for mental health services for the elderly is gradually expanding. The elderly population needs to alleviate various psychological disorders or negative emotions. Elderly people have a demand for mental health service teams or platforms, and professional medical support for moderate to severe psychological or mental illnesses. There is huge market potential for products that meet the psychological health needs of the elderly. This paper reviewed the current research status of intelligent diagnosis and treatment technology for the mental health of the elderly, including the current status and harm of mental health issues in the elderly, and proposed the medical needs of the elderly for mental health. Finally, the problems existed in intelligent diagnosis and treatment technology were elaborated, and the development trend was discussed, aiming to provide reference for alleviating various problems faced by elderly patients with mental health disorders.

Pelvic organ prolapse (POP) is a disease caused by damage to the pelvic floor support structures, and its causes are complex and varied, seriously affecting the physical and mental health of patients. The pelvic organ prolapse quantification (POP-Q) scale is widely used both at home and abroad to specifically characterize the site of prolapse, which provides detailed descriptive information but does not visually assess the functional status of the pelvis. Ultrasonography has been widely used in clinical practice due to its advantages of easy operation, real-time dynamic imaging, low cost, and non-invasiveness, which can make up for the shortcomings of the POP-Q scoring system and detect mild POP without clinical symptoms in the early stages of the disease. This paper reviewed the application of ultrasound tomography and shear wave elastography in women with postpartum POP and the treatment of patients with pelvic floor ultrasound-guided POP, aiming to provide clinical diagnostic tests, significantly improve the diagnostic rate of anal retinaculum injuries, and help to provide effective preventive measures for pregnant women.

With the continuous improvement of the management system in modern public hospitals, the importance of precise management of medical equipment in modern hospital management has increasingly grown. At present, most hospitals have carried out economic benefit evaluations for large-scale medical equipment, but there is still a lack of economic benefit evaluation and performance appraisal for all medical equipment, and a lack of an economic benefit evaluation system for medical equipment. This paper reviewed the construction experience of economic benefit evaluation models for medical equipment procurement in public hospitals at home and abroad, explored the establishment of an economic benefit evaluation model for hospital medical equipment that conforms to the current situation in China, aiming to provide enlightenment and reference for the continuous improvement of pre-purchase benefit evaluation of hospital medical equipment in China.

Chronic obstructive pulmonary disease (COPD) is a common chronic disease characterized by pulmonary ventilation dysfunction. More and more scholars believe that COPD is a systemic disease, in which sarcopenia causes skeletal muscle dysfunction and leads to decreased exercise tolerance, which is associated with poor prognosis and increased hospitalization and mortality. Chest muscle can indirectly reflect the whole body muscle level, and can also participate in respiratory movement as respiratory muscle, which is closely related to the respiratory function of COPD patients. Chest CT is the main examination method for lung diseases, which can simultaneously assess lung and extrapulmonary tissues. This paper reviewed the research progress of CT in evaluated chest muscle attenuation in COPD patients, aiming to improve the understanding of systemic changes in COPD patients and provide imaging evidence for it.

Radiotherapy is one of the important comprehensive treatments for tumors. Currently, it has developed into the precision radiotherapy stage, but there are problems such as uneven distribution of medical resources, differences in level among doctors, long time spent in delineating the radiotherapy contour, human factors influencing planning design and image-guided registration, human factors influencing planning design and image-guided registration. In order to adapt to the development of the times, it is necessary to explore and try to develop intelligent radiotherapy. The increasing development of artificial intelligence technology provides a new method for realizing intelligent radiotherapy. Deep learning (DL) technology has a significant effect on graphics processing and output, which has natural advantages in the graphics processing requirements and radiotherapy process in the field of radiotherapy. Intelligent radiotherapy can promote the development of intelligent radiotherapy towards precision and personalized directions. This study reviewed the application status and development trend of DL in the process of intelligent radiotherapy for tumors.

Predictive maintenance (PdM) represents an integrated strategy for maintenance that encompasses machine status monitoring, fault diagnosis, fault prediction, and maintenance scheduling. In recent years, PdM has been progressively applied to the management of medical device and effectively addresses a series of challenges that cannot be solved by traditional maintenance strategies. To help healthcare practitioners deepen their understanding of this emerging concept, this paper introduced its background from three aspects, such as the comparison between traditional maintenance strategy and PdM, the goal of PdM and the scheme of constructing PdM; the application value of PdM in the field of medical device, the system architecture of PdM, and the practical application of PdM combined with machine learning in the management of medical device, which comprehensively demonstrated the highlights of PdM in the management of medical device; the limitations of PdM in the field of medical device management were discussed, and the corresponding solution strategies were proposed, and future development of PdM in the field of medical device management was envisioned. The main challenges currently faced were summarized to provide a reference for healthcare practitioners on this emerging field.

With the continuous development and advancement of oral digital technology, dynamic navigation and surgical robots have shown great potentiality in the field of oral digital implantation. Compared with traditional freehand implantation and static guideassisted implant surgery, dynamic navigation and implant robots can provide real-time feedback on the surgical process, so that the operator can adjust the surgical plan in a timely manner to avoid damage to important anatomical structures. Dynamic navigation and implant robotics can also improve the accuracy of implant placement, resulting in favorable long-term outcomes. At present, these two technologies are increasingly widely used by implantologists in clinical practice. This paper reviewed the clinical research and advantages and disadvantages related to dynamic navigation and surgical robots for oral implantation, in order to further understand the progress of clinical research on oral digital implantation.

A multimodal nanodiagnostic system designed for the treatment of Alzheimer’s disease (AD) combines imagingguided therapy (IGT), targeted drug delivery, smart release systems, and combination therapies. It demonstrates the potential of nanotechnology to improve therapeutic precision, efficiency, and personalization of treatment. IGT utilizes key imaging technologies to ensure therapeutic precision and optimal drug delivery pathways. Targeted drug delivery systems deliver therapeutic drugs directly to the drug area of the disease by designing specific nanocarriers, greatly increasing the local concentration of therapeutic drugs and reducing systemic side effects. The smart release system automatically adjusts the release of the drug according to the progress of the disease, maximizing the efficiency of drug use. Combination therapies integrate all of the above monotherapies to provide comprehensive treatment for AD in order to achieve a comprehensive effect. This mutimodal nanodiagnostic technology not only enhances the efficiency and precision of treatment, but also provides a new strategy for the treatment of AD through its innovative early detection and personalized treatment plan. In this paper, multimodal nano diagnosis and treatment theory was integrated into AD management, and predictive treatment plan was developed according to the disease assessment of AD, so as to reduce the harm to patients.

3D printing is a technique for creating three-dimensional models and uses computer-aided design software to convert digital models into physical objects. 3D printing technology is developing rapidly, especially in the field of health care, 3D printing technology has also been continuously developed. This paper discussed the clinical application of 3D printing technology in the field of orthopedics to treat scoliosis, briefly introduced the advantages and disadvantages of 3D printing technology, and put forward its research direction and prospect, aiming to provide the following references for 3D printing technology for scoliosis.

Nerve electrical stimulation techniques, including vagus nerve stimulation, responsive neurostimulation, and deep brain stimulation, are effective treatment methods for patients with drug-resistant epilepsy and those unsuitable for traditional epilepsy surgery. With the continuous progress and improvement of nerve electrical stimulation techniques in recent years, their application in treating drug-resistant epilepsy has gradually expanded. In clinical practice, in addition to considering the individual indications for patients, potential ethical issues during the application process need to be considered, such as patient autonomy, risk-benefit ratio, patient unrealistic expectations and privacy security, to fully protect patients’ rights and promote the standardization and safety of the clinical application of new medical technologies. This article reviewed the medical ethical issues that might arise or potentially arise during the clinical application of nerve electrical stimulation therapy technology, in order to further standardize its research and application in clinical practice.