Intelligent medical devices are flourishing with the deep integration of modern information and artificial intelligence technologies into healthcare.Testing is an important means of performance evaluation and quality control for intelligent medical devices.Compared with traditional medical devices,the testing methods and technologies of intelligent medical devices are still immature,and need active research to promote the progress in this area.Intelligent medical devices are classified according to their characteristics as artificial intelligence medical devices in the form of software and medical robots based on a general discussion of their development.The medical-device Internet of Things(IoT)system has also been included due to its close relation to the construction of smart hospitals.For each type of intelligent medical device,testing indexes and testing plans are discussed.It is suggested that specific test rules should be further developed for various specific devices.Besides,the evaluation method of complex intelligent systems should be introduced and real-world data should be used for evaluation.This paper aims to accelerate the development of intelligent medical device testing,laying the foundation for quality control and performance evaluation of intelligent medical devices.

This review article aims to explore the major challenges that the healthcare system is currently facing and propose a new paradigm shift that harnesses the potential of wearable devices and novel theoretical frameworks on health and disease. Lifestyle-induced diseases currently account for a significant portion of all healthcare spending, with this proportion projected to increase with population aging. Wearable devices have emerged as a key technology for implementing large-scale healthcare systems focused on disease prevention and management. Advancements in miniaturized sensors, system integration, the Internet of Things, artificial intelligence, 5G, and other technologies have enabled wearable devices to perform high-quality measurements comparable to medical devices. Through various physical, chemical, and biological sensors, wearable devices can continuously monitor physiological status information in a non-invasive or minimally invasive way, including electrocardiography, electroencephalography, respiration, blood oxygen, blood pressure, blood glucose, activity, and more. Furthermore, by combining concepts and methods from complex systems and nonlinear dynamics, we developed a novel theory of continuous dynamic physiological signal analysis-dynamical complexity. The results of dynamic signal analyses can provide crucial information for disease prevention, diagnosis, treatment, and management. Wearable devices can also serve as an important bridge connecting doctors and patients by tracking, storing, and sharing patient data with medical institutions, enabling remote or real-time health assessments of patients, and providing a basis for precision medicine and personalized treatment. Wearable devices have a promising future in the healthcare field and will be an important driving force for the transformation of the healthcare system, while also improving the health experience for individuals.
Humans ; Artificial Intelligence ; Wearable Electronic Devices ; Monitoring, Physiologic/methods*

Wearable monitoring, which has the advantages of continuous monitoring for a long time with low physiological and psychological load, represents a future development direction of monitoring technology. Based on wearable physiological monitoring technology, combined with Internet of Things (IoT) and artificial intelligence technology, this paper has developed an intelligent monitoring system, including wearable hardware, ward Internet of Things platform, continuous physiological data analysis algorithm and software. We explored the clinical value of continuous physiological data using this system through a lot of clinical practices. And four value points were given, namely, real-time monitoring, disease assessment, prediction and early warning, and rehabilitation training. Depending on the real clinical environment, we explored the mode of applying wearable technology in general ward monitoring, cardiopulmonary rehabilitation, and integrated monitoring inside and outside the hospital. The research results show that this monitoring system can be effectively used for monitoring of patients in hospital, evaluation and training of patients' cardiopulmonary function, and management of patients outside hospital.
Humans ; Artificial Intelligence ; Internet of Things ; Wearable Electronic Devices ; Monitoring, Physiologic/methods* ; Electrocardiography ; Internet

Patients with acute heart failure (AHF) often experience dyspnea, and monitoring and quantifying their breathing patterns can provide reference information for disease and prognosis assessment. In this study, 39 AHF patients and 24 healthy subjects were included. Nighttime chest-abdominal respiratory signals were collected using wearable devices, and the differences in nocturnal breathing patterns between the two groups were quantitatively analyzed. Compared with the healthy group, the AHF group showed a higher mean breathing rate (BR_mean) [(21.03 ± 3.84) beat/min vs. (15.95 ± 3.08) beat/min, P < 0.001], and larger R_RSBI_cv [70.96% (54.34%-104.28)% vs. 58.48% (45.34%-65.95)%, P = 0.005], greater AB_ratio_cv [(22.52 ± 7.14)% vs. (17.10 ± 6.83)%, P = 0.004], and smaller SampEn (0.67 ± 0.37 vs. 1.01 ± 0.29, P < 0.001). Additionally, the mean inspiratory time (TI_mean) and expiration time (TE_mean) were shorter, TI_cv and TE_cv were greater. Furthermore, the LBI_cv was greater, while SD1 and SD2 on the Poincare plot were larger in the AHF group, all of which showed statistically significant differences. Logistic regression calibration revealed that the TI_mean reduction was a risk factor for AHF. The BR_ mean demonstrated the strongest ability to distinguish between the two groups, with an area under the curve (AUC) of 0.846. Parameters such as breathing period, amplitude, coordination, and nonlinear parameters effectively quantify abnormal breathing patterns in AHF patients. Specifically, the reduction in TI_mean serves as a risk factor for AHF, while the BR_mean distinguishes between the two groups. These findings have the potential to provide new information for the assessment of AHF patients.
Humans ; Heart Failure/diagnosis* ; Prognosis ; Respiration ; Wearable Electronic Devices ; Acute Disease

In recent years, wearable devices have seen a booming development, and the integration of wearable devices with clinical settings is an important direction in the development of wearable devices. The purpose of this study is to establish a prediction model for postoperative pulmonary complications (PPCs) by continuously monitoring respiratory physiological parameters of cardiac valve surgery patients during the preoperative 6-Minute Walk Test (6MWT) with a wearable device. By enrolling 53 patients with cardiac valve diseases in the Department of Cardiovascular Surgery, West China Hospital, Sichuan University, the grouping was based on the presence or absence of PPCs in the postoperative period. The 6MWT continuous respiratory physiological parameters collected by the SensEcho wearable device were analyzed, and the group differences in respiratory parameters and oxygen saturation parameters were calculated, and a prediction model was constructed. The results showed that continuous monitoring of respiratory physiological parameters in 6MWT using a wearable device had a better predictive trend for PPCs in cardiac valve surgery patients, providing a novel reference model for integrating wearable devices with the clinic.
Humans ; Lung ; Walking/physiology* ; Walk Test ; Heart Valves/surgery* ; Postoperative Period ; Postoperative Complications/etiology*

OBJECTIVE: To review the research progress in biotherapy of rotator cuff injury in recent years, in order to provide help for clinical decision-making of rotator cuff injury treatment. METHODS: The literature related to biotherapy of rotator cuff injury at home and abroad in recent years was widely reviewed, and the mechanism and efficacy of biotherapy for rotator cuff injury were summarized from the aspects of platelet-rich plasma (PRP), growth factors, stem cells, and exosomes. RESULTS: In order to relieve patients' pain, improve upper limb function, and improve quality of life, the treatment of rotator cuff injury experienced an important change from conservative treatment to open surgery to arthroscopic rotator cuff repair. Arthroscopic rotator cuff repair plus a variety of biotherapy methods have become the mainstream of clinical treatment. All kinds of biotherapy methods have ideal mid- and long-term effectiveness in the repair of rotator cuff injury. The biotherapy method to promote the healing of rotator cuff injury is controversial and needs to be further studied. CONCLUSION All kinds of biotherapy methods show a good effect on the repair of rotator cuff injury. It will be an important research direction to further develop new biotherapy technology and verify its effectiveness.
Humans ; Rotator Cuff Injuries/therapy* ; Quality of Life ; Arthroplasty ; Exosomes ; Neurosurgical Procedures

Internet of Things (IoT) technology plays an important role in smart healthcare. This paper discusses IoT solution for emergency medical devices in hospitals. Based on the cloud-edge-device architecture, different medical devices were connected; Streaming data were parsed, distributed, and computed at the edge nodes; Data were stored, analyzed and visualized in the cloud nodes. The IoT system has been working steadily for nearly 20 months since it run in the emergency department in January 2021. Through preliminary analysis with collected data, IoT performance testing and development of early warning model, the feasibility and reliability of the in-hospital emergency medical devices IoT was verified, which can collect data for a long time on a large scale and support the development and deployment of machine learning models. The paper ends with an outlook on medical device data exchange and wireless transmission in the IoT of emergency medical devices, the connection of emergency equipment inside and outside the hospital, and the next step of analyzing IoT data to develop emergency intelligent IoT applications.
Internet of Things ; Reproducibility of Results ; Internet ; Machine Learning ; Technology

Objective:To examine the influence of acute hypoxemia on central venous pressure (CVP) and diastolic blood pressure (DBP) in critical patients assisted by mechanical ventilation.Methods:We retrospectively analyzed the clinical data of critical patients assisted by mechanical ventilation in Medical Information Mart for Intensive Care Ⅲ (MIMIC-Ⅲ) database. Influence of acute hypoxemia on CVP and diastolic blood pressure (DBP) were evaluated. Hypoxemia was defined according to oxygenation index (OI) (OI≤100 as severe, 100

Sleep apnea causes cardiac arrest, sleep rhythm disorders, nocturnal hypoxia and abnormal blood pressure fluctuations in patients, which eventually lead to nocturnal target organ damage in hypertensive patients. The incidence of obstructive sleep apnea hypopnea syndrome (OSAHS) is extremely high, which seriously affects the physical and mental health of patients. This study attempts to extract features associated with OSAHS from 24-hour ambulatory blood pressure data and identify OSAHS by machine learning models for the differential diagnosis of this disease. The study data were obtained from ambulatory blood pressure examination data of 339 patients collected in outpatient clinics of the Chinese PLA General Hospital from December 2018 to December 2019, including 115 patients with OSAHS diagnosed by polysomnography (PSG) and 224 patients with non-OSAHS. Based on the characteristics of clinical changes of blood pressure in OSAHS patients, feature extraction rules were defined and algorithms were developed to extract features, while logistic regression and lightGBM models were then used to classify and predict the disease. The results showed that the identification accuracy of the lightGBM model trained in this study was 80.0%, precision was 82.9%, recall was 72.5%, and the area under the working characteristic curve (AUC) of the subjects was 0.906. The defined ambulatory blood pressure features could be effectively used for identifying OSAHS. This study provides a new idea and method for OSAHS screening.
Blood Pressure ; Blood Pressure Monitoring, Ambulatory ; Humans ; Hypertension/complications* ; Polysomnography ; Sleep Apnea, Obstructive/diagnosis*

Purpose: Osteosarcoma (OS) universally exhibits heterogeneity and cisplatin (CDDP) resistance. Although the Wee1/CDC2 and nuclear factor кB (NF-κB) pathways were reported to show abnormal activation in some tumor cells with CDDP resistance, whether there is any concrete connection is currently unclear. We explored it in human OS cells. Materials and Methods: Multiple OS cell lines were exposed to a Wee1 inhibitor (AZD1775) and CDDP to assess the half-maximal inhibitory concentration values. Western blot, coimmunoprecipitation, confocal immunofluorescence, cell cycle, and Cell Counting Kit-8assays were performed to explore the connection between the Wee1/CDC2 and NF-κB pathways and their subsequent physiological contribution to CDDP resistance. Finally, CDDP-resistant PDX-OS xenograft models were established to confirm that AZD1775 restores the antitumor effects of CDDP. Results: A sensitivity hierarchy of OS cells to CDDP and AZD1775 exists. In the highly CDDP-tolerant cell lines, Wee1 and RelA were physically crosslinked, which resulted in increased abundance of phosphorylated CDC2 (Y15) and RelA (S536) and consequent modulation of cell cycle progression, survival, and proliferation. Wee1 inhibition restored the effects of CDDP on these processes in CDDP-resistant OS cells. In addition, animal experiments with CDDP-resistant PDX-OS cells showed that AZD1775 combined with CDDP not only restored CDDP efficacy but also amplified AZD1775 in inhibiting tumor growth and prolonged the median survival of the mice. Conclusion Simultaneous enrichment of molecules in the Wee1/CDC2 and NF-κB pathways and their consequent coactivation is a new molecular mechanism of CDDP resistance in OS cells. OS with this molecular signature may respond well to Wee1 inhibition as an alternative treatment strategy.