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WO2018109107A1 - Système de surveillance, appareil de surveillance, client, et procédé de surveillance pour dispositif médical - Google Patents

Système de surveillance, appareil de surveillance, client, et procédé de surveillance pour dispositif médical Download PDF

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Publication number
WO2018109107A1
WO2018109107A1 PCT/EP2017/082886 EP2017082886W WO2018109107A1 WO 2018109107 A1 WO2018109107 A1 WO 2018109107A1 EP 2017082886 W EP2017082886 W EP 2017082886W WO 2018109107 A1 WO2018109107 A1 WO 2018109107A1
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WO
WIPO (PCT)
Prior art keywords
real
medical device
monitoring
process data
abnormal
Prior art date
Application number
PCT/EP2017/082886
Other languages
English (en)
Inventor
Si Xiang PENG
Ming Jin SUN
Original Assignee
Siemens Healthcare Gmbh
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Siemens Healthcare Gmbh filed Critical Siemens Healthcare Gmbh
Publication of WO2018109107A1 publication Critical patent/WO2018109107A1/fr

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Classifications

    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H40/00—ICT specially adapted for the management or administration of healthcare resources or facilities; ICT specially adapted for the management or operation of medical equipment or devices
    • G16H40/60—ICT specially adapted for the management or administration of healthcare resources or facilities; ICT specially adapted for the management or operation of medical equipment or devices for the operation of medical equipment or devices
    • G16H40/67—ICT specially adapted for the management or administration of healthcare resources or facilities; ICT specially adapted for the management or operation of medical equipment or devices for the operation of medical equipment or devices for remote operation
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L67/00—Network arrangements or protocols for supporting network services or applications
    • H04L67/01—Protocols
    • H04L67/12—Protocols specially adapted for proprietary or special-purpose networking environments, e.g. medical networks, sensor networks, networks in vehicles or remote metering networks

Definitions

  • the present invention relates to the technical field of medical devices and, in particular, relates to a monitoring system, a monitoring apparatus, a client, and a monitoring method for a medical device.
  • Angiography is an auxiliary examination technology that helps doctors to find a disease in a timely manner and control the progression of the disease, effectively improving a patient's survival rate.
  • An angiography system usually consists of a rack, a C-shaped arm, an X-ray tube assembly, a flat panel detector, a patient examination bed, a high pressure generator, a monitor and a suspension system therefor, a console, and the like .
  • the console During a current usage process of an angiography system, the console generates a log file when a predetermined trigger condition is satisfied.
  • the log file records non-real ⁇ time process data, and a real-time working status of the angiography system cannot be learned of in real-time based on the log file.
  • Other medical devices also exhibit a similar problem.
  • U.S. Patent Application US 20060214777 Al discloses a medical device and a method for remotely maintaining the medical device.
  • the patent application provides a data transmission device capable of transmitting data over a powerline network.
  • Chinese Patent Application CN 104238540 A discloses an information collection method and apparatus, and a medical device for system abnormality diagnosis, wherein the method includes: presetting at least one system running status to be monitored and at least one information collection operation corresponding to each system running status, where each information collection operation is respectively used for collecting different information related to fault analysis; after determining that a current system reaches any system running status to be monitored, monitoring the system running status to be monitored that the system reaches; performing an information collection operation corresponding to the system running status when an abnormality occurs in the monitored system running status; and after information collection is complete, packaging the collected information, and storing the packaged information at a specified location, and/or sending the packaged information to a remote service platform.
  • the technical solutions disclosed by the invention can collect abnormality diagnosis information of a system running status at a first time.
  • Embodiments of the invention provide a monitoring system, a monitoring apparatus, a client, and a monitoring method for a medical device, which embodiments can monitor a real-time working status of a medical device such as an angiography system in real time.
  • a monitoring system for a medical device including:
  • a client which is disposed on the medical device and is used for acquiring real-time process data about the medical device and sending the real-time process data to a cloud
  • a monitoring apparatus which is disposed at the cloud and is used for monitoring, based on the real-time process data, a real-time working status of the medical device.
  • the client sends the real-time process data to the cloud, which can allow a professional at the cloud to perform various subsequent processing on the real-time process data, and can reduce the working stress of medical care personnel at a medical device end.
  • the monitoring apparatus includes:
  • an analysis module for matching the real-time process data with an abnormal data signal feature library, wherein when matching is successful, the real-time working status is determined to be abnormal, or, when matching is unsuccessful, the real-time working status is determined to be normal.
  • whether the real-time working status of the medical device is normal can be determined by matching the real-time process data with the abnormal data signal feature library.
  • the monitoring system further includes: a first alarm module, which is disposed at the cloud and is used for issuing a first alarm signal when the real-time working status is abnormal; and/or
  • a second alarm module which is disposed on the medical device and is used for issuing a second alarm signal when the real-time working status is abnormal.
  • the monitoring system further includes: a virtual private network (VPN) gateway disposed between the client and the monitoring apparatus; and/or
  • VPN virtual private network
  • firewall disposed between the client and the monitoring apparatus .
  • information security between the client and the monitoring apparatus can be improved by setting up the VPN gateway or the firewall .
  • a monitoring apparatus for a medical device including: an interface module for acquiring real-time process data from a medical device via a remote network connection; and
  • an analysis module for matching the real-time process data with an abnormal data signal feature library, wherein when matching is successful, a real-time working status of the medical device is determined to be abnormal, or, when matching is unsuccessful, a real-time working status of the medical device is determined to be normal.
  • the monitoring apparatus in the embodiments of the present invention acquires the real-time process data about the medical device via the remote network connection, and based on the real-time process data, monitors the real-time working status of the medical device, such that a working status of the medical device can be remotely learned of in a timely manner.
  • the monitoring apparatus further includes :
  • a first alarm module for issuing a first alarm signal when the real-time working status is abnormal
  • the abnormal data signal feature library is a dynamically adjustable abnormal data signal feature library
  • the monitoring apparatus further comprises: a training module for training the abnormal data signal feature library based on an artificial neural network training algorithm.
  • an alarm prompt can be issued at the cloud, thereby facilitating working personnel at the cloud in learning of the occurrence of an abnormality in a timely manner.
  • the abnormal data signal feature library can further be trained based on the artificial neural network training algorithm, improving monitoring accuracy for the medical device.
  • a client for a medical device including:
  • a data collection module for collecting real-time process data of the medical device
  • a communication module for sending the real-time process data to a cloud.
  • the client in the embodiments of the present invention acquires the real-time process data about the medical device and sends the real-time process data to the cloud so as to facilitate the monitoring, at the cloud, of the real-time working status of the medical device.
  • the communication module is further used for receiving an abnormality notification message from the cloud.
  • the client further includes:
  • a second alarm module for issuing a second alarm signal based on the abnormality notification message.
  • an alarm prompt can be issued at the medical device end, thereby facilitating working personnel at the medical device end in learning of the occurrence of an abnormality in a timely manner.
  • a monitoring method for a medical device including :
  • the real-time process data about the medical device is acquired, the real-time process data is sent to the cloud via the remote network connection, and the cloud can monitor, based on the real-time process data, the real-time working status of the medical device, so that the working status of the medical device can be learned of in a timely manner.
  • the monitoring, at the cloud and based on the real-time process data, of the real-time working status of the medical device includes:
  • the abnormal data signal feature library can be trained based on the artificial neural network training algorithm, thereby improving monitoring accuracy for a medical device such as an angiography system.
  • FIG. 1 is a structural diagram of a monitoring system for a medical device according to an embodiment of the present invention .
  • FIG. 2 is a structural diagram of a client for a medical device according to an embodiment of the present invention.
  • FIG. 3 is a structural diagram of a monitoring apparatus for a medical device according to an embodiment of the present invention .
  • FIG. 4 is an exemplary structural diagram of a monitoring system for a medical device according to an embodiment of the present invention.
  • FIG. 5 is a flowchart of a monitoring method for a medical device according to an embodiment of the present invention.
  • FIG. 1 is a structural diagram of a monitoring system for a medical device according to an embodiment of the present invention.
  • an angiography system is taken as an example for description, and same can also be implemented for other medical devices.
  • the monitoring system 30 includes:
  • a client 12 which is disposed on an angiography system 10 and is used for acquiring real-time process data about the angiography system 10 and sending the real-time process data to a cloud;
  • a monitoring apparatus 11 which is disposed at the cloud and is used for monitoring, based on the real-time process data, a real-time working status of the angiography system 10.
  • the real-time process data about the angiography system 10 refers to real-time data related to a working process of the angiography system 10, including at least the following:
  • a voltage value of a power supply for example, a voltage value of a power supply; a current value of a tube; a status of an air switch; a temperature value of a high pressure generator; parameters of a monitor; a status of a foot gate; a status of an intercom system, and the like.
  • Various types of hardware sensors are typically disposed in the angiography system 10, and based on these hardware sensors, real-time parameters of corresponding pieces of hardware of the angiography system 10 can be detected. The hardware sensors send the respective detected real-time hardware parameters to a console (not shown in FIG. 1) of the angiography system 10.
  • CRA cranial angle of inclination
  • CAU caudal angle of inclination
  • REO right anterior oblique angle
  • LAO left anterior oblique angle
  • C-arm rotation speed set value a sampling rate set value, and the like.
  • a software system of the angiography system sends the various types of real-time software parameters to the console of the angiography system 10.
  • the software system of the angiography system 10 determines, based on user operation actions, corresponding user operation data and sends the user operation data to the console.
  • the client 12 is preferably disposed in the console of the angiography system 10 so as to acquire the real-time process data aggregated in the console.
  • the client 12 may be not disposed in the console but instead be disposed as a discrete module on the angiography system 10.
  • the discrete module has a wired or wireless connection to the console and can acquire the real ⁇ time process data of the angiography system 10 from the console .
  • the client 12 may be implemented as a hardware module, may be implemented as a software module, or may be implemented as a hybrid structure combining hardware and software. When implemented as a hardware module, the client 12 may specifically include various types of data collection hardware circuits. When implemented as a software module, the client 12 may specifically be data collection software installed in the console. When the client 12 is implemented as a hybrid structure combining hardware and software, the client 12 not only includes a data collection hardware circuit, but also includes data collection software installed in the console.
  • the monitoring apparatus 11 includes: an interface module (not shown in FIG. 1) for acquiring the real-time process data from the client 12; and
  • an analysis module (not shown in FIG. 1) for matching the real-time process data acquired by the interface module with an abnormal data signal feature library, wherein when matching is successful, the real-time working status is determined to be abnormal, or, when matching is unsuccessful, the real-time working status is determined to be normal.
  • the abnormal data signal feature library includes data features of abnormal data and is used for determining whether a working status of the angiography system is abnormal.
  • the abnormal data signal feature library may be implemented as a static abnormal data signal feature library configured in the analysis module, or implemented as a static abnormal data signal feature library stored in a database accessible to the analysis module.
  • the meaning of a static abnormal data signal feature library is that data features of abnormal data contained therein are fixed.
  • the analysis module carries out feature matching with regard to the real-time process data and the static abnormal data signal feature library. When feature matching is successful, it is determined that the real-time working status is abnormal, and when feature matching is unsuccessful, it is determined that the real-time working status is normal.
  • the following fixed rule is stored in the static abnormal data signal feature library: when the temperature of a high pressure generator is greater than 80 degrees Celsius, it is determined that the high pressure generator is abnormal due to a short circuit. Assuming that the real-time process data acquired by the interface module indicates that the temperature of the high-pressure generator is 85 degrees, the analysis module determines that a feature of the real-time process data matches a feature of the fixed rule in the static abnormal data signal feature library, and therefore determines the real-time working status of the angiography system to be abnormal.
  • the abnormal data signal feature library may be implemented as a dynamic abnormal data signal feature library configured in the analysis module, or implemented as a dynamic abnormal data signal feature library stored in a database accessible to the analysis module.
  • a dynamic abnormal data signal feature library is that data features of abnormal data contained therein are not fixed, but may be dynamically adjusted based on a training process.
  • the abnormal data signal feature library may be trained based on an artificial neural network training algorithm and a large amount of training data.
  • the analysis module carries out feature matching with regard to the real-time process data and the trained dynamic abnormal data signal feature library. When feature matching is successful, it is determined that the real-time working status is abnormal, and when feature matching is unsuccessful, it is determined that the real-time working status is normal.
  • the following initial rule is stored in the dynamic abnormal data signal feature library: when the temperature of a high pressure generator is greater than 70 degrees Celsius, it is determined that the high pressure generator is abnormal due to a short circuit.
  • working personnel first acquire training data based on a large amount of empirical data, and use the training data as input parameters for the artificial neural network training algorithm so as to train the dynamic abnormal data signal feature library. It is assumed that the training data contains the following rule: when the temperature of a high pressure generator is greater than 80 degrees Celsius, the high pressure generator is abnormal due to a short circuit.
  • the initial rule is adjusted to be: when the temperature of a high pressure generator is greater than 80 degrees Celsius, it is determined that the high pressure generator is abnormal due to a short circuit.
  • the analysis module determines that a feature of the real-time process data matches a feature of the trained rule in the dynamic abnormal data signal feature library, and may therefore determine that the real-time working status of the angiography system is abnormal.
  • the monitoring system 30 further includes: a first alarm module (not shown in FIG. 1), which is disposed at the cloud (preferably disposed in the monitoring apparatus 11) and is used for issuing a first alarm signal when the real-time working status is abnormal.
  • a first alarm module (not shown in FIG. 1), which is disposed at the cloud (preferably disposed in the monitoring apparatus 11) and is used for issuing a first alarm signal when the real-time working status is abnormal.
  • the first alarm module disposed at the cloud may prompt working personnel at the cloud by using a plurality of prompting manners such as a sound prompt, an optical prompt, and an interface prompt. Therefore, the working personnel at the cloud can learn, in a timely manner, of the occurrence of an abnormality in the angiography system which is remotely located.
  • the monitoring system 30 further includes: a second alarm module (not shown in FIG. 1), which is disposed in the angiography system 10 (preferably disposed in the client 12) and is used for issuing a second alarm signal when the real-time working status is abnormal.
  • a second alarm module (not shown in FIG. 1), which is disposed in the angiography system 10 (preferably disposed in the client 12) and is used for issuing a second alarm signal when the real-time working status is abnormal.
  • the interface module sends an abnormality notification message to the angiography system.
  • the second alarm module disposed in the angiography system prompts, based on the abnormality notification message, working personnel at an angiography system end by using a plurality of prompting manners such as a sound prompt, an optical prompt, and an interface prompt. Therefore, the working personnel at the angiography system end can also learn of the occurrence of an abnormality in a timely manner.
  • the monitoring system further includes: a VPN gateway (not shown in FIG. 1) disposed between the client and the monitoring apparatus.
  • the VPN gateway may use a double network card structure.
  • the client and the monitoring apparatus are separately connected to the Internet via an external network card of the VPN gateway; the client 12 accesses an internal network of the monitoring apparatus 11 via an internal network card of the VPN gateway, and the monitoring apparatus 11 accesses an internal network of the client 12 via the internal network card of the VPN gateway.
  • the monitoring system 30 further includes: a firewall (not shown in FIG. 1) disposed between the client and the monitoring apparatus.
  • the firewall may include: a hardware firewall, a data packet filtering-type software firewall, a circuit layer gateway, an application level gateway, and the like. Respective firewalls may be respectively disposed on the client and the monitoring apparatus, or a shared firewall may be disposed between the client and the monitoring apparatus.
  • the embodiments of the present invention further provide a client for a medical device .
  • FIG. 2 is a structural diagram of a client for a medical device according to an embodiment of the present invention.
  • an angiography system is still taken as an example for description, and same can also be implemented for other medical devices .
  • the client 42 includes:
  • a data collection module 121 for collecting real-time process data of an angiography system
  • a communication module 122 for sending the real-time process data to a cloud.
  • the communication module 122 may send the real-time process data to the cloud in a wired transmission manner or a wireless transmission manner.
  • the communication module 122 is further used for receiving an abnormality notification message from the cloud; and the client 42 further comprises: a second alarm module 123 for issuing, based on the abnormality notification message, a second alarm signal.
  • the client 42 shown in FIG. 2 may be disposed in a console of the angiography system, and acquire real-time process data aggregated in the console.
  • the client 42 may also be not disposed in the console, but rather disposed as a discrete module on the angiography system.
  • the discrete module has a wired or wireless connection to the console and can acquire the real-time process data about the angiography system 10 from the console.
  • the client 42 shown in FIG. 2 may be implemented as a hardware module, may be implemented as a software module, or may be implemented as a hybrid structure combining hardware and software.
  • the client 42 may specifically include various types of data collection hardware circuits.
  • the client 42 may specifically be data collection software installed in the console.
  • the client 42 is implemented as a hybrid structure combining hardware and software, the client 42 not only includes a data collection hardware circuit, but also includes data collection software installed in the console.
  • FIG. 3 is a structural diagram of a monitoring apparatus for a medical device according to an embodiment of the present invention.
  • an angiography system is taken as an example for description, and same can also be implemented for other medical devices.
  • the monitoring apparatus 51 includes: an interface module 111 for acquiring real-time process data from an angiography system via a remote network connection; and
  • an analysis module 112 for matching the real-time process data with an abnormal data signal feature library, wherein when matching is successful, a real-time working status of the angiography system is determined to be abnormal, or, when matching is unsuccessful, a real-time working status of the angiography system is determined to be normal.
  • the monitoring apparatus 51 further includes :
  • a first alarm module 114 for issuing a first alarm signal when the real-time working status is abnormal.
  • the abnormal data signal feature library may be implemented as a dynamically adjustable abnormal data signal feature library.
  • the monitoring apparatus further includes a training module 113 for training the abnormal data signal feature library based on an artificial neural network training algorithm.
  • FIG. 4 is an exemplary structural diagram of a monitoring system for a medical device according to an embodiment of the present invention.
  • an angiography system is taken as an example for description, and same can also be implemented for other medical devices.
  • each angiography system 20 is respectively disposed in a corresponding department of a respective hospital. These hospitals may be located in the same city, or may be located in different cities, or even located in different countries.
  • One client 22 is respectively disposed in each angiography system 20. Each client 22 acquires real-time process data aggregated in a console of the respective angiography system in real time. The real-time process data acquired by each client 22 is separately sent, via a client firewall 241, the Internet 23, and a cloud firewall 242, to a monitoring apparatus 21 disposed at a cloud.
  • the monitoring apparatus 21 disposed at the cloud includes an interface module 211, an analysis module 212, a database 213, and a first alarm module 214.
  • the interface module 211 respectively receives the real-time process data of the respective angiography systems 20 from the respective clients 22.
  • the real-time process data received by the interface module 211 is preferably continuous and real-time data, specifically including real-time hardware parameters, real-time software parameters, user operation data, and the like of the angiography system.
  • the database 213 buffers the real-time process data received by the interface module 211.
  • the cloud firewall 242 is disposed in the monitoring apparatus 21. The cloud firewall 242 filters and scans the real-time process data sent by each of the clients 22 according to a specific rule.
  • the analysis module 212 stores an abnormal data signal feature library.
  • the analysis module 212 respectively matches the real-time process data of each angiography system with the abnormal data signal feature library, wherein when matching is successful, a real-time working status of the angiography system is determined to be abnormal, and when matching is unsuccessful, the real-time working status of the angiography system is determined to be normal.
  • the first alarm module 214 prompts working personnel 215 at the cloud by using prompting manners such as a sound prompt, an optical prompt, and an interface prompt.
  • Research and development personnel 216 at the cloud may further train the abnormal data signal feature library in the analysis module 212 based on an artificial neural network training algorithm.
  • the embodiments of the present invention further provide a monitoring method for a medical device.
  • FIG. 5 is a flowchart of a monitoring method for a medical device according to an embodiment of the present invention.
  • an angiography system is taken as an example for description, and same can also be implemented for other medical devices .
  • the method includes:
  • Step 501 Establishing a remote network connection between an angiography system and a cloud
  • Step 502 Acquiring real-time process data about the angiography system, and sending the real-time process data to the cloud based on the remote network connection;
  • Step 503 Monitoring, at the cloud and based on the real ⁇ time process data, a real-time working status of the angiography system.
  • the monitoring, at the cloud and based on the real-time process data, of a real-time working status of an angiography system in step 503 includes: determining an abnormal data signal feature library; based on an artificial neural network training algorithm, training the abnormal data signal feature library; and matching the real-time process data with the trained abnormal data signal feature library, wherein when matching is successful, the real-time working status is determined to be abnormal, or, when matching is unsuccessful, the real-time working status is determined to be normal.
  • a monitoring system includes: a client, which is disposed on an angiography system and is used for acquiring real-time process data of the angiography system and sending the real-time process data to a cloud; and a monitoring apparatus, which is disposed at the cloud and is used for monitoring, based on the real-time process data, a real-time working status of the angiography system.
  • the embodiments of the present invention provide a technical solution for remotely monitoring a medical device in real time so that a working status of the medical device, such as an angiography, system can be learned of in a timely manner.
  • a client sends the real-time process data to the cloud, which can allow a professional at the cloud to perform various subsequent processing on the real-time process data, and can reduce the working stress of medical care personnel at a medical device end, such as an angiography system.
  • the embodiments of the present invention can not only an alarm prompt at the cloud, but also issue an alarm prompt at the medical device end, such as an angiography system so as to facilitate working personnel everywhere in learning about the occurrence of an abnormality in a timely manner.
  • an abnormal data signal feature library can be trained based on an artificial neural network training algorithm, thereby improving monitoring accuracy for a medical device, such as an angiography system.
  • An embodiment of the invention also provides a computer readable medium having computer instructions stored thereon that, when executed by a processor, cause the processor to perform the monitoring method for a medical device described above.
  • a system or an apparatus provided with a storage medium may be provided, which storage medium stores a software program code that implements the functions of any of the embodiments described above, and a computer (either a CPU or an MPU) of the system or the apparatus reads and executes the program code stored in the storage medium.
  • the program code itself which is read from the storage medium, may realize the functions of any one of the embodiments described above, and therefore, the program code and the storage medium storing the program code form a part of the present invention.
  • Storage medium embodiments for providing the program code include a floppy disk, a hard disk, a magneto-optical disk, an optical disk (such as a CD-ROM, a CD-R, a CD-RW, a DVD-ROM, a DVD-RAM, a DVD-RW, a DVD+RW) , a magnetic tape, a non-volatile memory card, and a ROM.
  • the program code may be downloaded from a server computer via a communication network.
  • the program code read from the storage medium is written into a memory provided in an expansion board inserted into the computer or into a memory provided in an expansion unit connected to the computer, and then, based on the instructions of the program code, the CPU or the like mounted on the expansion board or the expansion unit is made to perform some or all of the actual operations so as to realize the functions of any one of the embodiments described above.
  • a hardware unit may be implemented mechanically or electrically.
  • a hardware unit may comprise a permanent dedicated circuit or logic (such as a dedicated processor, FPGA or ASIC) so as to accomplish a corresponding operation.
  • the hardware unit may also comprise a programmable logic or circuit (such as a universal processor or other programmable processors) , and may be set temporarily by software so as to accomplish a corresponding operation.
  • a programmable logic or circuit such as a universal processor or other programmable processors
  • the specific implementation method mechanically, a dedicated permanent circuit, or a temporarily set circuit

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Abstract

La présente invention concerne, selon des modes de réalisation, un système de surveillance, un appareil de surveillance, un client, et un procédé de surveillance pour un dispositif médical, en particulier un système d'angiographie. Le système de surveillance comprend : un client, qui est disposé sur le dispositif médical et qui est utilisé pour acquérir des données de processus en temps réel concernant le dispositif médical et pour envoyer les données de processus en temps réel à un nuage ; et un appareil de surveillance, qui est disposé au niveau du nuage et qui est utilisé pour surveiller, sur la base des données de processus en temps réel, un état de fonctionnement en temps réel du dispositif médical. Au moyen des modes de réalisation de la présente invention, un état de fonctionnement du dispositif médical peut être notifié de manière opportune. Lorsqu'un état de fonctionnement en temps réel du dispositif médical est anormal, les modes de réalisation de la présente invention peuvent non seulement émettre une invite d'alarme au niveau du nuage, mais également émettre une invite d'alarme au niveau du dispositif médical, permettant ainsi au personnel à tout endroit d'être informé de l'apparition d'une anomalie d'une manière opportune. Les modes de réalisation de la présente invention peuvent former une bibliothèque de caractéristiques de signal de données anormales sur la base d'un algorithme d'apprentissage de réseau neuronal artificiel, ce qui permet d'améliorer la précision de la surveillance.
PCT/EP2017/082886 2016-12-16 2017-12-14 Système de surveillance, appareil de surveillance, client, et procédé de surveillance pour dispositif médical WO2018109107A1 (fr)

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CN201611167356.XA CN108206837A (zh) 2016-12-16 2016-12-16 医疗设备的监测系统、监测装置、客户端和监测方法

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CN112786181A (zh) * 2019-11-01 2021-05-11 科美诊断技术股份有限公司 一种运行数据的上传方法及装置
CN114093497A (zh) * 2021-11-23 2022-02-25 江苏大学附属医院 基于虚拟专用网络的放疗远程会诊监控装置、系统及方法
CN117251698A (zh) * 2023-11-17 2023-12-19 北京德众国良环保科技有限公司 一种基于区块链的饮食业油烟净化在线监控方法及系统
CN117809861A (zh) * 2024-01-18 2024-04-02 南京裕隆生物医学发展有限公司 用于医疗数据管理平台的远程实时监测协作方法及系统
CN118013892A (zh) * 2024-04-07 2024-05-10 杭州汽轮动力集团股份有限公司 一种基于多物理场的燃气轮机状态实时监控方法和装置
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CN119092078A (zh) * 2024-11-06 2024-12-06 安徽中技国医医疗科技有限公司 一种手术室spd医用耗材智能监测追溯管理方法

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CN117251698B (zh) * 2023-11-17 2024-01-19 北京德众国良环保科技有限公司 一种基于区块链的饮食业油烟净化在线监控方法及系统
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CN118013892A (zh) * 2024-04-07 2024-05-10 杭州汽轮动力集团股份有限公司 一种基于多物理场的燃气轮机状态实时监控方法和装置
CN118534515A (zh) * 2024-07-23 2024-08-23 苏州阿基米德网络科技有限公司 一种医疗环境辐射的检测方法、终端及系统
CN119092078A (zh) * 2024-11-06 2024-12-06 安徽中技国医医疗科技有限公司 一种手术室spd医用耗材智能监测追溯管理方法

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