System and method for realizing remote medical image consultation

Abstract

The invention provides a system and a method for realizing remote medical image consultation, and belongs to the technical field of image consultation. Comprising a medical image acquisition and uploading module which is used for acquiring and uploading medical images; and the data storage and management module is used for providing cloud storage and data indexing and retrieval functions. Through the modern network technology, the cloud computing platform and the artificial intelligence technology, medical images and diagnosis information can be efficiently transmitted to experts, geographical restrictions are crossed, timely and accurate diagnosis is provided for patients, medical resource allocation can be optimized, medical efficiency can be improved, and the medical system is suitable for popularization and application. Meanwhile, the problem that patients in many remote areas are difficult to obtain high-quality medical services is also solved, the medical data are encrypted through artificial intelligence, and the situation that the privacy of the patients is leaked due to the fact that the image data may be accessed without authorization in the storage and transmission process is avoided.

Description

Technical Field

[0001] The present invention relates to the technical field of image consultation, and in particular to a system and method for realizing remote medical image consultation. Background Art

[0002] Telemedicine has emerged as the times require, breaking through the time and space limitations of traditional medical services and providing patients with efficient and convenient medical services.

[0003] However, some existing remote medical image consultation technologies do not have good encryption methods. Since medical data is highly sensitive, image data may be accessed without authorization during storage and transmission, thereby leaking patient privacy. In addition, the efficiency of remote medical image consultation is not high. Therefore, this application provides a system and method for realizing remote medical image consultation to meet the needs. Summary of the invention

[0004] The technical problem to be solved by the present invention is to provide a system and method for realizing remote medical image consultation to solve the existing problems.

[0005] In order to solve the above technical problems, the present invention provides the following technical solutions: A system for implementing remote medical image consultation, comprising: a medical image acquisition and upload module, the medical image acquisition and upload module being used for acquiring and uploading medical images; A data storage and management module, which is used to provide cloud storage and data indexing and retrieval functions. The cloud storage includes using cloud computing technology to store medical image data on a cloud server to ensure data security and high availability. The data indexing and retrieval includes indexing image data and classifying and storing them according to information such as patient ID, disease type, and image examination date to facilitate quick retrieval. Image analysis and diagnosis module, which includes artificial intelligence-assisted diagnosis and expert system support; A remote consultation module, wherein the remote consultation module is used for conducting remote consultation; The feedback and report generation module is used to generate a detailed diagnosis report based on the consultation results, and send the diagnosis report to the patient or referral hospital through the remote platform. The patient can also view the report in real time through the terminal device; User terminal devices, including patient terminal devices: patients can make appointments for imaging examinations, view diagnostic reports, participate in remote consultations, etc. through mobile terminal devices; Doctor-side devices: Doctors use PC or mobile devices to view, consult and diagnose medical images.

[0006] Optionally, the medical image acquisition and uploading module includes an image acquisition device, and the image acquisition device includes CT, MRI, X-ray, and ultrasonic medical imaging equipment, which is used to obtain medical imaging data of patients; It also includes an image preprocessing module, which is used to perform denoising, enhancement, and image segmentation preprocessing operations on the image data to ensure data quality; It also includes a data uploading module, which uploads the processed image data to a cloud platform or a telemedicine center in an encrypted manner for further analysis.

[0007] Optionally, the remote consultation module includes a video consultation system, a collaborative work platform and an electronic health record. The video consultation system is used to support video conferencing and voice communication between doctors and patients or multiple doctors, and doctors can share medical images in real time for discussion; the collaborative work platform is used for doctors to jointly view the patient's medical imaging data and diagnostic reports on the remote platform, and discuss the condition and collaboratively diagnose; the electronic health record is used during the consultation process, so that doctors can view the patient's complete medical history, imaging data and previous diagnostic results to form a complete medical record.

[0008] Optionally, data encryption in the data upload module is performed in an artificial intelligence manner.

[0009] A method for implementing remote medical imaging consultation includes the following steps: S1. Patients undergo medical imaging examinations at local medical institutions, and imaging equipment scans the patients and generates digital images; S2, the image data is pre-processed and then uploaded to the cloud platform or the server of the telemedicine center through encrypted transmission; S3, the uploaded imaging data is stored and managed by the cloud platform, and indexed by metadata such as patient ID and imaging type; S4. Use deep learning and computer vision technology to automatically analyze image data, give preliminary diagnostic suggestions, and generate analysis reports; S5. Doctors access patients’ medical images and medical records through the remote consultation platform and use the video conferencing function to communicate with other experts or patients in real time; S6. Based on the consultation results, a diagnosis report is automatically generated and sent to the patient or referral hospital through the telemedicine platform. The patient can view the report through a mobile device.

[0010] Optionally, the encryption method in S2 adopts artificial intelligence encryption, specifically: S21. Use a neural network model to encrypt and decrypt communication data, use an autoencoder to learn the representation of the data, and encode it into a vector of the hidden layer; S22, using a generative adversarial network to generate fake data, and then mixing the original data with the fake data for encryption; S23, performing noise processing on the data to obtain noisy data, and performing dimension reduction on the noisy data through a DNN network to obtain a binary encrypted data string; S24, processing the noisy data through an encryption encoder to obtain output data of a network hidden layer; S25. Use quantum key distribution technology to distribute encryption keys through the quantum state between quantum bits to achieve secure transmission of information; S26. After the transmission is completed, the transmitted data is decrypted.

[0011] Optionally, the specific steps of using the autoencoder to encrypt communication data in S21 are as follows: S211. Train an autoencoder to learn a compressed representation of data. The autoencoder consists of two parts: an encoder and a decoder. The encoder encodes the original data into a low-dimensional representation, and the decoder decodes the low-dimensional representation into a reconstruction of the original data. During the training process, the goal of the autoencoder is to minimize the reconstruction error, that is, the difference between the input data and the reconstructed data. S212. After the autoencoder is trained, the encoder part is used to encrypt the data. This can be achieved by inputting the original data into the encoder and obtaining its compressed representation. Since the encoder learns a low-dimensional representation of the data, this representation can be regarded as encrypted data. S213. To decrypt the data, just input the encrypted low-dimensional representation into the decoder.

[0012] Optionally, the specific steps of using the adversarial network to encrypt communication data in S22 are as follows: S221. Generate encrypted data using the generator of GANs. This can be achieved by taking real data as input and training the generator to generate data. The generator is trained to generate synthetic data that is as similar to real data as possible, but in fact the data it generates is an abstract representation of the real data and can therefore be regarded as encrypted data. S222. The generated encrypted data can be transmitted to the recipient through a secure channel. Since these data are generated by the generator, they do not directly expose the content of the original data, thereby providing a certain degree of confidentiality.

[0013] Optionally, the quantum technology in S25 is specifically: using the principles of quantum mechanics to create and distribute encryption keys, which are used for encryption and decryption operations in traditional encrypted communications. In QKD, the communicating parties create shared random keys by sending and receiving quantum bits, which are then used to encrypt the transmitted data and use the randomness in the quantum system to generate random numbers. Since the measurement results in the quantum system are uncertain, this uncertainty can be used to generate truly random numbers, which can be used as encryption keys. At the same time, quantum entanglement can be used to create secure encryption keys, because the measurement results of a quantum system will immediately affect other quantum systems entangled with it, thereby preventing interference from unauthorized eavesdroppers. Quantum state measurements are used to check whether the transmitted quantum bits are interfered with or eavesdropped. By performing appropriate quantum state measurements, the communicating parties can detect any attempts to eavesdrop or interfere with communications.

[0014] Compared with the prior art, the present invention has at least the following beneficial effects: In the above solution, through modern network technology, cloud computing platform and artificial intelligence technology, medical images and diagnostic information can be efficiently transmitted to experts, transcending geographical restrictions, providing patients with timely and accurate diagnosis, and optimizing the allocation of medical resources and improving medical efficiency. At the same time, it also solves the problem that many patients in remote areas have difficulty in obtaining high-quality medical services. Medical data is encrypted through artificial intelligence to prevent unauthorized access to image data during storage and transmission, thereby leaking patient privacy. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] The accompanying drawings, which are incorporated herein and constitute a part of the specification, illustrate embodiments of the present disclosure and, together with the description, further serve to explain the principles of the present disclosure and to enable those skilled in the relevant art to make and use the present disclosure.

[0016] Figure 1 A schematic diagram of the structure of a system for realizing remote medical image consultation; Figure 2 The present invention is a flowchart of a method for realizing remote medical imaging consultation. DETAILED DESCRIPTION

[0017] The following is a detailed description of a system and method for implementing remote medical image consultation provided by the present invention in conjunction with the accompanying drawings and specific embodiments. At the same time, it is explained here that in order to make the embodiments more detailed, the following embodiments are listed as the best and preferred embodiments, and other alternatives can also be used by technicians in some known technical fields; and the accompanying drawings are only for more specific description of the embodiments, and are not intended to specifically limit the present invention.

[0018] It should be noted that the references to "one embodiment", "embodiment", "exemplary embodiment", "some embodiments" and the like in the specification indicate that the described embodiments may include specific features, structures or characteristics, but not every embodiment may include the specific features, structures or characteristics. In addition, when a specific feature, structure or characteristic is described in conjunction with an embodiment, it should be within the knowledge of a person skilled in the art to implement such feature, structure or characteristic in conjunction with other embodiments (whether or not explicitly described).

[0019] In general, a term can be understood, at least in part, from its use in context. For example, depending, at least in part, on the context, the term "one or more" as used herein can be used to describe any feature, structure, or characteristic in the singular sense, or can be used to describe a combination of features, structures, or characteristics in the plural sense. Additionally, the term "based on" can be understood as not necessarily intended to convey an exclusive set of factors, but can instead, depending, at least in part, on the context, allow for the presence of other factors that are not necessarily explicitly described.

[0020] It will be understood that the meaning of “on,” “over,” and “above” in this disclosure should be interpreted in the broadest manner, so that “on” not only means “directly on” something, but also includes the meaning of being “on” something with intervening features or layers therebetween, and “on” or “over” not only means “on” or “above” something, but also includes the meaning of being “on” or “above” something with no intervening features or layers therebetween.

[0021] Additionally, spatially relative terms such as "under," "beneath," "lower," "above," "upper," and the like may be used herein for descriptive convenience to describe the relationship of one element or feature to another element or features, as shown in the accompanying drawings. Spatially relative terms are intended to encompass different orientations of the device in use or operation in addition to the orientation depicted in the accompanying drawings. The device may be oriented in other ways, and the spatially relative descriptors used herein may be similarly interpreted accordingly.

[0022] like Figure 1 As shown, an embodiment of the present invention provides a system for implementing remote medical image consultation, including: a medical image acquisition and upload module, the medical image acquisition and upload module is used for acquiring and uploading medical images; Data storage and management module: The data storage and management module is used to provide cloud storage and data indexing and retrieval functions. Cloud storage includes using cloud computing technology to store medical imaging data on cloud servers to ensure data security and high availability. Data indexing and retrieval includes indexing imaging data and classifying and storing them according to patient ID, disease type, imaging examination date and other information to facilitate quick retrieval. Image analysis and diagnosis module, which includes artificial intelligence-assisted diagnosis and expert system support; Remote consultation module, which is used for remote consultation; The feedback and report generation module is used to generate a detailed diagnosis report based on the consultation results, and send the diagnosis report to the patient or referral hospital through the remote platform. The patient can also view the report in real time through the terminal device; User terminal devices, including patient terminal devices: patients can make appointments for imaging examinations, view diagnostic reports, participate in remote consultations, etc. through mobile terminal devices; Doctor-side devices: Doctors use PC or mobile devices to view, consult and diagnose medical images.

[0023] It should be further explained in this embodiment that the medical image acquisition and uploading module includes an image acquisition device, and the image acquisition device includes CT, MRI, X-ray, and ultrasonic medical imaging equipment, which are used to obtain medical imaging data of patients; It also includes an image preprocessing module, which is used to perform denoising, enhancement, and image segmentation preprocessing operations on image data to ensure data quality; It also includes a data upload module, which uploads the processed image data to a cloud platform or a telemedicine center in an encrypted manner for further analysis.

[0024] It should be further explained in this embodiment that the remote consultation module includes a video consultation system, a collaborative work platform and an electronic health record. The video consultation system is used to support video conferencing and voice communication between doctors and patients or multiple doctors, and doctors can share medical images in real time for discussion; the collaborative work platform is used for doctors to jointly view the patient's medical imaging data and diagnostic reports on the remote platform, and discuss the condition and collaboratively diagnose; the electronic health record is used during the consultation process, so that doctors can view the patient's complete medical history, imaging data and previous diagnostic results to form a complete medical record.

[0025] It should be further explained in this embodiment that data encryption in the data upload module is performed in an artificial intelligence manner.

[0026] like Figure 2 As shown, a method for implementing remote medical imaging consultation includes the following steps: S1. Patients undergo medical imaging examinations at local medical institutions, and imaging equipment scans the patients and generates digital images; S2, the image data is pre-processed and then uploaded to the cloud platform or the server of the telemedicine center through encrypted transmission; S3, the uploaded imaging data is stored and managed by the cloud platform, and indexed by metadata such as patient ID and imaging type; S4. Use deep learning and computer vision technology to automatically analyze image data, give preliminary diagnostic suggestions, and generate analysis reports; S5. Doctors access patients’ medical images and medical records through the remote consultation platform and use the video conferencing function to communicate with other experts or patients in real time; S6. Based on the consultation results, a diagnosis report is automatically generated and sent to the patient or referral hospital through the telemedicine platform. The patient can view the report through a mobile device.

[0027] It should be further explained in this embodiment that the encryption method in S2 adopts artificial intelligence encryption, specifically: S21. Use a neural network model to encrypt and decrypt communication data, use an autoencoder to learn the representation of the data, and encode it into a vector of the hidden layer; S22, using a generative adversarial network to generate fake data, and then mixing the original data with the fake data for encryption; S23, performing noise processing on the data to obtain noisy data, and performing dimension reduction on the noisy data through a DNN network to obtain a binary encrypted data string; S24, processing the noisy data through an encryption encoder to obtain output data of a network hidden layer; S25. Use quantum key distribution technology to distribute encryption keys through the quantum state between quantum bits to achieve secure transmission of information; S26. After the transmission is completed, the transmitted data is decrypted.

[0028] It should be further explained in this embodiment that the specific steps of using the autoencoder to encrypt communication data in S21 are as follows: S211. Train an autoencoder to learn a compressed representation of data. The autoencoder consists of two parts: an encoder and a decoder. The encoder encodes the original data into a low-dimensional representation, and the decoder decodes the low-dimensional representation into a reconstruction of the original data. During the training process, the goal of the autoencoder is to minimize the reconstruction error, that is, the difference between the input data and the reconstructed data. S212. After the autoencoder is trained, the encoder part is used to encrypt the data. This can be achieved by inputting the original data into the encoder and obtaining its compressed representation. Since the encoder learns a low-dimensional representation of the data, this representation can be regarded as encrypted data. S213. To decrypt the data, just input the encrypted low-dimensional representation into the decoder.

[0029] It should be further explained in this embodiment that the specific steps of using the adversarial network to encrypt communication data in S22 are as follows: S221. Generate encrypted data using the generator of GANs. This can be achieved by taking real data as input and training the generator to generate data. The generator is trained to generate synthetic data that is as similar to real data as possible, but in fact the data it generates is an abstract representation of the real data and can therefore be regarded as encrypted data. S222. The generated encrypted data can be transmitted to the recipient through a secure channel. Since these data are generated by the generator, they do not directly expose the content of the original data, thereby providing a certain degree of confidentiality.

[0030] It should be further explained that the quantum technology in S25 is specifically: using the principles of quantum mechanics to create and distribute encryption keys, which are used for encryption and decryption operations in traditional encrypted communications. In QKD, the communicating parties create shared random keys by sending and receiving quantum bits, which are then used to encrypt the transmitted data and use the randomness in the quantum system to generate random numbers. Since the measurement results in the quantum system are uncertain, this uncertainty can be used to generate true random numbers, which can be used as encryption keys. At the same time, quantum entanglement can be used to create secure encryption keys, because the measurement results of a quantum system will immediately affect other quantum systems entangled with it, thereby preventing the intervention of unauthorized eavesdroppers. The transmitted quantum bits are checked for interference or eavesdropping through quantum state measurement. By performing appropriate quantum state measurements, the communicating parties can detect any attempt to eavesdrop or interfere with communications.

[0031] The present invention covers any substitution, modification, equivalent method and scheme made on the essence and scope of the present invention. In order to make the public have a thorough understanding of the present invention, specific details are described in detail in the following preferred embodiments of the present invention, but those skilled in the art can fully understand the present invention without the description of these details. In addition, in order to avoid unnecessary confusion about the essence of the present invention, well-known methods, processes, procedures, components and circuits are not described in detail.

[0032] A person skilled in the art will appreciate that all or part of the steps in the above-mentioned embodiment method can be completed by instructing related hardware through a program, and the program can be stored in a computer-readable storage medium, such as ROM / RAM, a disk, an optical disk, etc.

[0033] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principle of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A system for realizing remote medical image consultation, characterized in that: include: A medical image acquisition and upload module, which is used for the acquisition and upload of medical images; A data storage and management module, which is used to provide cloud storage and data indexing and retrieval functions. The cloud storage includes using cloud computing technology to store medical image data on a cloud server to ensure data security and high availability. The data indexing and retrieval includes indexing image data and classifying and storing them according to information such as patient ID, disease type, and image examination date to facilitate quick retrieval. Image analysis and diagnosis module, which includes artificial intelligence-assisted diagnosis and expert system support; A remote consultation module, wherein the remote consultation module is used for conducting remote consultation; The feedback and report generation module is used to generate a detailed diagnosis report based on the consultation results, and send the diagnosis report to the patient or referral hospital through the remote platform. The patient can also view the report in real time through the terminal device; User terminal devices, including patient terminal devices: patients can make appointments for imaging examinations, view diagnostic reports, participate in remote consultations, etc. through mobile terminal devices; Doctor-side devices: Doctors use PC or mobile devices to view, consult and diagnose medical images.

2. A system for implementing remote medical image consultation according to claim 1, characterized in that: The medical image acquisition and uploading module includes an image acquisition device, which includes CT, MRI, X-ray, and ultrasonic medical imaging equipment, for acquiring medical image data of patients; It also includes an image preprocessing module, which is used to perform denoising, enhancement, and image segmentation preprocessing operations on the image data to ensure data quality; It also includes a data uploading module, which uploads the processed image data to a cloud platform or a telemedicine center in an encrypted manner for further analysis.

3. A system for implementing remote medical image consultation according to claim 1, characterized in that: The remote consultation module includes a video consultation system, a collaborative work platform and an electronic health record. The video consultation system is used to support video conferencing and voice communication between doctors and patients or multiple doctors, and doctors can share medical images in real time for discussion; the collaborative work platform is used for doctors to jointly view the patient's medical imaging data and diagnostic reports on the remote platform, and discuss the condition and collaboratively diagnose; the electronic health record is used during the consultation process, so that doctors can view the patient's complete medical history, imaging data and previous diagnostic results to form a complete medical record.

4. A system for implementing remote medical image consultation according to claim 2, characterized in that: The data encryption in the data upload module is performed in an artificial intelligence manner.

5. A method for implementing remote medical image consultation, applied to a system for implementing remote medical image consultation as claimed in any one of claims 1 to 4, characterized in that: The steps include: S1. Patients undergo medical imaging examinations at local medical institutions, and imaging equipment scans the patients and generates digital images; S2, the image data is pre-processed and then uploaded to the cloud platform or the server of the telemedicine center through encrypted transmission; S3, the uploaded imaging data is stored and managed by the cloud platform, and indexed by metadata such as patient ID and imaging type; S4. Use deep learning and computer vision technology to automatically analyze image data, give preliminary diagnostic suggestions, and generate analysis reports; S5. Doctors access patients’ medical images and medical records through the remote consultation platform and use the video conferencing function to communicate with other experts or patients in real time; S6. Based on the consultation results, a diagnosis report is automatically generated and sent to the patient or referral hospital through the telemedicine platform. The patient can view the report through a mobile device.

6. A method for implementing remote medical image consultation according to claim 5, characterized in that: The encryption method in S2 adopts artificial intelligence encryption, specifically: S21. Use a neural network model to encrypt and decrypt communication data, use an autoencoder to learn the representation of the data, and encode it into a vector of the hidden layer; S22, using a generative adversarial network to generate fake data, and then mixing the original data with the fake data for encryption; S23, performing noise processing on the data to obtain noisy data, and performing dimension reduction on the noisy data through a DNN network to obtain a binary encrypted data string; S24, processing the noisy data through an encryption encoder to obtain output data of a network hidden layer; S25. Use quantum key distribution technology to distribute encryption keys through the quantum state between quantum bits to achieve secure transmission of information; S26. After the transmission is completed, the transmitted data is decrypted.

7. A method for implementing remote medical image consultation according to claim 6, characterized in that: The specific steps of using the autoencoder to encrypt communication data in S21 are as follows: S211. Train an autoencoder to learn a compressed representation of data. The autoencoder consists of two parts: an encoder and a decoder. The encoder encodes the original data into a low-dimensional representation, and the decoder decodes the low-dimensional representation into a reconstruction of the original data. During the training process, the goal of the autoencoder is to minimize the reconstruction error, that is, the difference between the input data and the reconstructed data. S212. After the autoencoder is trained, the encoder part is used to encrypt the data. This can be achieved by inputting the original data into the encoder and obtaining its compressed representation. Since the encoder learns a low-dimensional representation of the data, this representation can be regarded as encrypted data. S213. To decrypt the data, just input the encrypted low-dimensional representation into the decoder.

8. The method for implementing remote medical image consultation according to claim 6, characterized in that: The specific steps of using the adversarial network to encrypt communication data in S22 are as follows: S221. Generate encrypted data using the generator of GANs. This can be achieved by taking real data as input and training the generator to generate data. The generator is trained to generate synthetic data that is as similar to real data as possible, but in fact the data it generates is an abstract representation of the real data and can therefore be regarded as encrypted data. S222. The generated encrypted data can be transmitted to the recipient through a secure channel. Since these data are generated by the generator, they do not directly expose the content of the original data, thereby providing a certain degree of confidentiality.

9. A method for implementing remote medical image consultation according to claim 6, characterized in that: The quantum technology in S25 is specifically: using the principles of quantum mechanics to create and distribute encryption keys, which are used for encryption and decryption operations in traditional encrypted communications. In QKD, the communicating parties create shared random keys by sending and receiving quantum bits, which are then used to encrypt the transmitted data and use the randomness in the quantum system to generate random numbers. Since the measurement results in the quantum system are uncertain, this uncertainty can be used to generate true random numbers, which can be used as encryption keys. At the same time, quantum entanglement can be used to create secure encryption keys, because the measurement results of a quantum system will immediately affect other quantum systems entangled with it, thereby preventing the intervention of unauthorized eavesdroppers. Quantum state measurements are used to check whether the transmitted quantum bits are interfered with or eavesdropped. By performing appropriate quantum state measurements, the communicating parties can detect any attempts to eavesdrop or interfere with communications.

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