WO2020087792A1 - 人工智能的病种分析方法及装置、存储介质、计算机设备 - Google Patents

人工智能的病种分析方法及装置、存储介质、计算机设备 Download PDF

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WO2020087792A1
WO2020087792A1 PCT/CN2019/073293 CN2019073293W WO2020087792A1 WO 2020087792 A1 WO2020087792 A1 WO 2020087792A1 CN 2019073293 W CN2019073293 W CN 2019073293W WO 2020087792 A1 WO2020087792 A1 WO 2020087792A1
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analysis
disease
disease type
request
information
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PCT/CN2019/073293
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English (en)
French (fr)
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韦政勤
罗裕期
陈定一
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平安科技（深圳）有限公司
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    • 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
    • G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
    • G16H50/20—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F21/00—Security arrangements for protecting computers, components thereof, programs or data against unauthorised activity
    • G06F21/60—Protecting data
    • G06F21/602—Providing cryptographic facilities or services
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F21/00—Security arrangements for protecting computers, components thereof, programs or data against unauthorised activity
    • G06F21/60—Protecting data
    • G06F21/62—Protecting access to data via a platform, e.g. using keys or access control rules
    • G06F21/6218—Protecting access to data via a platform, e.g. using keys or access control rules to a system of files or objects, e.g. local or distributed file system or database
    • G06F21/6245—Protecting personal data, e.g. for financial or medical purposes
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F21/00—Security arrangements for protecting computers, components thereof, programs or data against unauthorised activity
    • G06F21/60—Protecting data
    • G06F21/64—Protecting data integrity, e.g. using checksums, certificates or signatures
    • 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
    • G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
    • G16H50/70—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for mining of medical data, e.g. analysing previous cases of other patients
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F2221/00—Indexing scheme relating to security arrangements for protecting computers, components thereof, programs or data against unauthorised activity
    • G06F2221/21—Indexing scheme relating to G06F21/00 and subgroups addressing additional information or applications relating to security arrangements for protecting computers, components thereof, programs or data against unauthorised activity
    • G06F2221/2133—Verifying human interaction, e.g., Captcha

Definitions

  • the present application relates to the field of artificial intelligence technology, and in particular to an artificial intelligence disease analysis method and device, storage medium, and computer equipment.
  • AI Artificial Intelligence
  • the existing AI disease analysis methods are basically closed systems. Specifically, a single AI disease analysis team (that is, AI analysis equipment) provides AI diseases for each AI user terminal (such as hospitals, outpatient clinics, and medical examination centers, etc.) kind of analysis results.
  • AI disease analysis team that is, AI analysis equipment
  • AI user terminal such as hospitals, outpatient clinics, and medical examination centers, etc.
  • this application provides an artificial intelligence disease analysis method and device, storage medium, and computer equipment.
  • the main purpose is to solve the current single closed AI disease analysis team.
  • the analysis of disease types is limited and cannot meet different AI. Analysis of the needs of various AI diseases of user terminals.
  • an artificial intelligence disease analysis method includes:
  • AI disease type analysis request carries disease type information and patient examination information corresponding to the disease type to be analyzed
  • a disease analysis device of artificial intelligence which includes:
  • the receiving unit is configured to receive an AI disease type analysis request sent by an AI user terminal and carrying disease type information and patient examination information corresponding to the disease type to be analyzed;
  • Query unit for querying AI analysis equipment corresponding to the disease type information to support analysis
  • a forwarding unit configured to forward the AI disease type analysis request to the AI analysis device, so that the AI analysis device performs AI disease type analysis according to the patient examination information to obtain an AI disease type analysis result;
  • a processing unit configured to receive the AI disease type analysis result sent by the AI analysis device, and perform integrated filtering processing on the AI disease type analysis result;
  • the forwarding unit is also used to forward the AI disease analysis result after the integrated filtering process to the AI user terminal.
  • a non-volatile readable storage medium on which computer-readable instructions are stored, which when executed by a processor implements the above-mentioned artificial intelligence disease analysis method.
  • a computer device including a non-volatile readable storage medium, a processor, and a computer readable stored on the non-volatile readable storage medium and operable on the processor Instructions, the processor implements the above artificial intelligence disease analysis method when executing the computer readable instructions.
  • the application provides an artificial intelligence disease analysis method and device, storage medium, and computer equipment. Compared with the current AI disease analysis method, this application is to compare multiple AI diseases The analysis team is integrated together. When receiving an AI disease analysis request carrying the disease type information and patient examination information corresponding to the disease to be analyzed, it will automatically query and match the AI analysis equipment that supports the analysis of the disease. An AI analysis device that meets the requirements analyzes the AI disease analysis request, and then performs integrated filtering processing on the analysis results of each AI analysis device, and sends the processing results to the AI user terminal.
  • each team has differences in the types of diseases analyzed, and the system is composed of multiple AI analysis equipment, that is, multiple AI disease analysis teams, it enhances the diversity and professionalism of system analysis and can solve all AI diseases
  • the types of diseases covered by the analysis team from receiving disease analysis requests from AI user terminals, to matching corresponding AI analysis equipment, and later integrated filtering processing of analysis results, to feedback of results, the entire process can be fully automated , Greatly improving the efficiency of disease analysis, and the final feedback result is obtained by integrating and filtering the analysis results of multiple AI disease analysis teams, and all AI results are combined and sent to the AI user terminal for the AI user terminal to choose and consider . In this way, some unprofessional analysis is filtered out, making the analysis results more scientific, greatly reducing analysis errors, and improving the efficiency of AI disease analysis.
  • FIG. 1 shows a schematic flowchart of an artificial intelligence disease analysis method provided by an embodiment of the present application
  • FIG. 2 shows a schematic flowchart of another artificial intelligence disease analysis method provided by an embodiment of the present application
  • FIG. 3 shows a schematic diagram of an example of a disease analysis system provided by an embodiment of the present application
  • FIG. 4 shows a schematic structural diagram of an artificial disease analysis device provided by an embodiment of the present application.
  • FIG. 5 shows a schematic structural diagram of another artificial intelligence disease analysis device provided by an embodiment of the present application.
  • this embodiment provides an artificial intelligence disease analysis method, as shown in the figure As shown in 1, the method includes:
  • the AI user terminal is a terminal configured on the side of disease detection institutions such as hospitals, outpatient clinics, and medical examination centers, that is, consumers who need to perform disease analysis;
  • the AI disease analysis request is an analysis instruction issued by the AI user terminal to the AI disease analysis device It is hoped that the diagnosis result of the disease can be obtained based on the judgment of the AI disease analysis equipment.
  • the AI disease analysis request may carry the type of disease that requires AI disease analysis, the type of inspection device, the physical examination site, the AI analysis device selected preferentially, and patient information, diagnostic information, and image information.
  • an AI disease analysis request sent by a medical examination center is received, where the AI disease analysis analysis request can be learned that the disease type information corresponding to the disease type is: blood disease, and the patient inspection information is: blood sampling test report , And can get the patient's personal basic information.
  • the AI analysis equipment is a device or equipment for AI disease analysis, that is, equivalent to the analysis equipment with the analysis ability of the AI disease team, specifically providing various disease diagnosis services for AI user terminals, a single AI disease analysis equipment
  • the types of diseases that can be supported are limited. For example, some devices only support lung nodule screening, while some devices only support sugar net screening.
  • each AI disease analysis device can be summarized in advance according to the supported disease type information.
  • the disease type information carried in the AI disease type analysis request is surgery, all AI analysis devices that can analyze and diagnose surgical diseases can be queried among the numerous AI analysis devices.
  • the AI analysis device performs AI disease type analysis according to the patient examination information to obtain the AI disease type analysis result.
  • the AI analysis device will call different AI algorithms and models according to the requested disease type and related image files (such as body examination parts, etc.) to calculate the corresponding AI disease analysis results.
  • an AI disease analysis request sent by a medical examination center is received, where the AI disease analysis analysis request can be learned that the disease type information corresponding to the disease type is: blood disease, and the patient inspection information is: blood sampling test report . Forward this disease type analysis request to all AI analysis devices that can analyze and diagnose blood diseases. Different AI analysis devices will diagnose the corresponding AI disease type analysis results according to their own judgment.
  • the integrated filtering process is to first filter out some non-compliant AI disease analysis results (such as garbled, incomplete analysis result data is filtered), and merge the remaining compliant AI disease analysis results together to prepare Issued to the AI user terminal for consideration by the AI user terminal.
  • the obtained AI disease analysis result after integrated filtering processing is sent to the AI user terminal in the form of a report list, and then the AI user terminal can obtain the analysis result obtained by each AI disease analysis device.
  • the AI user terminal can mark the AI disease analysis equipment corresponding to each result (such as a specific team, etc.), and you can also mark the number of cases analyzed by each AI disease analysis equipment and the accuracy rate information.
  • the method for analyzing disease types of artificial intelligence when receiving an AI disease analysis request sent by an artificial intelligence AI user terminal, it can automatically query and match AI analysis devices that support the analysis of the disease type, and multiple The AI analysis equipment analyzes the AI disease analysis request, then integrates and filters the analysis results of each AI analysis equipment, and finally sends all the results to the AI user terminal.
  • multiple AI disease analysis equipment is used for analysis The method makes the analysis result more scientific, greatly reduces the analysis error, and also improves the efficiency of AI disease analysis.
  • the method includes :
  • an AI disease analysis request sent from a hospital is received, where the AI disease analysis request can be learned that the disease type information corresponding to the disease type is: brain disease, and the patient examination information is: brain CT, and Carrying CT image results.
  • security verification mainly consists of sending a verification code to the registered mobile phone number corresponding to the logged-in account, and verifying by the user inputting the verification code sent, to determine whether the regular disease detection agency that has been registered on the platform logs in by itself, thereby preventing others from being logged in Illegal misappropriation; request permission verification is mainly to determine whether the AI user terminal has the right to use the system to perform AI request analysis.
  • an AI disease analysis request sent by a hospital After receiving an AI disease analysis request sent by a hospital, first send a SMS verification code to the mobile phone number reserved by the hospital during registration to perform security verification on the AI user terminal, if the verification code entered by the user is correct, It is judged that it is operated independently by the hospital and passes the security verification; if the verification code entered is inconsistent with the verification code of the short message sent, it is deemed to have failed the security verification. If the security verification is passed, the request authorization verification of the AI disease analysis request is performed. This is to prevent the illegal use of the AI user terminal account by others, and to verify whether the hospital has opened the AI disease analysis service on the platform .
  • a request identifier corresponding to the AI user terminal is generated, and an AI analysis device that supports analysis corresponding to the type information of the disease is queried.
  • the request ID is a request ID.
  • the request ID is the digital account information that the system automatically generates randomly and meets certain rules. Each ID corresponds to query different information.
  • the AI user terminal hospital a has passed the security verification and has the request authority for the AI disease analysis request, the patient information such as the type of disease carried in the initiated AI disease analysis request, the type of examination equipment, and the location of the physical examination, etc., Query the AI analysis equipment that supports the analysis of AI disease type analysis requests.
  • the AI user terminal in order for the AI user terminal to subsequently query the AI disease analysis result and the information carried in the AI disease analysis request from the blockchain network platform through the request identifier.
  • a randomly generated request identification ID is sent to an AI user terminal. If the AI analysis device analyzes the AI disease analysis result, the AI disease analysis result and the AI analysis device information can be saved in the blockchain network platform The subsequent AI user terminal can use this request ID to retrieve the corresponding AI disease analysis result information from the blockchain network platform and the corresponding AI disease analysis device information obtained by the corresponding analysis.
  • this scheme is provided with scoring rules.
  • the score information is the basis for measuring the professionalism of the AI analysis equipment. The higher the score, the higher the diagnosis rate of the AI analysis equipment for disease diagnosis. The professional technology is strong. It is also more popular with AI user terminals. The higher the score, the AI analysis equipment. The priority is higher when the AI user terminal initiates the AI request.
  • the score is determined by the quality of the analysis results of the AI user terminal's disease analysis by the AI analysis device. When the AI disease analysis result information is sent to the AI user terminal, the corresponding score of the AI analysis device will also be noted, so that the AI user terminal Reading AI disease analysis results serves as a reference.
  • the score information of the AI analysis device can be automatically evaluated by the system.
  • the evaluation method may specifically include: calculating the similarity between the diagnosis information and the received AI disease analysis result; querying the first score corresponding to the similarity; updating the corresponding to the received AI disease analysis result according to the first score AI analysis equipment score information.
  • the system in order to promote the benign competition of AI analysis equipment, can automatically score different AI analysis equipment.
  • the rule for judging the first score according to the similarity can be set independently in advance according to the actual situation.
  • the scoring rule that can set the score is: set the score to a perfect score of 5 when the similarity is greater than 90%, when the similarity Score greater than 70% and less than 90% is 4 points. Score is 3 when the similarity is greater than 50% and less than 70%. Score is 2 when the similarity is greater than 30% and less than 50%. When the similarity is greater than 10 % And less than 30%, the score is 1 point, and the set score below 10% is 0 point.
  • Update the score information of the AI analysis device corresponding to the received AI disease analysis result according to the score where the update scheme can be selected or formulated independently, and the score information can be accumulated into the existing score of the AI analysis device, and the total score is used as The final score of the AI analysis device; it is also possible to accumulate the score information in all the scores and obtain the average value, and use the average value as the final score of the AI analysis device.
  • the score of the AI analysis device a is 4 points. If the total score is selected as the final score, the 4 points are added to the previous total score and the final score obtained is updated to the AI
  • the score information of the analysis device a serves as the existing score of the AI analysis device a. If the average score is selected as the final score of the AI analysis device, the average value of the currently accumulated scores (including this 4 points) is calculated as the updated final score.
  • the scoring information of the AI analysis device can also be evaluated by the AI user terminal, and the evaluation method may specifically include: receiving a second score sent by the AI user terminal to the AI disease analysis result received by the AI user terminal; according to the second score Update the score information of the AI analysis device corresponding to the AI disease analysis result received by the AI user terminal.
  • the AI user terminal in order to obtain better feedback information of the AI user terminal, can score the AI analysis after obtaining the AI analysis result, and the score is mainly based on the doctor's diagnosis result and the score evaluation rules It can be set according to your needs, for example, you can set a single score to a full five-point system or a ten-point system or a 100-point system.
  • Update the score information of the AI analysis device corresponding to the received AI disease analysis result according to the score where the update scheme can be selected or formulated independently, and the score information can be accumulated into the existing score of the AI analysis device, and the total score is used as The final score of the AI analysis device; it is also possible to accumulate the score information in all the scores and obtain the average value, and use the average value as the final score of the AI analysis device.
  • a five-point full-point evaluation rule is selected, and the score of the AI analysis device b is determined to be 5 points. If the total score is selected as the final score, 5 points are added to the previous total score , And update the obtained final score to the score information of the AI analysis device a; if you choose to use the average score as the final score of the AI analysis device, the average value of each accumulated score (including this 5 points) is calculated, As the final score after the update.
  • the AI disease analysis request to the AI analysis device whose score is greater than a preset score threshold.
  • the AI analysis device will call different AI algorithms and models according to the information carried in the platform request, and then obtain AI disease analysis results. For example, a model is trained in advance based on the diagnosis medical records of different patients and the corresponding patient examination information of the patient, wherein each patient examination information can determine the corresponding disease analysis result according to the corresponding diagnosis medical record, and then the patient examination information of the patient to be analyzed Input into this model, and based on big data analysis, get the AI disease analysis results of the patient to be analyzed.
  • the preset scoring threshold can be set in advance according to actual business needs, used to judge whether the AI analysis device reaches the standard score threshold, and the AI analysis device greater than the threshold indicates that it can participate in the analysis of AI diseases sent by the AI user terminal,
  • the threshold size can be formulated or modified according to the set scoring rules and the actual situation. Therefore, when selecting a suitable AI analysis device, if there are multiple candidate AI analysis devices, the scores of these candidate AI analysis devices will be obtained from the AI scoring system, and then ranked according to the AI analysis device's score from high to low, AI analysis equipment with a score greater than a certain threshold is preferentially selected.
  • the scoring rule is set as follows: a single score full score system is 5 points, and the final score is presented as an average score, that is, the highest score is 5 points and the lowest is 0 points. If the set score threshold is 3 points, the AI disease analysis request will be forwarded to the AI analysis device with a score greater than 3 points. The AI analysis device performs AI disease analysis based on the patient examination information in the AI disease analysis request, and then obtains Analysis results of the respective AI diseases.
  • the integrated filtering processing of the AI disease analysis results may specifically include: according to the time point at which the AI disease analysis request is forwarded and the time point at which the AI disease analysis result is received, the statistics of each AI analysis device returns AI disease analysis The response time of the results; merge the analysis results of the AI diseases whose response time is less than the preset time threshold.
  • the response time of the AI analysis device should be set to be less than the preset time threshold. For example, if the preset duration threshold is set to 30 minutes, the timing starts from the time point when the AI disease analysis request is forwarded, and the timing ends at 30 minutes, and the AI disease analysis results received within the timing period are retained and Merge and filter out AI disease analysis results received outside the time period.
  • step 207 after all the analysis results of the disease types generated within a preset time threshold of 30 minutes are obtained, all the analysis results of the disease types are packaged, combined and forwarded to the AI user terminal.
  • an AI disease analysis request of an AI user terminal may be configured according to the type of disease and sent to a corresponding AI analysis device or multiple AI analysis In the device (provided that the AI analysis device supports the disease type), that is, different AI user terminals are associated with one or more AI analysis devices (this refers to the case where the relationship is fixed in advance).
  • the AI analysis device corresponding to the AI user terminal that sent the request can be directly queried, and then the corresponding request is forwarded. In this way, the processing efficiency of the AI disease analysis request can be accelerated.
  • the system will treat all the AI diseases of the configured AI analysis devices.
  • the analysis results are integrated.
  • the AI user terminal passes the security verification and has the request authority for the AI disease analysis request, it will automatically query the AI analysis device corresponding to the AI user terminal, and the AI analysis device that is selected first will analyze the AI disease analysis request . Furthermore, it can meet the needs of AI user terminals.
  • the AI user terminal hospital a has a configuration relationship with the AI analysis device, and the associated AI analysis device is set to b
  • the AI user terminal a when the AI user terminal a sends an AI disease analysis request, it will automatically send the AI disease analysis request to the AI
  • AI disease analysis request For analysis of AI diseases in analysis equipment b, there is no need to filter and filter AI analysis equipment; when hospital a is equipped with multiple AI analysis equipment, if they are AI analysis equipment c, AI analysis equipment d, and AI analysis equipment e, respectively, when When the AI user terminal a issues an AI disease analysis request, it automatically sends the AI disease analysis request to the AI analysis device c, AI analysis device d, and AI analysis device e for AI disease analysis, and the AI user terminal sets which priority is used in advance.
  • the AI analysis device or the AI analysis device with a high score is preferentially selected according to the score, the set priority or the corresponding score of the AI user terminal is obtained, and the analysis result of the AI analysis device with the highest priority is sent to the AI user terminal.
  • the priority is specified, it will wait for the analysis results of AI analysis device c, AI analysis device d, and AI analysis device e to return, and then merge all the AI results to AI user terminal a AI and marked each team score for AI user terminal selection considerations.
  • the blockchain network platform can ensure that the relevant information submitted by the AI user terminal and the analysis results of the AI analysis device can be effectively protected from being modified at will, and the security of information storage is improved.
  • the platform sends patient information, diagnostic information, and image information in the AI disease analysis request to the platform's blockchain network for storage and sharing.
  • the platform blockchain The network is responsible for encrypting information and preventing tampering; for AI results returned by the AI team, the platform will also send to the blockchain network for storage and sharing.
  • the blockchain network platform encrypts and stores the information carried in the AI disease analysis request and the AI disease analysis results.
  • the information carried in the AI disease analysis request and the AI disease analysis results are sent to the blockchain network platform for storage.
  • FIG. 3 it is composed of an AI user terminal (i.e., AI consumer), a back-end (BackForForFrontends, BFF) serving the front end, an AI module, an AI analysis device (i.e., AI provider), and a cloud server.
  • AI user terminal i.e., AI consumer
  • BFF BackForForFrontends
  • AI module i.e., AI module
  • AI analysis device i.e., AI provider
  • cloud server i.e., a cloud server
  • AI user terminals mainly refer to hospitals, outpatient clinics, medical examination centers, or medical system integrators, etc .
  • BFF mainly conducts user access through the server's access key (Access Key ID, AK) and secret key (Secret Access Key, SK) Security verification
  • AI module is used to verify request authority, generate request ID, request analysis of AI analysis equipment, provide uniform image identifier (Uniform Resource Identifier, URI) for AI analysis equipment, and save AI disease feedback from AI analysis equipment Kind of analysis results.
  • the main function of Ping An Cloud is to store the synchronization file corresponding to the picture URL for the AI analysis device.
  • the hospital can provide the platform with the interface provided by the platform.
  • the BFF module must first perform security verification after receiving the AI disease analysis request sent by the AI user terminal, and sign the request using AK and SK.
  • the AI disease analysis request will be returned to the AI user terminal; if it passes the AK / SK verification, the AI disease analysis request will be sent to the AI module, and the AI module will first verify the requested authority. The request is automatically returned to the AI user terminal. If the verification is successful, the AI module generates a request ID and sends the request ID to the AI user terminal. At the same time, query the AI analysis equipment corresponding to the type of disease information to support analysis. The AI analysis device feeds back the query results. The AI analysis device corresponding to the AI disease analysis request analysis will request to download the image information of the AI disease analysis request. Based on the URI of the returned image, the image is downloaded and analyzed on the cloud server.
  • the AI disease analysis results are sent to the AI module, and the AI module further integrates and filters the AI disease analysis results. Forward the analysis results of the AI disease after the integrated filtering process to the AI user terminal. Subsequent AI user terminals can use the generated request ID to query the information in the AI disease analysis request and the AI analysis result on the blockchain network of the AI module.
  • the above disease analysis methods can solve all the types of diseases covered by the AI disease analysis team; from receiving the disease analysis request of the AI user terminal, to matching the corresponding AI analysis equipment, and the later integrated filtering processing of the analysis results , And then to the feedback of the results, the whole process can be fully automated, greatly improving the efficiency of disease analysis, and the final feedback results are obtained by integrating and filtering the analysis results of multiple AI disease analysis teams, which is to a large extent Filter out some unprofessional and untimely analysis, make the analysis results more scientific, greatly reduce the analysis error, and also improve the reliability of AI disease analysis. And combined with the blockchain technology, it ensures that the relevant information submitted by the AI user terminal and the analysis results of the AI analysis equipment cannot be effectively modified, and the security and reliability of the system are enhanced.
  • the present application provides an artificial intelligence disease analysis device.
  • the device includes: a receiving unit 41, a query unit 42, and a forwarding unit 43 ⁇ processing unit 44.
  • the receiving unit 41 may be used to receive an AI disease analysis request sent by an artificial intelligence AI user terminal and carrying disease type information and patient examination information corresponding to the disease to be analyzed;
  • the query unit 42 can be used to query AI analysis equipment corresponding to the type of disease information to support analysis;
  • the forwarding unit 43 can be used to forward the AI disease type analysis request to the AI analysis device, so that the AI analysis device performs AI disease type analysis according to the patient examination information to obtain the AI disease type analysis result;
  • the processing unit 44 may be used to receive the AI disease analysis results sent by the AI analysis device, and perform integrated filtering processing on the AI disease analysis results;
  • the forwarding unit 43 can also be used to forward the AI disease analysis result after the integrated filtering process to the AI user terminal.
  • the forwarding unit 43 can also be used to obtain the scoring information of the AI analysis device; according to the scoring information, forward the AI disease analysis request to the AI analysis device with a score greater than a preset scoring threshold.
  • the forwarding unit 43 can also be used to calculate the similarity between the diagnostic information and the received AI disease analysis results; the query corresponds to the similarity Based on the first score; update the score information of the AI analysis device corresponding to the received AI disease analysis result.
  • the forwarding unit 43 can also be used to receive the second score sent by the AI user terminal to the AI disease analysis result received by the AI user terminal; update the AI corresponding to the analysis result received by the AI user terminal according to the second score Analyze the score information of the device.
  • the device further includes: a sending unit 45.
  • the sending unit 45 may be used to send to the blockchain network platform the disease type information corresponding to the disease to be analyzed carried in the AI disease type analysis request and the patient examination information and the AI filter type analysis result after the integrated filtering process. Furthermore, the blockchain network platform encrypts and stores the information carried in the AI disease analysis request and the AI disease analysis results.
  • the query unit 42 can also be specifically used to sequentially perform security verification on the AI user terminal and request permission verification on the AI disease analysis request; when the AI user terminal passes the security verification and has the AI disease analysis request
  • the AI user terminal passes the security verification and has the AI disease analysis request
  • requesting authority When requesting authority, generate a request identifier corresponding to the AI user terminal, and query the AI analysis device corresponding to the type of disease information to support analysis; send the request identifier to the AI user terminal, so that the AI user terminal subsequently uses the request identifier from the blockchain network
  • the platform can query the results of AI disease analysis after integration and filtering.
  • the processing unit 44 may be further specifically configured to count the response time of each AI analysis device to return the AI disease analysis result according to the time point at which the AI disease analysis request is forwarded and the time point at which the AI disease analysis result is received; The AI disease analysis results of the preset time threshold are combined.
  • this embodiment also provides a non-volatile readable storage medium on which computer-readable instructions are stored, and when the readable instructions are executed by the processor.
  • the above-mentioned artificial intelligence disease analysis method shown in FIGS. 1 to 2 is realized.
  • the technical solution of the present application can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (can be a CD-ROM, U disk, mobile hard disk, etc.), including several The instructions are used to enable a computer device (which may be a personal computer, a server, or a network device, etc.) to execute the methods described in various implementation scenarios of the present application.
  • a computer device which may be a personal computer, a server, or a network device, etc.
  • this embodiment also provides a computer device including non-volatile Readable storage medium and processor; non-volatile readable storage medium for storing computer readable instructions; processor for executing computer readable instructions to implement the artificial intelligence shown in FIGS. 1 to 2 described above Disease analysis method.
  • the computer device may further include a user interface, a network interface, a camera, a radio frequency (Radio Frequency) circuit, a sensor, an audio circuit, a WI-FI module, and so on.
  • the user interface may include a display (Display), an input unit such as a keyboard, and the like, and the optional user interface may also include a USB interface, a card reader interface, and the like.
  • the network interface may optionally include a standard wired interface, a wireless interface (such as a WI-FI interface), and so on.
  • the computer device structure provided in this embodiment does not constitute a limitation on the physical device, and may include more or less components, or combine some components, or arrange different components.
  • the storage medium may also include an operating system and a network communication module.
  • An operating system is a computer-readable instruction that manages the hardware and software resources of the above-mentioned computer equipment, and supports the operation of information-processing computer-readable instructions and other software and / or computer-readable instructions.
  • the network communication module is used to realize communication between various components inside the storage medium and other hardware and software in the information processing entity device.
  • the present application can be implemented by means of software plus a necessary general hardware platform, or by hardware.
  • the automatic query matching supports the AI analysis equipment for analyzing the disease type, and multiple AI analysis equipments that meet the requirements analyze the AI disease type analysis request, and then The analysis results of all AI analysis equipment are integrated and filtered, and the processing results are sent to the AI user terminal, which solves the problem of the single type of AI disease analysis in the prior art.

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Abstract

一种人工智能的病种分析方法及装置、存储介质、计算机设备，该方法包括：接收AI用户终端发送的AI病种分析请求（101），AI病种分析请求中携带有待分析病种对应的病种类型信息和患者检查信息；查询与病种类型信息对应支持分析的AI分析设备（102）；将AI病种分析请求转发给AI分析设备（103），以使得AI分析设备根据患者检查信息进行AI病种分析，得到AI病种分析结果；接收AI分析设备发送的AI病种分析结果，并对AI病种分析结果进行整合过滤处理（104）；将整合过滤处理后的AI病种分析结果转发给AI用户终端（105）。

Description

人工智能的病种分析方法及装置、存储介质、计算机设备
本申请要求与2018年10月30日提交中国专利局、申请号为2018112804473、申请名称为“人工智能的病种分析方法及装置、存储介质、计算机设备”的中国专利申请的优先权，其全部内容通过引用结合在申请中。
技术领域
本申请涉及人工智能技术领域，尤其是涉及到一种人工智能的病种分析方法及装置、存储介质、计算机设备。
背景技术
人工智能(Artificial Intelligence，AI)里的“深度学习”算法，最擅长的就是“图像识别和分析”。早期能够识别出图像里的猫，也就是说，给一批具有病症的医学图像，通过学习，机器就知道这个图片是不是异常图片，是不是很有可能患了某种疾病。而很多医学检测，判断是否有某种疾病时候，很多都要依靠对医学影像来进行，这么一来，人工智能就有了用武之地。
目前现有AI病种分析方式基本上都是封闭的系统，具体由单一的AI病种分析团队(即AI分析设备)为每个AI用户终端(如医院、门诊、体检中心等)提供AI病种分析结果。
然而，在封闭的系统下，不同的AI病种分析团队之间没有联系，每个AI病种分析团队能够分析的病种类型十分有限，比如有的AI病种分析团队只支持肺结节筛查，而有的AI病种分析团队仅支持糖网筛查等。导致单一的AI病种分析团队不能满足不同AI用户终端的多种AI病种分析需求，进而造成现有的AI病种分析方式可支持分析的病种类型比较单一，从而不能提供精确的AI病种分析结果。
发明内容
有鉴于此，本申请提供了一种人工智能的病种分析方法及装置、存储介质、计算机设备，主要目的在于解决当下单一封闭的AI病种分析团队，分析病种类型有限，无法满足不同AI用户终端的多种AI病种分析需求的问题。
根据本申请的一个方面，提供了一种人工智能的病种分析方法，该方法包括：
接收AI用户终端发送的AI病种分析请求，所述AI病种分析请求中携带有待分析病种对应的病种类型信息和患者检查信息；
查询与所述病种类型信息对应支持分析的AI分析设备；
将所述AI病种分析请求转发给所述AI分析设备，以使得所述AI分析设备根据所述患者检查信息进行AI病种分析，得到AI病种分析结果；
接收所述AI分析设备发送的所述AI病种分析结果，并对所述AI病种分析结果进行整合过滤处理；
将整合过滤处理后的所述AI病种分析结果转发给所述AI用户终端。
根据本申请的另一个方面，提供了一种人工智能的病种分析装置，该装置包括：
接收单元，用于接收AI用户终端发送的携带有待分析病种对应的病种类型信息和患者检查信息的AI病种分析请求；
查询单元，用于查询与所述病种类型信息对应支持分析的AI分析设备；
转发单元，用于将所述AI病种分析请求转发给所述AI分析设备，以使得所述AI分析设备根据所述患者检查信息进行AI病种分析，得到AI病种分析结果；
处理单元，用于接收所述AI分析设备发送的所述AI病种分析结果，并对所述AI病种分析结果进行整合过滤处理；
转发单元，还用于将整合过滤处理后的所述AI病种分析结果转发给所述AI用户终端。
根据本申请的又一个方面，提供了一种非易失性可读存储介质，其上存储有计算机可读指令，所述可读指令被处理器执行时实现上述人工智能的病种分析方法。
根据本申请的再一个方面，提供了一种计算机设备，包括非易失性可读存储介质、处理器及存储在非易失性可读存储介质上并可在处理器上运行的计算机可读指令，所述处理器执行所述计算机可读指令时实现上述人工智能的病种分析方法。
借由上述技术方案，本申请提供的一种人工智能的病种分析方法及装置、存储介质、计算机设备，与目前现有AI病种分析的方式相比，本申请是将多个AI病种分析团队集成在一起，在接收到携带有待分析病种对应的病种类型信息和患者检查信息的AI病种分析请求时，会自动查询匹配支持分析该病种的AI分析设备，由一个或多个符合要求的AI分析设备对该AI病种分析请求进行分析，再对各个AI分析设备的分析结果进行整合过滤处理，将处理结果发送给AI用户终端。因每个团队针对分析的病种类型存在差异，而系统是由多个AI分析设备即多个AI病种分析团队构成，故增强了系统分析的多样性以及专业性，可解决所有AI病种分析团队涵盖的病种类型；从接收AI用户终端的病种分析请求，到匹配对应的AI分析设备，以及后期的对分析结果的整合过滤处理，再到结果的反馈，整个过程可完全自动化处理，大大提高了病种分析的效率，并且最终反馈结果是对多个AI病种分析团队的分析结果整合过滤得到的，将所有的AI结果，合并发给AI用户终端，供AI用户终端选择考量。这样在很大程度上滤除了一些不专业的分析，使分析结果更具有科学性，大大减少了分析误差，也提高了AI病种分析的效率。
上述说明仅是本申请技术方案的概述，为了能够更清楚了解本申请的技术手段，而可依照说明书的内容予以实施，并且为了让本申请的上述和其它目的、特征和优点能够更明显易懂，以下特举 本申请的具体实施方式。
附图说明
此处所说明的附图用来提供对本申请的进一步理解，构成本申请的一部分，本申请的示意性实施例及其说明用于解释本申请，并不构成对本地申请的不当限定。在附图中：
图1示出了本申请实施例提供的一种人工智能的病种分析方法的流程示意图；
图2示出了本申请实施例提供的另一种人工智能的病种分析方法的流程示意图；
图3示出了本申请实施例提供的一种病种分析系统的实例示意图；
图4示出了本申请实施例提供的一种人工智能的病种分析装置的结构示意图；
图5示出了本申请实施例提供的另一种人工智能的病种分析装置的结构示意图。
具体实施方式
下文中将参考附图并结合实施例来详细说明本申请。需要说明的是，在不冲突的情况下，本申请中的实施例及实施例中的特征可以相互结合。
针对目前现有AI病种分析的方式可支持分析的病种类型比较单一，从而不能提供精确的AI病种分析结果的问题，本实施例提供了一种人工智能的病种分析方法，如图1所示，该方法包括：
101、接收AI用户终端发送的AI病种分析请求。
其中，AI用户终端为医院、门诊、体检中心等疾病检测机构侧配置的终端，即需要进行病种分析的消费方；AI病种分析请求为AI用户终端向AI病种分析设备发出的分析指令，希望根据AI病种分析设备的判断，得到疾病的诊断结果。为了方便检测，AI病种分析请求中可携带需要AI病种分析的病种类型，检查设备类型，身体检查部位、优先选择的AI分析设备以及患者信息、诊断信息以及影像信息等。
例如，接收到体检中心发送来的AI病种分析请求，其中，通过AI病种分析请求可获知，该病种对应的病种类型信息为：血液疾病，患者检查信息为：血液的采样检查报告，以及可以获知到患者的个人基本信息。
102、查询与病种类型信息对应支持分析的AI分析设备。
其中，AI分析设备为用于AI病种分析的装置或设备，即相当于具备AI病种团队分析能力的分析设备，具体为AI用户终端提供各种病种诊断服务，单个AI病种分析设备能够支持的病种有限，比如有的设备只支持肺结节筛查，而有的设备仅支持糖网筛查等。而本实施例中，可以将各个AI病种分析设备按照所支持的病种类型信息进行事先汇总。
例如，AI病种分析请求中携带的病种类型信息为外科，则在众多AI分析设备中查询出所有可以分析诊断外科疾病的AI分析设备。
103、将AI病种分析请求转发给查询到的AI分析设备。
进一步的，以使得AI分析设备根据患者检查信息进行AI病种分析，得到AI病种分析结果。AI分析设备会根据请求的病种类型以及相关的影像文件(如身体检查部位等)去调用不同的AI算法和模型，计算得出相应的AI病种分析结果。
例如，接收到体检中心发送来的AI病种分析请求，其中，通过AI病种分析请求可获知，该病种对应的病种类型信息为：血液疾病，患者检查信息为：血液的采样检查报告。将此病种分析请求转发给所有可以分析诊断血液疾病的AI分析设备，不同的AI分析设备都会根据自身的判断，诊断得到相应的AI病种分析结果。
104、接收AI分析设备发送的AI病种分析结果，并对AI病种分析结果进行整合过滤处理。
其中，整合过滤处理为首先滤除一些不符合规定的AI病种分析结果(如将乱码、不完整的分析结果数据进行过滤)，并且将其余符合规定的AI病种分析结果合并在一起，准备发给AI用户终端，供AI用户终端选择考量。
例如，在接收到所有AI分析设备针对同一AI病种分析请求发来的AI病种分析结果后，首先将一些暂时未判断出结果的AI分析设备滤除掉，然后将其余的分析结果进行整合，具体可以整合成为列表形式的AI病种分析结果。
105、将整合过滤处理后的AI病种分析结果转发给AI用户终端。
例如，将得到的经过整合过滤处理的AI病种分析结果，以报告列表形式发送给AI用户终端，进而AI用户终端可以获得各个AI病种分析设备分析得到的结果。在结果中可以标注每个结果对应的AI病种分析设备(如具体某个团队等)，并且还可以标注每个AI病种分析设备分析的案件数量，以及准确率的信息。
通过本实施例中的人工智能的病种分析方法，可以在接收到人工智能AI用户终端发送的AI病种分析请求时，自动查询匹配支持分析该病种的AI分析设备，由多个符合要求的AI分析设备对该AI病种分析请求进行分析，再对各个AI分析设备的分析结果进行整合过滤处理，最后将所有结果合并发送给AI用户终端，同时采用多个AI病种分析设备进行分析的方法，使分析结果更具有科学性，大大减少了分析误差，也提高了AI病种分析的效率。
进一步的，作为上述实施例具体实施方式的细化和扩展，为了完整说明本实施例中的具体实施过程，提供了另一种人工智能的病种分析方法，如图2所示，该方法包括：
201、接收AI用户终端发送的AI病种分析请求。
例如，接收到医院发送来的AI病种分析请求，其中，通过AI病种分析请求可获知，该病种对应的病种类型信息为：脑部疾病，患者检查信息为：脑部CT，并携带有CT影像结果。
202、对AI用户终端依次进行安全验证和AI病种分析请求的请求权限验证。
在具体的实施方式中，为了增强系统的安全性，设定在为AI用户终端提供病种分析前，需要进行安全验证以及请求权限验证。其中，安全验证，主要通过对所登陆账号对应的注册手机号发送验证码，通过用户输入发送的验证码来进行验证，判断是否由已在平台注册的正规疾病检测机构自身登陆，从而防止被他人非法盗用；请求权限验证主要是为了判断AI用户终端是否有具有使用本系统的进行AI请求分析的权限。
例如，接收到医院发送来的AI病种分析请求，首先向该医院在注册时预留的手机号发送短信验证码，对该AI用户终端进行安全验证，如用户输入的验证码是正确的，则判断是医院自主操作的，通过安全验证；如输入的验证码与发出的短信验证码不一致，则视为未通过安全验证。如通过了安全验证，则进行AI病种分析请求的请求权限验证，这样做的是为了防止AI用户终端的账号被他人非法盗用，以及验证该医院是否在平台开通了AI病种分析这一服务。
203、当AI用户终端通过安全验证、且具备AI病种分析请求的请求权限时，生成AI用户终端对应的请求标识，并查询与病种类型信息对应支持分析的AI分析设备。
其中，请求标识为请求ID，请求ID是系统自动随机生成且符合特定规则的数字账号信息，每个ID对应查询不同的信息。
例如，AI用户终端医院a已通过安全验证、且具备AI病种分析请求的请求权限，则根据发起的AI病种分析请求中携带的病种类型，检查设备类型，身体检查部位等患者信息，查询对应支持分析AI病种分析请求的AI分析设备。
204、将生成的请求标识发送给AI用户终端。
进一步的，以便于AI用户终端后续通过请求标识从区块链网络平台中查询AI病种分析结果以及AI病种分析请求中携带的信息。
例如，将随机生成的请求标识ID发送给AI用户终端，如果AI分析设备分析得到AI病种分析结果后，可以将该AI病种分析结果和该AI分析设备信息保存在区块链网络平台中，后续AI用户终端可通过这个请求ID从区块链网络平台中查取相应的AI病种分析结果信息以及对应分析得到该结果的AI病种分析设备信息。
205、获取查询到的AI分析设备的评分信息。
在具体的实施方式中，为了保证诊断质量，以及起到激励AI分析设备的作用，此方案中设有评分规则。评分信息是衡量AI分析设备专业性的依据，评分越高，则表示该AI分析设备的病种诊断确诊率高，专业技术性强，也较受AI用户终端的欢迎，评分越高的AI分析设备在AI用户终端发起AI请求的时候优先级越高。评分高低是由AI分析设备对AI用户终端病种分析结果的质量来决定的，在向 AI用户终端发送AI病种分析结果信息时也会备注AI分析设备对应的评分，这样可以在AI用户终端读取AI病种分析结果起到参考作用。
其中，AI分析设备的评分信息可由系统自动评定。评定方式具体可包括：计算诊断信息和接收到的AI病种分析结果之间的相似度；查询与相似度对应的第一得分；根据第一得分更新与接收到的AI病种分析结果对应的AI分析设备的评分信息。
在具体的实施方式中，为了促进AI分析设备的良性竞争，系统可自动对不同AI分析设备进行评分操作。其中，根据相似度来判断第一得分的规则可根据实际情况提前自主设定，例如，可设定得分的评分规则为：设定当相似度大于90％时得分为满分5分，当相似度大于70％且小于90％时得分为4分，当相似度大于50％且小于70％时得分为3分，当相似度大于30％且小于50％时得分为2分，当相似度大于10％且小于30％时得分为1分，低于10％的设定得分为0分。根据得分更新与接收到的AI病种分析结果对应的AI分析设备的评分信息，其中，更新方案可自主选择或制定，可将得分信息累加到AI分析设备现有的分数中，将总分作为AI分析设备的最终评分；也可将得分信息累加到所有分数中，并求得平均值，将平均值作为AI分析设备的最终评分。
例如，根据设定的评分规则，确定AI分析设备a的评分为4分，若选择将总分作为最终评分，则将4分加到以往的总评分中，并将得到的最终评分更新到AI分析设备a的评分信息中，作为AI分析设备a的现有评分。若选择将平均分作为AI分析设备的最终评分，则计算出当前累计的各个评分值(包括这个4分)的平均值，作为更新后的最终评分。
相应的，AI分析设备的评分信息还可由AI用户终端来评定，评定方式具体可包括：接收AI用户终端发送的对AI用户终端接收到的AI病种分析结果的第二得分；根据第二得分更新与AI用户终端接收到的AI病种分析结果对应的AI分析设备的评分信息。
在具体的实施方式中，为了更好的得到AI用户终端的反馈信息，AI用户终端在获取到AI分析结果后，可以对AI分析进行评分，评分的依据主要是医生的诊断结果，分数评定规则可根据自身需求来设定，例如可设定单次评分为满分五分制或十分制或百分制等。根据得分更新与接收到的AI病种分析结果对应的AI分析设备的评分信息，其中，更新方案可自主选择或制定，可将得分信息累加到AI分析设备现有的分数中，将总分作为AI分析设备的最终评分；也可将得分信息累加到所有分数中，并求得平均值，将平均值作为AI分析设备的最终评分。
例如，根据设定的评分规则，选择的是满分五分制评判规则，确定AI分析设备b的评分为5分，若选择将总分作为最终评分，则将5分加到以往的总评分中，并将得到的最终评分更新到AI分析设备a的评分信息中；若选择将平均分作为AI分析设备的最终评分，则计算出当前累计的各个评分值(包括这个5分)的平均值，作为更新后的最终评分。
206、根据评分信息，将AI病种分析请求转发给评分大于预设评分阈值的AI分析设备。
进一步的，AI分析设备会根据平台请求中携带的信息去调用不同的AI算法和模型，进而得到AI病种分析结果。例如，预先根据不同病人的诊断病历，以及该病人相应的患者检查信息训练模型，其中每个患者检查信息可根据对应的诊断病历确定相应的病种分析结果，然后将待分析患者的患者检查信息输入到该模型中，基于大数据分析，得到该待分析患者的AI病种分析结果。
其中，预设评分阈值可为根据实际业务需求提前设定的，用来评判AI分析设备是否达到标准的分数阈值，大于阈值的AI分析设备说明可参与AI用户终端发来的AI病种分析，阈值大小可根据设定的评分规则以及实际情况进行制定或修改。因此在选择适合的AI分析设备时，如存在多个候选AI分析设备的情况，会从AI评分系统中获取这些候选AI分析设备的评分，然后会根据AI分析设备的评分从高到低排序，优先选择评分大于一定阈值的AI分析设备。例如，若设定的评分规则为：单次得分满分制为5分，最终得分以平均分的方式来呈现，即最终得分最高为5分，最低为0分。设定的评分阈值为3分，则会将AI病种分析请求转发给评分大于3分的AI分析设备，AI分析设备根据AI病种分析请求中的患者检查信息进行AI病种分析，进而得到各自的AI病种分析结果。
207、接收AI分析设备发送的AI病种分析结果，并对AI病种分析结果进行整合过滤处理。
其中，对AI病种分析结果进行整合过滤处理，具体可包括：按照转发AI病种分析请求的时间点和接收到AI病种分析结果的时间点，统计每个AI分析设备返回AI病种分析结果的响应时长；将响应时长小于预设时长阈值的AI病种分析结果进行合并处理。
在具体的实施方式中，为了保证反馈的效率，设定AI分析设备的响应时长应小于预设时长阈值。例如，若设定预设时长阈值为30分钟，则从转发AI病种分析请求的时间点开始计时，到30分钟时计时结束，将在计时时间段内接收到的AI病种分析结果保留并合并，将在计时时间段之外接收到的AI病种分析结果滤除。
208、将整合过滤处理后的AI病种分析结果转发给AI用户终端。
例如，基于步骤207的实例，得到所有在预设时长阈值30分钟内产生的病种分析结果后，将所有病种分析结果打包合并统一转发给AI用户终端。
在具体的实施方式中，优选的，为了满足AI用户终端的特定需求，可以根据病种类型配置某个AI用户终端的AI病种分析请求发送到对应的某个AI分析设备或者多个AI分析设备中(前提是该AI分析设备支持该病种)，即将不同的AI用户终端和一个或多个AI分析设备进行关联(这里指的是预先固定关系的情况)。这样后续接收到请求后，可直接查询与发送请求的AI用户终端对应的AI分析设备，进而转发相应请求，通过这种方式可以加快AI病种分析请求的处理效率。
对于有多个AI分析设备的情况，可以指定优先级优先使用哪个AI分析设备或者根据评分优先选 择评分高的AI团队，如果没有指定，那么系统会将所有已配置的AI分析设备的AI病种分析结果进行整合处理。在AI用户终端通过安全验证、且具备AI病种分析请求的请求权限时，会自动查询与AI用户终端对应关联的AI分析设备，由优先选择的AI分析设备进行对AI病种分析请求的分析。进而可以满足AI用户终端的需求。
例如，若AI用户终端医院a对AI分析设备存在配置关系，设定关联的AI分析设备为b，当AI用户终端a发出AI病种分析请求时，会自动将AI病种分析请求发送到AI分析设备b中进行AI病种分析，无需再进行AI分析设备的筛选过滤；当医院a配置多个AI分析设备时，若分别为AI分析设备c、AI分析设备d、AI分析设备e，当AI用户终端a发出AI病种分析请求时，自动将AI病种分析请求发送到AI分析设备c、AI分析设备d、AI分析设备e进行AI病种分析，在AI用户终端预先设置优先使用哪个AI分析设备或者根据评分优先选择评分高的AI分析设备时，则获取设置的优先级或AI用户终端各自对应的评分，将优先级最高的AI分析设备的分析结果发送给AI用户终端，如果没有指定优先级时，则会等待AI分析设备c、AI分析设备d、AI分析设备e的分析结果返回后，将所有的AI结果，合并发送给AI用户终端a，并标注每个AI团队的评分，供AI用户终端选择考量。
209、将AI病种分析请求中携带的待分析病种对应的病种类型信息和患者检查信息和整合过滤处理后的AI病种分析结果发送到区块链网络平台。
其中，区块链网络平台可确保对AI用户终端提交的相关信息和AI分析设备的分析结果进行有效的保护不能被随意修改，提高信息存储的安全性。当AI用户终端发起AI病种分析请求时，平台会将AI病种分析请求中的患者信息、诊断信息以及影像信息等私密信息发送到平台的区块链网络进行存储和共享，平台区块链网络负责对信息进行加密和防止篡改；对于AI团队返回的AI结果，平台也会发送到区块链网络进行存储和共享。进一步的，以使得区块链网络平台对AI病种分析请求中携带的信息和AI病种分析结果进行加密存储。
例如，在得到整合过滤处理后的AI病种分析结果后，为了方便存储以及提高安全性，将AI病种分析请求中携带的信息和AI病种分析结果发送到区块链网络平台进行存储。
为了进一步说明本实施例提供的方法，给出如下实例，但不限于此。
例如，如图3所示，由AI用户终端(即AI消费方)、服务于前端的后端(Back For Frontends，BFF)、AI模块、AI分析设备(即AI提供方)、云端服务器构成。其中AI用户终端主要是指医院、门诊、体检中心或者医疗系统集成商等；BFF主要是通过服务器的访问密钥(Access Key ID，AK)和秘密密钥(Secret Access Key，SK)对用户进行安全验证；AI模块用于进行请求权限验证、生成请求ID、对AI分析设备请求解析，为AI分析设备提供图片统一资源标识符(Uniform Resource Identifier，URI)、以及保存AI分析设备反馈的AI病种分析结果。平安云主要作用是为AI分析设备存储图片URL对应的同 步文件。例如，患者在医院进行一次检查后会生成对应的影像信息，比如X光胸片、肺部CT的DICOM文件或者OCT眼底照片等，在获取到这些影像后，医院可以通过平台提供的接口向平台发起AI病种分析请求，BFF模块在接收AI用户终端发送的AI病种分析请求后，先要进行安全验证，使用AK、SK对请求进行签名。
如果安全验证未通过，则将AI病种分析请求退回AI用户终端；若通过AK/SK验证，则将AI病种分析请求发送到AI模块，AI模块首先进行请求权限验证，在验证失败时将请求自动给返回给AI用户终端，若验证成功，则AI模块生成请求ID，并将请求ID发送给AI用户终端。同时，查询与病种类型信息对应支持分析的AI分析设备。AI分析设备反馈查询结果，与AI病种分析请求分析对应的AI分析设备会请求下载AI病种分析请求的图片信息，根据返回图片的URI，在云服务器中下载图片并进行分析，分析出的AI病种分析结果发送到AI模块，AI模块进而会对AI病种分析结果进行整合过滤处理。将整合过滤处理后的AI病种分析结果转发给AI用户终端。后续AI用户终端可利用生成的请求ID在AI模块的区块链网络进行AI病种分析请求中信息以及AI分析结果的查询。
对于上述病种分析方法，可解决所有AI病种分析团队涵盖的病种类型；从接收AI用户终端的病种分析请求，到匹配对应的AI分析设备，以及后期的对分析结果的整合过滤处理，再到结果的反馈，整个过程可完全自动化处理，大大提高了病种分析的效率，并且最终反馈结果是对多个AI病种分析团队的分析结果整合过滤得到的，这样在很大程度上滤除了一些不专业以及不及时的分析，使分析结果更具科学性，大大减少了分析误差，也提高了AI病种分析的可靠性。并且结合了区块链技术，确保对AI用户终端提交的相关信息和AI分析设备的分析结果进行有效的保护不能被随意修改，增强了系统的安全性以及可靠性。
进一步的，作为图1和图2所示方法的具体实现，本申请提供了一种人工智能的病种分析装置，如图4所示，该装置包括：接收单元41、查询单元42、转发单元43、处理单元44。
接收单元41，可用于接收人工智能AI用户终端发送的携带有待分析病种对应的病种类型信息和患者检查信息的AI病种分析请求；
查询单元42，可用于查询与病种类型信息对应支持分析的AI分析设备；
转发单元43，可用于将AI病种分析请求转发给AI分析设备，以使得AI分析设备根据患者检查信息进行AI病种分析，得到AI病种分析结果；
处理单元44，可用于接收AI分析设备发送的AI病种分析结果，并对AI病种分析结果进行整合过滤处理；
转发单元43，还可用于将整合过滤处理后的AI病种分析结果转发给AI用户终端。
在具体的应用场景中，转发单元43，还可以用于获取AI分析设备的评分信息；根据评分信息， 将AI病种分析请求转发给评分大于预设评分阈值的AI分析设备。
在具体的应用场景中，为了后续AI用户终端提供更加精确的AI分析结果，转发单元43，还可用于计算诊断信息和接收到的AI病种分析结果之间的相似度；查询与相似度对应的第一得分；根据第一得分更新与接收到的AI病种分析结果对应的AI分析设备的评分信息。
相应的，转发单元43，还可用于接收AI用户终端发送的对AI用户终端接收到的AI病种分析结果的第二得分；根据第二得分更新与AI用户终端接收到的分析结果对应的AI分析设备的评分信息。
在具体的应用场景中，为了保证数据不丢失且为了避免泄露患者信息，如图5所示，本装置还包括：发送单元45。
发送单元45，可用于将AI病种分析请求中携带的待分析病种对应的病种类型信息和患者检查信息和整合过滤处理后的AI病种分析结果发送到区块链网络平台。进一步的，以使得区块链网络平台对AI病种分析请求中携带的信息和AI病种分析结果进行加密存储。
在具体的应用场景中，查询单元42，具体还可用于对AI用户终端依次进行安全验证和AI病种分析请求的请求权限验证；当AI用户终端通过安全验证、且具备AI病种分析请求的请求权限时，生成AI用户终端对应的请求标识，并查询与病种类型信息对应支持分析的AI分析设备；将请求标识发送给AI用户终端，以便AI用户终端后续通过请求标识从区块链网络平台中查询整合过滤处理后的AI病种分析结果。
处理单元44，具体还可用于按照转发AI病种分析请求的时间点和接收到AI病种分析结果的时间点，统计每个AI分析设备返回AI病种分析结果的响应时长；将响应时长小于预设时长阈值的AI病种分析结果进行合并处理。
需要说明的是，本实施例提供的一种人工智能的病种分析装置所涉及各功能单元的其他相应描述，可以参考图1至图2的对应描述，在此不再赘述。
基于上述如图1至图2所示方法，相应的，本实施例还提供了一种非易失性可读存储介质，其上存储有计算机可读指令，该可读指令被处理器执行时实现上述如图1至图2所示的人工智能的病种分析方法。
基于这样的理解，本申请的技术方案可以以软件产品的形式体现出来，该软件产品可以存储在一个非易失性存储介质(可以是CD-ROM，U盘，移动硬盘等)中，包括若干指令用以使得一台计算机设备(可以是个人计算机，服务器，或者网络设备等)执行本申请各个实施场景所述的方法。基于上述如图1至图2所示的方法和图4、图5所示的虚拟装置实施例，为了实现上述目的，本实施例还提供了一种计算机设备，该计算机设备包括非易失性可读存储介质和处理器；非易失性可读存储介质，用于存储计算机可读指令；处理器，用于执行计算机可读指令以实现上述如图1至图2所示的人工智 能的病种分析方法。
可选的，该计算机设备还可以包括用户接口、网络接口、摄像头、射频(Radio Frequency，RF)电路，传感器、音频电路、WI-FI模块等等。用户接口可以包括显示屏(Display)、输入单元比如键盘(Keyboard)等，可选用户接口还可以包括USB接口、读卡器接口等。网络接口可选的可以包括标准的有线接口、无线接口(如WI-FI接口)等。本领域技术人员可以理解，本实施例提供的一种计算机设备结构并不构成对该实体设备的限定，可以包括更多或更少的部件，或者组合某些部件，或者不同的部件布置。
存储介质中还可以包括操作系统、网络通信模块。操作系统是管理上述计算机设备硬件和软件资源的计算机可读指令，支持信息处理计算机可读指令以及其它软件和/或计算机可读指令的运行。网络通信模块用于实现存储介质内部各组件之间的通信，以及与信息处理实体设备中其它硬件和软件之间通信。
通过以上的实施方式的描述，本领域的技术人员可以清楚地了解到本申请可以借助软件加必要的通用硬件平台的方式来实现，也可以通过硬件实现。通过应用本申请的技术方案，与目前现有技术相比，自动查询匹配支持分析该病种的AI分析设备，由多个符合要求的AI分析设备对该AI病种分析请求进行分析，再对所有AI分析设备的分析结果进行整合过滤处理，将处理结果发送给AI用户终端，解决了现有技术中AI病种分析类型单一的问题。
本领域技术人员可以理解附图只是一个优选实施场景的示意图，附图中的模块或流程并不一定是实施本申请所必须的。本领域技术人员可以理解实施场景中的装置中的模块可以按照实施场景描述进行分布于实施场景的装置中，也可以进行相应变化位于不同于本实施场景的一个或多个装置中。上述实施场景的模块可以合并为一个模块，也可以进一步拆分成多个子模块。
上述本申请序号仅仅为了描述，不代表实施场景的优劣。以上公开的仅为本申请的几个具体实施场景，但是，本申请并非局限于此，任何本领域的技术人员能思之的变化都应落入本申请的保护范围。

Claims (20)

  1. 一种人工智能的病种分析方法，其特征在于，包括：
    接收人工智能AI用户终端发送的AI病种分析请求，所述AI病种分析请求中携带有待分析病种对应的病种类型信息和患者检查信息；
    查询与所述病种类型信息对应支持分析的AI分析设备；
    将所述AI病种分析请求转发给所述AI分析设备，以使得所述AI分析设备根据所述患者检查信息进行AI病种分析，得到AI病种分析结果；
    接收所述AI分析设备发送的所述AI病种分析结果，并对所述AI病种分析结果进行整合过滤处理；
    将整合过滤处理后的所述AI病种分析结果转发给所述AI用户终端。
  2. 根据权利要求1所述的方法，其特征在于，所述将所述AI病种分析请求转发给所述AI分析设备，具体包括：
    获取所述AI分析设备的评分信息；
    根据所述评分信息，将所述AI病种分析请求转发给评分大于预设评分阈值的所述AI分析设备。
  3. 根据权利要求2所述的方法，其特征在于，所述AI病种分析请求中还携带有所述待分析病种对应的诊断信息，所述方法还包括：
    计算所述诊断信息和接收到的所述AI病种分析结果之间的相似度；
    查询与所述相似度对应的第一得分；
    根据所述第一得分更新与所述接收到的所述AI病种分析结果对应的所述AI分析设备的评分信息。
  4. 根据权利要求2所述的方法，其特征在于，在所述将整合过滤处理后的所述AI病种分析结果转发给所述AI用户终端之后，所述方法还包括：
    接收所述AI用户终端发送的对所述AI用户终端接收到的所述AI病种分析结果的第二得分；
    根据所述第二得分更新与所述AI用户终端接收到的所述AI病种分析结果对应的所述AI分析设备的评分信息。
  5. 根据权利要求1所述的方法，其特征在于，所述方法还包括：
    将所述AI病种分析请求中携带的待分析病种对应的病种类型信息和患者检查信息和所 述整合过滤处理后的AI病种分析结果发送到区块链网络平台，以使得所述区块链网络平台对所述AI病种分析请求中携带的待分析病种对应的病种类型信息和患者检查信息和所述AI病种分析结果进行加密存储。
  6. 根据权利要求5所述的方法，其特征在于，所述查询与所述病种类型信息对应支持分析的AI分析设备之前，所述方法还包括：
    对所述AI用户终端依次进行安全验证和AI病种分析请求的请求权限验证；
    所述查询与所述病种类型信息对应支持分析的AI分析设备，具体包括：
    当所述AI用户终端通过安全验证、且具备AI病种分析请求的请求权限时，生成所述AI用户终端对应的请求标识，并查询与所述病种类型信息对应支持分析的AI分析设备；
    将所述请求标识发送给所述AI用户终端，以便所述AI用户终端后续通过所述请求标识从所述区块链网络平台中查询所述整合过滤处理后的所述AI病种分析结果。
  7. 根据权利要求1所述的方法，其特征在于，所述对所述AI病种分析结果进行整合过滤处理，具体包括：
    按照转发所述AI病种分析请求的时间点和接收到所述AI病种分析结果的时间点，统计每个所述AI分析设备返回所述AI病种分析结果的响应时长；
    将所述响应时长小于预设时长阈值的所述AI病种分析结果进行合并处理。
  8. 一种人工智能的病种分析装置，其特征在于，包括：
    接收单元，用于接收人工智能AI用户终端发送的携带有待分析病种对应的病种类型信息和患者检查信息的AI病种分析请求；
    查询单元，用于查询与所述病种类型信息对应支持分析的AI分析设备；
    转发单元，用于将所述AI病种分析请求转发给所述AI分析设备，以使得所述AI分析设备根据所述患者检查信息进行AI病种分析，得到AI病种分析结果；
    处理单元，用于接收所述AI分析设备发送的所述AI病种分析结果，并对所述AI病种分析结果进行整合过滤处理；
    转发单元，还用于将整合过滤处理后的所述AI病种分析结果转发给所述AI用户终端。
  9. 根据权利要求8所述的装置，其特征在于，
    所述转发单元，具体用于获取所述AI分析设备的评分信息；
    根据所述评分信息，将所述AI病种分析请求转发给评分大于预设评分阈值的所述AI分析设备。
  10. 根据权利要求9所述的装置，其特征在于，所述AI病种分析请求中还携带有所述 待分析病种对应的诊断信息，
    所述转发单元，还用于计算所述诊断信息和接收到的所述AI病种分析结果之间的相似度；
    查询与所述相似度对应的第一得分；
    根据所述第一得分更新与所述接收到的所述AI病种分析结果对应的所述AI分析设备的评分信息。
  11. 根据权利要求9所述的装置，其特征在于，
    所述转发单元，还用于接收所述AI用户终端发送的对所述AI用户终端接收到的所述AI病种分析结果的第二得分；
    根据所述第二得分更新与所述AI用户终端接收到的所述AI病种分析结果对应的所述AI分析设备的评分信息。
  12. 根据权利要求8所述的装置，其特征在于，所述装置还包括：
    发送单元，用于将所述AI病种分析请求中携带的待分析病种对应的病种类型信息和患者检查信息和所述整合过滤处理后的AI病种分析结果发送到区块链网络平台，以使得所述区块链网络平台对所述AI病种分析请求中携带的待分析病种对应的病种类型信息和患者检查信息和所述AI病种分析结果进行加密存储。
  13. 根据权利要求12所述的装置，其特征在于，
    所述查询单元，还用于对所述AI用户终端依次进行安全验证和AI病种分析请求的请求权限验证；
    当所述AI用户终端通过安全验证、且具备AI病种分析请求的请求权限时，生成所述AI用户终端对应的请求标识，并查询与所述病种类型信息对应支持分析的AI分析设备；
    将所述请求标识发送给所述AI用户终端，以便所述AI用户终端后续通过所述请求标识从所述区块链网络平台中查询所述整合过滤处理后的所述AI病种分析结果。
  14. 根据权利要求8所述的装置，其特征在于，
    所述处理单元，具体用于按照转发所述AI病种分析请求的时间点和接收到所述AI病种分析结果的时间点，统计每个所述AI分析设备返回所述AI病种分析结果的响应时长；
    将所述响应时长小于预设时长阈值的所述AI病种分析结果进行合并处理。
  15. 一种非易失性可读存储介质，其上存储有计算机可读指令，其特征在于，所述计算机可读指令被处理器执行时实现人工智能的病种分析方法，包括：
    接收人工智能AI用户终端发送的AI病种分析请求，所述AI病种分析请求中携带有待 分析病种对应的病种类型信息和患者检查信息；
    查询与所述病种类型信息对应支持分析的AI分析设备；
    将所述AI病种分析请求转发给所述AI分析设备，以使得所述AI分析设备根据所述患者检查信息进行AI病种分析，得到AI病种分析结果；
    接收所述AI分析设备发送的所述AI病种分析结果，并对所述AI病种分析结果进行整合过滤处理；
    将整合过滤处理后的所述AI病种分析结果转发给所述AI用户终端。
  16. 根据权利要求15所述的非易失性可读存储介质，其特征在于，所述计算机可读指令被处理器执行时实现所述将所述AI病种分析请求转发给所述AI分析设备，具体包括：
    获取所述AI分析设备的评分信息；
    根据所述评分信息，将所述AI病种分析请求转发给评分大于预设评分阈值的所述AI分析设备。
  17. 根据权利要求16所述的非易失性可读存储介质，其特征在于，所述AI病种分析请求中还携带有所述待分析病种对应的诊断信息，所述计算机可读指令被处理器执行时实现所述方法还包括：
    计算所述诊断信息和接收到的所述AI病种分析结果之间的相似度；
    查询与所述相似度对应的第一得分；
    根据所述第一得分更新与所述接收到的所述AI病种分析结果对应的所述AI分析设备的评分信息。
  18. 一种计算机设备，包括非易失性可读存储介质、处理器及存储在非易失性可读存储介质上并可在处理器上运行的计算机可读指令，其特征在于，所述处理器执行所述计算机可读指令时实现人工智能的病种分析方法，包括：
    接收人工智能AI用户终端发送的AI病种分析请求，所述AI病种分析请求中携带有待分析病种对应的病种类型信息和患者检查信息；
    查询与所述病种类型信息对应支持分析的AI分析设备；
    将所述AI病种分析请求转发给所述AI分析设备，以使得所述AI分析设备根据所述患者检查信息进行AI病种分析，得到AI病种分析结果；
    接收所述AI分析设备发送的所述AI病种分析结果，并对所述AI病种分析结果进行整合过滤处理；
    将整合过滤处理后的所述AI病种分析结果转发给所述AI用户终端。
  19. 根据权利要求18所述的计算机设备，其特征在于，所述处理器执行所述计算机可读指令时实现所述将所述AI病种分析请求转发给所述AI分析设备，具体包括：
    获取所述AI分析设备的评分信息；
    根据所述评分信息，将所述AI病种分析请求转发给评分大于预设评分阈值的所述AI分析设备。
  20. 根据权利要求19所述的计算机设备，其特征在于，所述AI病种分析请求中还携带有所述待分析病种对应的诊断信息，所述处理器执行所述计算机可读指令时实现所述方法还包括：
    计算所述诊断信息和接收到的所述AI病种分析结果之间的相似度；
    查询与所述相似度对应的第一得分；
    根据所述第一得分更新与所述接收到的所述AI病种分析结果对应的所述AI分析设备的评分信息。
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