Pulmonary nodule cloud-assisted diagnosis and treatment evaluation system and method

Abstract

The embodiment of the present application provides a cloud-based terminal-assisted diagnosis and treatment evaluation system and method for lung nodules. The cloud-based terminal-assisted diagnosis and treatment evaluation system for lung nodules includes a patient side, a doctor side, an expert side, and a cloud server. The cloud server receives disease data information from the patient side; and sends examination suggestion information to the patient side based on the disease data information and preset consensus guideline information; determines the disease-assisted diagnosis and treatment results based on the examination data sent by the patient side; inputs the disease data information into a preset disease-assisted evaluation model to obtain the benign and malignant conditions or / and the malignant probability of the lung nodules, and sends the disease-assisted diagnosis and treatment results, the benign and malignant conditions of the disease or / and the malignant probability and the disease data information to the doctor side; generates a consultation instruction based on the consultation request from the doctor side and sends it to the expert side, so that the expert and the doctor can conduct online consultation and communication to obtain the first diagnosis and treatment plan. The embodiment of the present application can dynamically and intelligently assist doctors in diagnosing lung nodules and quickly improve the homogeneous diagnosis and treatment level of lung nodules.

Description

Technical Field

[0001] The embodiments of the present application relate to the field of medical technology, and in particular to a cloud-assisted terminal-assisted diagnosis and treatment evaluation system and method for lung nodules. Background Art

[0002] Lung cancer is currently the malignant tumor with the highest morbidity and mortality. Due to the limitations of the diagnosis and treatment level of some doctors, lung cancer is diagnosed late, resulting in a low survival rate for patients. In order to change this situation, it is urgent to promote lung cancer screening and early diagnosis and treatment. However, the diameter of early lung cancer (in situ and stage 1A lung cancer) in lung nodules is mostly within 10 mm, and it is difficult to obtain biopsy specimens for clear pathological diagnosis. In most cases, it depends only on imaging diagnosis and clinical experience. And due to the limitations of the diagnosis and treatment level of handicraft workshops, delayed diagnosis or overtreatment often occurs. The strategy proposed by Professor Bai Chunxue to "simplify complex problems, digitize simple problems, program digital problems, and systematize program problems" will improve medical and health care efficiency.

[0003] In order to improve the survival rate of patients, a cloud-plus-end auxiliary diagnosis and treatment model has been proposed in relevant technologies. In this model, "end" doctors need to learn the consensus guidelines provided by "cloud" experts in order to improve the level of homogeneous diagnosis and treatment.

[0004] Although "end" doctors can learn the consensus guidelines provided by "cloud" experts, this rote learning method is inefficient. Therefore, it is necessary to propose a cloud-plus-end assisted diagnosis and treatment evaluation system that can quickly improve the homogeneous diagnosis and treatment level of "end" doctors and "cloud" experts. Summary of the invention

[0005] In view of the above problems, the embodiments of the present application provide a cloud-and-end-assisted diagnosis and treatment evaluation system and method for lung nodules, which can dynamically and intelligently assist doctors in diagnosing lung nodules and quickly improve the level of homogeneous diagnosis and treatment of lung nodules.

[0006] In a first aspect, an embodiment of the present application provides a cloud-and-end-assisted diagnosis and treatment evaluation system for pulmonary nodules, including: a patient end, a doctor end, an expert end, and a cloud server;

[0007] The patient terminal is used to send disease data information to the cloud server according to the acquired disease data query information; the disease data information is filled in by the patient;

[0008] The cloud server is used to receive the disease data information, and send examination recommendation information to the patient end according to the disease data information and preset consensus guideline information; and input the disease data information into a preset disease auxiliary assessment model to obtain the benign or malignant condition of the disease or / and the malignant probability, and send the benign or malignant condition of the disease or / and the malignant probability and the disease data information to the doctor end;

[0009] The patient terminal is also used to receive the examination data input by the patient according to the examination suggestion information, and send the examination data to the cloud server;

[0010] The cloud server is also used to determine the disease auxiliary diagnosis and treatment results according to the examination data; and send the examination data and the disease auxiliary diagnosis and treatment results to the doctor end;

[0011] The doctor terminal is used to send a consultation request to the cloud server according to the benign and malignant conditions of the disease and / or the probability of malignancy, the examination data and the auxiliary diagnosis and treatment results of the disease; the consultation request at least includes the disease data information, the benign and malignant conditions of the disease and / or the probability of malignancy, the examination data and the auxiliary diagnosis and treatment results of the disease;

[0012] The cloud server is used to generate a consultation instruction according to the consultation request, and send the consultation instruction to the expert terminal;

[0013] The expert end is used to send online consultation information to the doctor end through the cloud server according to the received consultation instruction; the online consultation information is used to notify the doctor end to conduct an online consultation exchange to obtain a first diagnosis and treatment plan.

[0014] Optionally, the benign or malignant condition of the disease or / and the malignant probability include a low malignant probability, a moderate malignant probability and a high malignant probability; the doctor end is specifically used to send the consultation request to the cloud server when the benign or malignant condition of the disease or / and the malignant probability is the moderate malignant probability and the high malignant probability;

[0015] The cloud server is also used to match the expert end of the corresponding level according to the benign or malignant condition of the disease and / or the malignant probability in the consultation request and the preset expert matching rules, and send the generated consultation instruction to the expert end of the corresponding level.

[0016] Optionally, when the benign or malignant condition of the disease or / and the malignant probability is the low malignant probability, the doctor terminal is further used to send a second diagnosis and treatment plan to the cloud server;

[0017] The cloud server is also used to push the second diagnosis and treatment plan to the patient.

[0018] Optionally, before sending the examination suggestion information to the patient end, the cloud server is further used to send the examination suggestion information to the doctor end;

[0019] The doctor terminal is also used to receive the examination modification information input by the doctor according to the examination suggestion information, and send the examination modification information to the cloud server;

[0020] The cloud server is also used to modify the examination suggestion information according to the examination modification information, and send the modified examination suggestion information to the patient end.

[0021] Optionally, the preset disease auxiliary assessment model is trained based on historical patient data in a patient database, wherein the historical patient data at least includes historical disease data information and corresponding historical disease malignancy levels.

[0022] Optionally, before sending the disease data information to the cloud server based on the acquired disease data query information, the patient end is also used to send a disease consultation request to the cloud server and receive the disease data query information issued by the cloud server based on the disease consultation request.

[0023] Optionally, the cloud server is also used to receive a first diagnosis and treatment plan or a second diagnosis and treatment plan from the doctor side; generate a first recurrence and metastasis prevention and treatment plan based on the examination data and the first diagnosis and treatment plan, or generate a second recurrence and metastasis prevention and treatment plan based on the examination data and the second diagnosis and treatment plan; and push the first recurrence and metastasis prevention and treatment plan or the second recurrence and metastasis prevention and treatment plan to the doctor side and the patient side.

[0024] In a second aspect, the present application also provides a cloud-based terminal-assisted diagnosis and treatment evaluation method for pulmonary nodules, the method comprising:

[0025] Receive disease data information;

[0026] Sending examination recommendation information according to the disease data information and preset consensus guideline information; waiting for the patient to input examination data based on the first examination recommendation;

[0027] Inputting the disease data information into a preset disease auxiliary assessment model to obtain the benign or malignant condition of the disease or / and the probability of malignancy, and sending the benign or malignant condition of the disease or / and the probability of malignancy and the disease data information;

[0028] receiving the examination data, and determining the auxiliary diagnosis and treatment result of the disease according to the examination data;

[0029] Sending the inspection data and the auxiliary diagnosis and treatment results of the disease;

[0030] Receiving a consultation request; the consultation request at least includes the disease data information, the benign or malignant condition of the disease and / or the probability of malignancy, the examination data and the auxiliary diagnosis and treatment results of the disease;

[0031] generating a consultation instruction according to the consultation request, and sending the consultation instruction;

[0032] The received online consultation information sent by the expert is forwarded, so that the doctor and the expert can conduct online consultation and communication based on the online consultation information to obtain a first diagnosis and treatment plan.

[0033] Optionally, after sending the inspection data and the disease auxiliary diagnosis and treatment results, the method further includes:

[0034] A second diagnosis and treatment plan is received, where the second diagnosis and treatment plan is generated by a doctor based on the examination data and the auxiliary diagnosis and treatment result of the disease.

[0035] Optionally, before sending the inspection suggestion information, the method further includes:

[0036] receiving examination modification information input by the doctor according to the examination suggestion information;

[0037] The inspection suggestion information is modified according to the inspection modification information, and the modified inspection suggestion information is sent.

[0038] The cloud-based terminal-assisted diagnosis and treatment evaluation system and method for lung nodules provided in the embodiments of the present application. The cloud-based terminal-assisted diagnosis and treatment evaluation system for lung nodules includes a patient side, a doctor side, an expert side, and a cloud server. The cloud server receives disease data information from the patient side; and sends examination suggestion information to the patient side based on the disease data information and preset consensus guideline information; determines the disease-assisted diagnosis and treatment results based on the examination data sent by the patient side; inputs the disease data information into a preset disease-assisted evaluation model to obtain the benign and malignant conditions of the disease or / and the probability of malignancy, and sends the disease-assisted diagnosis and treatment results, the benign and malignant conditions of the disease or / and the probability of malignancy, and the disease data information to the doctor side; generates a consultation instruction based on the consultation request from the doctor side and sends it to the expert side, so that the expert and the doctor conduct an online consultation and communication to obtain the first diagnosis and treatment plan. By pre-setting consensus guideline information in the cloud server and programming the consensus guidelines, the cloud server can generate examination recommendation information based on the preset consensus guideline information after receiving disease data information from the patient and send it to the patient, without the need for doctors to memorize it. Therefore, it can dynamically and intelligently assist doctors in diagnosing lung nodules and quickly improve the level of homogeneous diagnosis and treatment of lung nodules.

[0039] The above description is only an overview of the technical solution of the embodiment of the present application. In order to more clearly understand the technical means of the embodiment of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the embodiment of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] In order to more clearly illustrate the technical solutions of the embodiments of the present application, a brief introduction will be given below to the drawings required for use in the description of the embodiments. Obviously, the drawings described below are some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0041] Figure 1 It is a structural diagram of a cloud-and-end-assisted diagnosis and treatment evaluation system for lung nodules according to an embodiment of the present application.

[0042] Figure 2 It is a structural diagram of another lung nodule cloud-and-end assisted diagnosis and treatment evaluation system according to an embodiment of the present application.

[0043] Figure 3 It is a communication diagram of the cloud-and-end assisted diagnosis and treatment evaluation system for lung nodules according to an embodiment of the present application.

[0044] Figure 4 It is a flow chart of the cloud-and-end assisted diagnosis and treatment evaluation method for lung nodules in an embodiment of the present application. DETAILED DESCRIPTION

[0045] In order to make the purpose, technical solution and advantages of the embodiments of the present application clearer, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0046] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by technicians in the technical field to which this application belongs; the terms used in the specification of the application are only for the purpose of describing specific embodiments and are not intended to limit this application; the terms "including" and "having" and any variations thereof in the specification, claims and drawings of this application are intended to cover non-exclusive inclusions.

[0047] Reference to "embodiments" herein means that a particular feature, structure, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present application. The appearance of the phrase "embodiments" in various locations in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0048] The term "and / or" in this article is only a description of the association relationship of the associated objects, indicating that there can be three relationships. For example, A and / or B can mean: A exists, A and B exist at the same time, and B exists. In addition, the character " / " in this article generally indicates that the associated objects before and after are in an "or" relationship.

[0049] In addition, the terms "first", "second", etc. in the specification and claims of this application or the above-mentioned drawings are used to distinguish different objects rather than to describe a specific order, and may explicitly or implicitly include one or more of the features.

[0050] In the description of the present application, unless otherwise specified, "plurality" means more than two (including two), and similarly, "multiple groups" means more than two groups (including two).

[0051] Primary bronchial lung cancer (lung cancer) is the malignant tumor with the highest incidence and mortality rate in the world. Due to late diagnosis, the survival rate of patients is low. In order to change this situation, it is urgent to promote lung cancer screening and early diagnosis and treatment. However, the diameter of early lung cancer (in situ and stage 1A lung cancer) in lung nodules is mostly within 10 mm, and it is difficult to obtain biopsy specimens for clear pathological diagnosis. In most cases, it depends only on imaging diagnosis and clinical experience. And due to the limitation of the level of handicraft workshop-style diagnosis and treatment, delayed diagnosis or overtreatment often occurs. To solve these problems, the embodiment of the present application proposes a cloud-plus-end auxiliary diagnosis and treatment evaluation system and method for lung nodules, which upgrades the handicraft workshop diagnosis and treatment mode with different levels to a homogeneous assembly line project. The following is a detailed description of the cloud-plus-end auxiliary diagnosis and treatment evaluation system and method for lung nodules in the embodiment of the present application in conjunction with the accompanying drawings. It can be understood that the cloud-plus-end auxiliary diagnosis and treatment evaluation system and method for lung nodules in the embodiment of the present application, in addition to the auxiliary diagnosis and treatment of lung nodules, are also applicable to the auxiliary diagnosis and treatment of other lung diseases, and no specific restrictions are made here.

[0052] Figure 1It is a structural diagram of a pulmonary nodule cloud plus terminal assisted diagnosis and treatment evaluation system of an embodiment of the present application, including: a terminal doctor as the "terminal" (using a mobile terminal or computer to interact with the doctor) and a data center, cloud platform and cloud expert as the "cloud". The "cloud" is a virtual computing resource that can be self-maintained and managed, and is a large server cluster. It includes computing servers, storage servers, broadband resources, etc. The "cloud" terminal here specifically refers to a collection of computers participating in the cloud computing of pulmonary nodule diagnosis and treatment services. The cloud is an intermediate carrier of software and operating systems, which can solve the problems of installation, maintenance, hardware resource requirements, etc. when using software in the past and the problems of using efficiency, and realize the real and complete greening of software. Through the "cloud", it is possible to conduct deep mining of massive information under the cloud computing framework, extract inquiry data and examination item data related to pulmonary nodules, build its data model, and monitor and interact with pulmonary nodule information based on the Internet of Things and online medical services. The "terminal" is a "terminal" software applet downloaded by the doctor's smartphone, PAD or laptop, which interacts with the "cloud" in real time online, and uses the applet running on the "cloud" platform to assist in the diagnosis and treatment of special diseases. The backend of the mini program can also be developed in Java, and OSS (Object Storage Service) is used for data storage. After uploading to the cloud computing, it can monitor the data, analyze and deeply mine it, and draw reference opinions for auxiliary diagnosis and treatment. Ultimately, it can assist all doctors to easily and conveniently assist in the diagnosis and treatment of diseases according to its process-based intelligent system.

[0053] Figure 2 Schematic diagram of another pulmonary nodule cloud-based terminal-assisted diagnosis and treatment evaluation system 1 according to an embodiment of the present application. Figure 2 As shown, the cloud-and-terminal auxiliary diagnosis and treatment evaluation system 1 may include: a patient terminal 10, a doctor terminal 20, an expert terminal 30 and a cloud server 40. Among them, the patient terminal 10 may be a terminal such as a smart phone, a tablet computer or a laptop computer used by the patient, and the corresponding software functions are realized based on the small program for the patient running on the application platform in the terminal. The doctor terminal 20 may be a terminal such as a smart phone, a tablet computer or a laptop computer used by the initial evaluation outpatient doctor, and the corresponding software functions may also be realized based on the small program for the outpatient doctor running on the application platform in the terminal. Similarly, the expert terminal 30 may be a terminal such as a smart phone, a tablet computer or a laptop computer used by the expert doctor for evaluation and judgment, and the corresponding software functions may also be realized based on the small program for the expert doctor running on the application platform in the terminal. The cloud server 40 may receive data from the patient terminal 10, the doctor terminal 20 and the expert terminal 30 through a communication link, process the data from the patient terminal 10, the doctor terminal 20 and the expert terminal 30, and send corresponding instructions to the corresponding terminal.

[0054] Combine the following Figure 3The communication process among the patient terminal 10, the doctor terminal 20, the expert terminal 30 and the cloud server 40 in the pulmonary nodule cloud-and-terminal assisted diagnosis and treatment evaluation system 1 of this embodiment is described.

[0055] The patient terminal 10 is used to send disease data information to the cloud server 40 according to the acquired disease data query information; the disease data information is filled in by the patient.

[0056] Before the patient terminal 10 obtains the disease data inquiry information, the patient needs to apply for registration authorization in the patient's mini program interface and select consultation, which triggers the patient terminal 10 to generate a disease consultation request. The patient's mini program obtains the trigger operation, and can generate a disease consultation request based on the trigger operation, and obtain the disease data inquiry information based on the disease consultation request.

[0057] The disease data query information may be preset in the patient terminal 10, or may be obtained by the patient terminal 10 from the cloud server 40. For example, after generating a disease inquiry request, the patient terminal 10 may obtain preset disease data inquiry information according to the disease inquiry request, and provide it to the patient. For another example, after generating a disease inquiry request, the patient terminal 10 may send the disease inquiry request to the cloud server 40, and after receiving the disease inquiry request, the cloud server 40 sends the disease data query information to the patient terminal 10 based on the disease inquiry request.

[0058] It should be noted that the disease data inquiry information is used to inquire the patient about the relevant information required for disease assessment. For example, for lung nodules, the relevant information required for disease assessment may include smoking history, passive smoking history, occupational disease history, personal and family tumor history, chronic respiratory disease history such as asthma, COPD and pulmonary fibrosis, and antibiotic treatment history. It is understandable that this embodiment can also be used to diagnose other lung diseases.

[0059] In a feasible implementation, the disease data inquiry information can be presented to the patient in the form of a form, and the patient can provide the disease data information to the cloud server 40 by filling in the form or selecting preset options in the form.

[0060] The cloud server 40 is used to receive disease data information, and send examination recommendation information to the patient end 10 based on the disease data information and preset consensus guideline information; and input the disease data information into a preset disease auxiliary assessment model to obtain the benign and malignant condition of the disease and / or the malignant probability, and send the benign and malignant condition of the disease and / or the malignant probability and the disease data information to the doctor end 20.

[0061] The consensus guideline information is obtained based on the guiding suggestions provided by experts to doctors and grassroots medical workers based on the disease for specific clinical problems. In this embodiment, the consensus guideline information is preset in the cloud server 40, so that after receiving the disease data information from the patient end 10, the cloud server 40 can analyze the disease data information based on the preset consensus guideline information to obtain corresponding examination recommendation information.

[0062] Exemplarily, for lung nodules, the disease data information may include thin-layer CT in the Digital Imaging and Communications in Medicine (DICOM) format. After receiving the thin-layer CT in the DICOM format, the cloud server 40 may analyze and evaluate the benign and malignant characteristics of the lung nodules, such as the diameter, the presence of lobulation, burrs and pleural indentation signs, the presence of calcification, cavitation signs and angiogenesis, based on the preset consensus guideline information, and generate corresponding examination recommendation information based on the results of the analysis and evaluation.

[0063] In this embodiment, after the cloud server 40 generates corresponding examination recommendation information based on the disease data information and the preset consensus guideline information, it sends the examination recommendation information to the patient end 10. Optionally, the examination recommendation information may include personalized examinations that the patient needs to undergo further, such as tuberculosis, fungi and clinical information and tumor marker combination (lung cancer biomarker panel, LCBP) examinations required to complete the differential diagnosis, as well as individualized examinations such as AI, circulating abnormal cells (CAC) and PET / CT, for patients or their families to choose.

[0064] In a feasible implementation, the cloud server 40 can also be used to send the examination recommendation information to the doctor end 20 before sending the examination recommendation information to the patient end 10; the doctor end 20 is also used to receive the examination modification information input by the initial evaluation outpatient doctor based on the examination recommendation information, and send the examination modification information to the cloud server 40; the cloud server 40 is also used to modify the examination recommendation information based on the examination modification information, and send the modified examination recommendation information to the patient end 10.

[0065] That is, before sending the examination suggestion information to the patient, the initial evaluation clinic doctor can also check the examination suggestion information automatically generated by the cloud server 40. If it contains some unnecessary examinations or omits some necessary examination items, the initial evaluation clinic doctor can input the examination modification information through the small program for doctors and send it to the cloud server 40, so that the cloud server 40 can modify the generated examination suggestion information according to the examination modification information, add or remove some examination items, and send the modified examination suggestion information to the patient terminal 10. In this way, it can prevent patients from missing or doing unnecessary examinations.

[0066] At the same time, a disease auxiliary assessment model is also added to the cloud server 40, which can automatically generate the benign and malignant conditions of the disease or / and the malignant probability according to the disease data information, and provide the benign and malignant conditions of the disease or / and the malignant probability to the doctor end 20 for reference, so as to assist the doctor in obtaining a diagnosis and treatment plan. The malignancy of the disease may include, for example, a low malignancy probability, a moderate malignancy probability, and a high malignancy probability. Among them, the preset disease auxiliary assessment model is obtained by training based on the historical patient data in the patient database, and the historical patient data at least includes historical disease data information and the corresponding historical disease malignancy. It is understandable that in the actual diagnosis and treatment process, each time a diagnosis and treatment plan is obtained, the patient data about the patient will also be placed in the patient database, and the preset disease auxiliary assessment model will be trained based on the new patient data to update the parameters of the preset disease auxiliary assessment model. In this way, through continuous learning, a more accurate benign and malignant condition of the disease or / and the malignant probability can be obtained for reference by the doctor.

[0067] The patient terminal 10 is also used to receive the examination data input by the patient according to the examination recommendation information, and send the examination data to the cloud server 40. The examination data is the specific value or image data entered by the patient on the interface of the mini program for the patient after receiving the examination report of each examination.

[0068] The cloud server 40 is also used to determine the auxiliary diagnosis and treatment results of the disease based on the examination data; and send the examination data and the auxiliary diagnosis and treatment results of the disease to the doctor end 20.

[0069] Specifically, the cloud server 40 can compare the inspection data of each inspection with the standard values ​​corresponding to each inspection data to obtain auxiliary disease diagnosis and treatment results for reference by the initial evaluation outpatient doctors.

[0070] The doctor end 20 is used to send a consultation request to the cloud server 40 based on the benign or malignant condition of the disease and / or the probability of malignancy, examination data and auxiliary diagnosis and treatment results of the disease; the consultation request includes at least disease data information, benign or malignant condition of the disease and / or the probability of malignancy, examination data and auxiliary diagnosis and treatment results of the disease.

[0071] The initial evaluation clinic doctor can view the benign and malignant conditions of the disease or / and the probability of malignancy, the examination data, and the auxiliary diagnosis and treatment results of the disease from the small program interface for doctors. In the case where the benign and malignant conditions of the disease or / and the probability of malignancy are low malignant probabilities, the initial evaluation clinic doctor can derive a second diagnosis and treatment plan based on the examination data and the auxiliary diagnosis and treatment results of the disease, and input the second diagnosis and treatment plan on the doctor's end 20, and trigger the sending of the second diagnosis and treatment plan to the cloud server 40. Accordingly, the cloud server 40 is also used to push the second diagnosis and treatment plan to the patient end 10.

[0072] For example, in the second diagnosis and treatment plan, for those with biopsy indications, one of the following techniques can be considered, such as bronchoscopy, EBUS, real-time ultrasound-guided bronchial needle aspiration biopsy (EBUS-TBNA), or electromagnetic navigation bronchoscopy (ENB), according to local conditions. For those who are not suitable, transthoracic needle aspiration biopsy (TNAB) can be considered, but the patient needs to be informed of the possibility of implantation metastasis. For those who cannot exclude infection, empirical antibiotic treatment for one week and reexamination can be considered; for those with lung nodules <10 mm, follow-up can be arranged according to consensus guidelines.

[0073] In the case of benign or malignant disease or / and moderate or high probability of malignancy, if the initial evaluation outpatient doctor finds it difficult to reach a diagnosis, for example, when a lung nodule ≥10mm cannot be diagnosed, a consultation request can be sent to the cloud server 40 through the doctor terminal 20 to request a prior consultation with an expert doctor.

[0074] The cloud server 40 is used to generate a consultation instruction according to the consultation request, and send the consultation instruction to the expert terminal 30. The consultation instruction may at least include disease data information, benign and malignant conditions of the disease or / and malignant probability, examination data and auxiliary diagnosis and treatment results of the disease, so that the expert doctor can understand the patient according to the disease data information, benign and malignant conditions of the disease or / and malignant probability, examination data and auxiliary diagnosis and treatment results of the disease.

[0075] In practical applications, expert doctors can be divided into different levels through system training, for example, they can be divided into four levels: A, B, C, and D. Expert doctors of different levels can participate in online consultations for diseases with different benign and malignant conditions or / and malignant probabilities. Accordingly, the cloud server 40 is also used to match the expert end of the corresponding level according to the benign and malignant conditions of the disease or / and the malignant probability in the consultation request and the preset expert matching rules, and send the generated consultation instruction to the expert end of the corresponding level.

[0076] The expert terminal 30 is used to send online consultation information to the doctor terminal 20 through the cloud server 40 according to the received consultation instruction; the online consultation information is used to notify the doctor terminal 20 to conduct online consultation communication to obtain a first diagnosis and treatment plan. It can be understood that after obtaining the first diagnosis and treatment plan, the first diagnosis and treatment plan can be sent from the professional terminal to the patient terminal 10 through the cloud server 40.

[0077] Furthermore, the cloud server 40 is also used to receive the first diagnosis and treatment plan or the second diagnosis and treatment plan from the doctor end 20; generate a first recurrence prevention and treatment plan based on the examination data and the first diagnosis and treatment plan, or generate a second recurrence prevention and treatment plan based on the examination data and the second diagnosis and treatment plan; and push the first recurrence prevention and treatment plan or the second recurrence prevention and treatment plan to the doctor end 20 and the patient end 10.

[0078] That is to say, in this embodiment, after diagnosis and treatment according to the first diagnosis and treatment plan or the second diagnosis and treatment plan, the cloud server 40 can also perform routine or individualized postoperative management according to the patient's requirements, referring to the pathological results and staging, such as formulating an individualized prevention and treatment plan for recurrence and metastasis based on AI, CAC, epigenetic and other information.

[0079] The pulmonary nodule cloud-and-end assisted diagnosis and treatment evaluation system 1 of the present embodiment includes a patient end 10, a doctor end 20, an expert end 30 and a cloud server 40. The cloud server 40 receives disease data information from the patient end 10; and sends examination suggestion information to the patient end 10 based on the disease data information and preset consensus guideline information; determines the disease-assisted diagnosis and treatment results based on the examination data sent by the patient end 10; inputs the disease data information into a preset disease-assisted evaluation model to obtain the benign and malignant conditions of the disease or / and the probability of malignancy, and sends the disease-assisted diagnosis and treatment results, the benign and malignant conditions of the disease or / and the probability of malignancy and the disease data information to the doctor end 20; generates a consultation instruction based on the consultation request of the doctor end 20 and sends it to the expert end 30, so that the expert and the doctor conduct an online consultation and communication to obtain a first diagnosis and treatment plan. By presetting the consensus guideline information in the cloud server 40 and programming the consensus guideline, the cloud server 40 can generate examination recommendation information based on the preset consensus guideline information after receiving the disease data information sent by the patient end 10 and send it to the patient end 10, without the need for doctors to memorize it. It can simplify complex problems, digitize simple problems, program digital problems, and systematize procedural problems, forming a master-apprentice inheritance model of procedural knowledge, which can dynamically and intelligently assist doctors in diagnosing lung nodules, improve the efficiency of lung nodule assessment, and rapidly improve the level of homogeneous diagnosis and treatment of lung nodules.

[0080] Figure 44 is a flow chart of the cloud-and-end-assisted diagnosis and treatment evaluation method for pulmonary nodules according to an embodiment of the present application. The cloud-and-end-assisted diagnosis and treatment evaluation method according to this embodiment can be specifically executed by the cloud server 40 in the above embodiment.

[0081] like Figure 4 As shown, the cloud-assisted diagnosis and treatment evaluation method for pulmonary nodules in this embodiment may include the following steps 3100 to 3800:

[0082] Step 3100: Receive disease data information.

[0083] Among them, disease data information may include, for example, smoking history, passive smoking history, occupational disease history, personal and family tumor history, chronic respiratory disease history such as asthma, chronic obstructive pulmonary disease and pulmonary fibrosis, and antibiotic treatment history.

[0084] Step 3200: Send examination recommendation information based on the disease data information and the preset consensus guideline information; wait for the patient to input examination data based on the first examination recommendation.

[0085] In this embodiment, the consensus guideline information is preset in the cloud server, so that after receiving the disease data information from the patient, the cloud server can analyze the disease data information based on the preset consensus guideline information to obtain corresponding examination recommendation information. Optionally, the examination recommendation information may include personalized examinations that the patient needs to undergo further, such as tuberculosis, fungal and clinical information and LCBP examinations required to improve differential diagnosis, as well as individualized examinations such as AI, CAC and PET / CT, for patients or their families to choose.

[0086] In a feasible implementation, before sending the examination recommendation information, it is also possible to receive examination modification information input by the doctor based on the examination recommendation information; modify the examination recommendation information based on the examination modification information, add or remove some examination items, and send the modified examination recommendation information to avoid patients missing or doing unnecessary examinations.

[0087] Step 3300: Input the disease data information into a preset disease auxiliary assessment model to obtain the benign or malignant condition of the disease and / or the probability of malignancy, and send the benign or malignant condition of the disease and / or the probability of malignancy and the disease data information.

[0088] Among them, the malignancy of the disease may include, for example, a low malignancy probability, a moderate malignancy probability, and a high malignancy probability. The preset disease auxiliary evaluation model is trained based on historical patient data in the patient database, and the historical patient data at least includes historical disease data information and the corresponding historical disease malignancy. It is understandable that in the actual diagnosis and treatment process, every time a diagnosis and treatment plan is obtained, the patient data about the patient will also be put into the patient database, and the preset disease auxiliary evaluation model will be trained based on the new patient data to update the parameters of the preset disease auxiliary evaluation model. In this way, through continuous learning, more accurate benign and malignant conditions of the disease or / and malignant probabilities can be obtained for reference by doctors.

[0089] Step 3400: Receive examination data and determine the results of auxiliary diagnosis and treatment of the disease based on the examination data.

[0090] Specifically, the inspection data of each inspection can be compared with the standard values ​​corresponding to each inspection data to obtain the auxiliary diagnosis and treatment results of the disease.

[0091] Step 3500: Send examination data and disease auxiliary diagnosis and treatment results.

[0092] In one implementation, after the examination data and the auxiliary diagnosis and treatment results are sent, if the primary evaluation clinician can come up with a second diagnosis and treatment plan based on the examination data and the auxiliary diagnosis and treatment results, the second diagnosis and treatment plan is sent to the cloud server. The cloud server receives the second diagnosis and treatment plan and pushes it to the patient.

[0093] In another implementation, after sending the examination data and the auxiliary diagnosis and treatment results of the disease, if the initial evaluation clinician finds it difficult to reach a diagnosis conclusion, a consultation request will be sent to the cloud server, and the cloud server will continue to execute step 3600.

[0094] Step 3600: receiving a consultation request; the consultation request includes at least disease data information, benign or malignant status of the disease and / or malignancy probability, examination data and auxiliary diagnosis and treatment results of the disease.

[0095] Step 3700: Generate a consultation instruction based on the consultation request and send the consultation instruction.

[0096] Step 3800: Forward the online consultation information received from the expert, so that the doctor and the expert can conduct online consultation and communication based on the online consultation information to obtain a first diagnosis and treatment plan.

[0097] Furthermore, in this embodiment, after diagnosis and treatment according to the first diagnosis and treatment plan or the second diagnosis and treatment plan, the cloud server can also perform routine or individualized postoperative management according to the patient's requirements and refer to the pathological results and staging, such as formulating an individualized prevention and treatment plan for recurrence and metastasis based on AI, CAC, epigenetic and other information.

[0098] The cloud-and-end assisted diagnosis and treatment evaluation method for lung nodules of this embodiment presets consensus guideline information in the cloud server and programs the consensus guideline, so that the cloud server can generate examination recommendation information based on the preset consensus guideline information after receiving the disease data information sent by the patient and send it to the patient, without the need for doctors to memorize the consensus guidelines. It can simplify complex problems, digitize simple problems, program digital problems, and systematize procedural problems, forming a master-apprentice inheritance model of procedural knowledge, which can dynamically and intelligently assist doctors in diagnosing diseases, improve the efficiency of lung nodule evaluation, and rapidly improve the level of homogeneous diagnosis and treatment of lung nodules.

[0099] In the several embodiments provided in the present application, it should be understood that the disclosed backpack base station, device and method can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of modules or units is only a logical function division. There may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0100] Each functional unit or module in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional unit.

[0101] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application is essentially or the part that contributes to the prior art or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a number of instructions to enable a computer device (which can be a personal computer, server, or network device, etc.) or a processor (processor) to perform all or part of the steps of each embodiment method of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (Read-Only Memory, ROM), random access memory (Random Access Memory, RAM), disk or optical disk and other media that can store program code.

[0102] In the claims, any reference symbols placed between brackets shall not be construed as limiting the claims. The word "comprising" described in the present application does not exclude the presence of elements or steps not listed in the claims. The word "a" or "an" preceding an element does not exclude the presence of a plurality of such elements. The present application can be implemented with the aid of hardware comprising several different elements and with the aid of a suitably programmed computer. In a unit claim that lists a number of devices, several units of these devices may be embodied by the same hardware item. The use of first, second, and third, etc. does not indicate any order, and these words may be interpreted as names. The steps in the above embodiments, unless otherwise specified, should not be understood as limitations on the order of execution.

[0103] As described above, the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A cloud-based terminal-assisted diagnosis and treatment evaluation system for pulmonary nodules, characterized in that: include: Patient side, doctor side, expert side and cloud server; The patient terminal is used to send disease data information to the cloud server according to the acquired disease data query information; The disease data information is filled in by the patient; The cloud server is used to receive the disease data information, and send examination recommendation information to the patient end according to the disease data information and the preset consensus guideline information; and input the disease data information into a preset disease auxiliary assessment model to obtain the benign or malignant condition of the disease or / and the malignant probability, and send the benign or malignant condition of the disease or / and the malignant probability and the disease data information to the doctor end; the examination recommendation information includes information suggesting that the patient further needs to undergo personalized examinations; The patient terminal is also used to receive the examination data input by the patient according to the examination suggestion information, and send the examination data to the cloud server; The cloud server is also used to determine the disease auxiliary diagnosis and treatment results according to the examination data; and send the examination data and the disease auxiliary diagnosis and treatment results to the doctor end; The doctor terminal is used to send a consultation request to the cloud server according to the benign or malignant condition of the disease or / and the probability of malignancy, the examination data and the auxiliary diagnosis and treatment results of the disease; the consultation request at least includes the disease data information, the benign or malignant condition of the disease or / and the probability of malignancy, the examination data and the auxiliary diagnosis and treatment results of the disease; the consultation request is used to request an online consultation with an expert doctor; The cloud server is used to generate a consultation instruction according to the consultation request, and send the consultation instruction to the expert terminal; The expert end is used to send online consultation information to the doctor end through the cloud server according to the received consultation instruction; the online consultation information is used to notify the doctor end to conduct online consultation and communication to obtain a first diagnosis and treatment plan; the benign and malignant conditions of the disease or / and the malignant probability include a low malignant probability, a moderate malignant probability and a high malignant probability; the doctor end is specifically used to send the consultation request to the cloud server when the benign and malignant conditions of the disease or / and the malignant probability are the moderate malignant probability and the high malignant probability; The cloud server is also used to match the expert end of the corresponding level according to the benign or malignant condition of the disease or / and the malignant probability in the consultation request and the preset expert matching rules, and send the generated consultation instruction to the expert end of the corresponding level; When the benign or malignant condition of the disease or / and the malignant probability is the low malignant probability, the doctor end is further used to send a second diagnosis and treatment plan to the cloud server; the second diagnosis and treatment plan is generated by the doctor end based on the examination data and the auxiliary diagnosis and treatment result of the disease; The cloud server is also used to push the second diagnosis and treatment plan to the patient.

2. The system according to claim 1, characterized in that Before sending the examination suggestion information to the patient, the cloud server is further used to send the examination suggestion information to the doctor; The doctor terminal is also used to receive the examination modification information input by the doctor according to the examination suggestion information, and send the examination modification information to the cloud server; The cloud server is also used to modify the examination suggestion information according to the examination modification information, and send the modified examination suggestion information to the patient end.

3. The system according to claim 1, characterized in that The preset disease auxiliary assessment model is trained based on historical patient data in a patient database, wherein the historical patient data at least includes historical disease data information and the corresponding historical disease malignancy.

4. The system according to claim 1, characterized in that Before sending the disease data information to the cloud server based on the acquired disease data query information, the patient end is also used to send a disease inquiry request to the cloud server and receive the disease data inquiry information issued by the cloud server based on the disease inquiry request.

5. The system according to claim 1, characterized in that The cloud server is also used to receive the first diagnosis and treatment plan or the second diagnosis and treatment plan from the doctor; generate a first prevention and treatment plan for recurrence and metastasis based on the examination data and the first diagnosis and treatment plan, or generate a second prevention and treatment plan for recurrence and metastasis based on the examination data and the second diagnosis and treatment plan; And the first prevention and treatment plan for recurrence and metastasis or the second prevention and treatment plan for recurrence and metastasis is pushed to the doctor end and the patient end.

6. A cloud-based terminal-assisted diagnosis and treatment evaluation method for pulmonary nodules, characterized in that: The method comprises: Receive disease data information; Sending examination recommendation information according to the disease data information and the preset consensus guideline information; waiting for the patient to input examination data based on the examination recommendation information; the examination recommendation information includes information suggesting that the patient further needs to undergo personalized examinations; Inputting the disease data information into a preset disease auxiliary assessment model to obtain the benign or malignant condition of the disease or / and the probability of malignancy, and sending the benign or malignant condition of the disease or / and the probability of malignancy and the disease data information; receiving the examination data, and determining the auxiliary diagnosis and treatment result of the disease according to the examination data; Sending the inspection data and the auxiliary diagnosis and treatment results of the disease; Receiving a consultation request; the consultation request at least includes the disease data information, the benign or malignant condition of the disease and / or the probability of malignancy, the examination data and the auxiliary diagnosis and treatment results of the disease; generating a consultation instruction according to the consultation request, and sending the consultation instruction; Forwarding the received online consultation information sent by the expert, so that the doctor and the expert can conduct online consultation and communication based on the online consultation information to obtain a first diagnosis and treatment plan; The benign or malignant condition of the disease and / or the probability of malignancy include a low probability of malignancy, a moderate probability of malignancy and a high probability of malignancy; The receiving of the consultation request includes: receiving the consultation request when the benign or malignant condition of the disease or / and the malignant probability is the moderate malignant probability and the high malignant probability; the consultation request is used to request an online consultation with an expert doctor; Generating a consultation instruction according to the consultation request and sending the consultation instruction includes: matching experts of corresponding levels according to the benign or malignant condition of the disease or / and the probability of malignancy in the consultation request and preset expert matching rules, and sending the generated consultation instruction to the experts of corresponding levels; After sending the inspection data and the disease auxiliary diagnosis and treatment results, the method further includes: In case the disease is benign or malignant and / or the probability of malignancy is the low probability of malignancy, a second diagnosis and treatment plan is received and pushed to the patient, wherein the second diagnosis and treatment plan is generated by the doctor based on the examination data and the auxiliary diagnosis and treatment results of the disease.

7. The method according to claim 6, characterized in that Before sending the inspection suggestion information, the method further includes: receiving examination modification information input by the doctor according to the examination suggestion information; The inspection suggestion information is modified according to the inspection modification information, and the modified inspection suggestion information is sent.

Images