CN115458160B - Whole-course intelligent management system, method, equipment and medium for breast tumor patients - Google Patents
Whole-course intelligent management system, method, equipment and medium for breast tumor patients Download PDFInfo
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Abstract
The invention relates to an intelligent whole-course management system, method, equipment and medium for breast tumor patients, wherein the system comprises: the system comprises a preliminary screening module capable of outputting the position and the risk score of the suspected region image, a first pathological diagnosis module capable of diagnosing the suspected region image of the breast tumor with the risk score smaller than a certain threshold value, a second pathological diagnosis module capable of diagnosing the suspected region image of the breast tumor with the risk score not smaller than the certain threshold value or the image pushed by the first pathological diagnosis module, a treatment scheme output module capable of outputting a treatment scheme and a follow-up interaction module capable of interacting with a patient and constructing a three-dimensional patient model. By the method, screening precision and diagnosis and treatment efficiency are improved, corresponding treatment schemes are provided for patients in time, and the possibility of further deterioration of the conditions of the patients is reduced. Meanwhile, after intervention, the disease progress of the patient and various indexes of the body of the patient can be obtained in real time, and the three-dimensional model is displayed to the patient, so that the follow-up visit of the patient is timely reminded.
Description
Technical Field
The invention relates to the technical field of medical data processing, in particular to an intelligent whole-course management system, method, equipment and medium for breast tumor patients.
Background
Unlike other cancers, breast cancer has its specificity, and patient survival rate can be significantly improved by early screening and treatment.
Meanwhile, with the continuous updating and iteration of various screening modes and detection devices, the layered detection images and data bring more accurate disease information to medical staff, and simultaneously bring a large amount of distinguishing and processing work, which is a great challenge to the existing medical staff.
Therefore, in order to reduce the burden of medical staff and improve the efficiency and the identification accuracy, it is necessary to establish an intelligent management system capable of automatically running in the whole course of a breast tumor patient.
Disclosure of Invention
First, the technical problem to be solved
In view of the above-mentioned shortcomings and disadvantages of the prior art, the present invention provides a system, a method, a device and a medium for whole-course intelligent management of breast tumor patients, which solve the technical problem that the existing medical treatment does not provide an intelligent management scheme capable of whole-course automatic operation for treating breast tumors.
(II) technical scheme
In order to achieve the above purpose, the main technical scheme adopted by the invention comprises the following steps:
in a first aspect, an embodiment of the present invention provides an intelligent whole-course management system for a breast tumor patient, including:
the preliminary screening module is used for traversing the imaging examination image of each patient in a preset database, and outputting the position of the suspected region image and the risk score determined by extracting the image features when the suspected region of the breast tumor is identified;
the first pathological diagnosis module is used for outputting a diagnosis following suggestion report or pushing the diagnosis following suggestion report to a next pathological diagnosis module for processing according to palpation information of a patient and/or audit feedback information of medical staff called from a database aiming at a breast tumor suspected region image with a risk score smaller than a certain threshold value;
the second pathological diagnosis module is used for outputting pathological results according to one or more data among palpation information of a patient, audit feedback information of medical staff, suspected region images, gene detection results and histopathological examination results, which are called from a preset database, aiming at the images of the suspected regions of the breast tumor with the risk score not smaller than a certain threshold value or the images pushed by the first pathological diagnosis module;
a treatment plan output module for determining at least one disease category based on the pathology results and outputting a treatment plan in combination with patient information and related past treatment cases recalled from a database;
and the follow-up interaction module is used for establishing a three-dimensional patient model according to the follow-up advice report or the prognosis sign index of the patient obtained through follow-up, so that the patient can obtain the disease development condition at any time.
Optionally, the preliminary screening module includes:
the tumor suspected region identification unit is used for traversing the imaging examination image of each patient and recording the position of the suspected region image when the breast tumor suspected region is identified;
the feature extraction unit is used for extracting key features on the image of the suspected region of the breast tumor;
and the risk determining unit is used for obtaining a risk score according to the key characteristics through a pre-trained risk prediction model.
Alternatively, the process may be carried out in a single-stage,
the imaging examination image is image data acquired by CT, PET, magnetic resonance or ultrasonic imaging equipment;
the key features include: morphology, margin, maximum diameter, aspect ratio, resistance index, peak systolic flow rate, substantial echo, cystic changes, calcification lesions, and blood flow signals;
the risk prediction model is as follows: support vector machine, neural network, bayesian network, and decision tree.
Optionally, the first pathological diagnosis module includes:
the palpation information calling unit is used for calling palpation information of a patient from a preset database;
the first pushing unit is used for pushing the breast tumor suspected region image with the risk score smaller than a certain threshold to an operation end of a medical staff to acquire auditing feedback information of the medical staff;
the report output unit is used for outputting a follow-up advice report when the palpation information and the audit feedback information of the patient indicate benign conclusions;
and the second pushing unit is used for pushing the palpation information and the audit feedback information of the patient to the next-stage pathological diagnosis module for processing when at least one item of palpation information and audit feedback information of the patient indicates a non-benign conclusion.
Optionally, the second pathological diagnosis module includes:
the multi-information calling and processing unit is used for calling one or more data among palpation information of a patient, audit feedback information of medical staff, tumor suspected region images, gene detection results and histopathological examination results from a preset database and carrying out normalization processing;
the multi-factor regression analysis unit is used for obtaining a plurality of risk factors through Logistic regression analysis on the palpation information of the normalized patient, the audit feedback information of medical staff, the tumor suspected region image, the gene detection result and the histopathological examination result;
the pathology result output unit is used for comparing each risk factor with a preset threshold value to obtain a pathology result;
wherein the pathological outcome comprises one or more data of definite age, tumor type, tumor stage, tumor size, molecular typing, regional lymph node metastasis, distant metastasis.
Optionally, the treatment plan output module includes:
a disease classification unit for determining at least one disease category based on the pathology results;
a related treatment case calling unit for calling related treatment case set from the database according to disease category
The method comprises the steps of carrying out a first treatment on the surface of the Wherein, the liquid crystal display device comprises a liquid crystal display device,x n is the firstnCase of item therapy>
，
For several characteristic data for each treatment case,iand (3) withnAre all positive integers;
a quantization processing unit for performing quantization processing on the current disease category and the patient information to form a current case
；
A treatment case screening unit for calculating current case
And related treatment case set->
Every treatment case->
Distance value of (2);
a treatment plan output unit for taking the treatment case with the smallest distance value as the treatment plan at this time;
wherein, the treatment cases are stored in a preset database in a collective form in a clustering mode;
and the number of feature data includes: age, height, weight, combined disease, past history, family tumor history, adverse drug reactions, economic status, treatment waiting time, treatment effect, treatment side effects, treatment duration, and economic costs.
Optionally, the follow-up interaction module includes:
a follow-up unit for obtaining the prognosis sign index of the patient through follow-up;
the three-dimensional model building unit is used for building a three-dimensional patient model capable of simulating daily activities of a patient;
and the importing display unit is used for importing data in the follow-up advice report or the prognosis sign index into the virtual module and displaying the abnormal label at the corresponding position of the model.
In a second aspect, an embodiment of the present invention provides a method for whole-course intelligent management of a breast tumor patient, including:
traversing an imaging examination image of each patient in a preset database, and outputting the suspected region image position and the risk score determined by extracting image features when the suspected region of the breast tumor is identified;
outputting a follow-up advice report or pushing the follow-up advice report to a next-stage pathology diagnosis module for processing according to palpation information of a patient and/or audit feedback information of medical staff called from a database aiming at a breast tumor suspected region image with risk score smaller than a certain threshold value;
outputting a pathology result according to one or more data among palpation information of a patient, audit feedback information of medical staff, suspected region images, gene detection results and histopathology examination results which are called from a preset database aiming at the breast tumor suspected region images with risk scores not smaller than a certain threshold value or the images pushed by the first pathology diagnosis module;
determining at least one disease category based on the pathology results and outputting a treatment plan in combination with patient information and related past treatment cases recalled from a database;
and establishing a three-dimensional patient model according to the follow-up advice report or the prognosis sign index of the patient obtained through follow-up, so that the patient can obtain the disease development condition at any time.
In a third aspect, an embodiment of the present invention provides an intelligent whole-course management apparatus for a breast tumor patient, including: at least one processor; and a memory communicatively coupled to the at least one processor; wherein the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method steps of a method for intelligent management of a breast tumor patient throughout the course as described above.
In a fourth aspect, an embodiment of the present invention provides a computer readable storage medium having stored thereon computer executable instructions, wherein the executable instructions when executed by a processor implement the steps of a method for intelligently managing the whole course of a breast tumor patient as described above.
(III) beneficial effects
The invention provides an intelligent management scheme for omnibearing automatic processing from the discovery of breast tumor to the intervention of the whole process, which not only improves screening precision and diagnosis and treatment efficiency, but also provides a treatment scheme for patients in time and reduces the possibility of further deterioration of the illness state of the patients. Meanwhile, after intervention, the disease progress of the patient and various indexes of the body of the patient can be obtained in real time, the three-dimensional model is displayed to the patient, the patient is reminded of follow-up visit in time, and anxiety and bad state of the patient are relieved.
Drawings
Fig. 1 is a schematic diagram of a whole-course intelligent management system for breast tumor patients according to an embodiment of the present invention;
fig. 2 is a flow chart of a method for whole-course intelligent management of breast tumor patients according to an embodiment of the invention.
Detailed Description
The invention will be better explained for understanding by referring to the following detailed description of the embodiments in conjunction with the accompanying drawings.
As shown in fig. 1, the embodiment of the invention provides an intelligent whole-course management system for a breast tumor patient, which comprises: the preliminary screening module is used for traversing the imaging examination image of each patient in the preset database, and outputting the suspected region image position and the risk score determined by extracting the image features when the suspected region of the breast tumor is identified. The first pathological diagnosis module is used for outputting a diagnosis following suggestion report or pushing the diagnosis following suggestion report to the next pathological diagnosis module for processing according to palpation information of a patient and/or audit feedback information of medical staff called from a database aiming at the breast tumor suspected region image with the risk score smaller than a certain threshold value. The second pathological diagnosis module is used for outputting pathological results according to one or more data among palpation information of a patient, audit feedback information of medical staff, suspected region images, gene detection results and histopathological examination results, which are called from a preset database, aiming at the images of the suspected regions of the breast tumor with the risk score not smaller than a certain threshold value or the images pushed by the first pathological diagnosis module. The treatment plan output module determines at least one disease category based on the pathology results and outputs a treatment plan in combination with the patient information and the related past treatment cases recalled from the database. And the follow-up interaction module is used for establishing a three-dimensional patient model according to the follow-up advice report or the prognosis sign index of the patient acquired through follow-up, so that the patient can acquire the disease development condition at any time.
The invention provides an intelligent management scheme for omnibearing automatic processing from the discovery of breast tumor to the intervention of the whole process, which not only improves screening precision and diagnosis and treatment efficiency, but also provides a treatment scheme for patients in time and reduces the possibility of further deterioration of the illness state of the patients. Meanwhile, after intervention, the disease progress of the patient and various indexes of the body of the patient can be obtained in real time, the three-dimensional model is displayed to the patient, the patient is reminded of follow-up visit in time, and anxiety and bad state of the patient are relieved.
In order to better understand the above technical solution, exemplary embodiments of the present invention will be described in more detail below with reference to the accompanying drawings. While exemplary embodiments of the present invention are shown in the drawings, it should be understood that the present invention may be embodied in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the invention to those skilled in the art.
Further, the preliminary screening module includes:
and the tumor suspected region identification unit is used for traversing the imaging examination image of each patient and recording the position of the suspected region image when the breast tumor suspected region is identified.
And the feature extraction unit is used for extracting key features from the image of the suspected region of the breast tumor.
And the risk determining unit is used for obtaining a risk score according to the key characteristics through a pre-trained risk prediction model. Wherein, the imaging examination image is image data acquired by CT, PET, magnetic resonance or ultrasonic imaging equipment; the key features include: quantitative index: maximum diameter, aspect ratio, resistance index, and peak systolic flow rate, qualitative indicators including morphology, edge, substantial echo, cystic changes, calcification foci, and blood flow signals; the risk prediction model is as follows: support vector machine, neural network, bayesian network, and decision tree.
Considering that the problems of large workload and inaccurate positioning exist in the manual segmentation of the suspected breast tumor area by doctors, and the delineated tumor boundaries can have certain differences caused by the difference of experience by different doctors, the accuracy of image identification is obviously influenced subjectivity. Therefore, a segmentation algorithm such as a clustering-based segmentation method is adopted and expert priori rules are combined, so that a segmentation result similar to manual sketching can be obtained. Then, the image characteristics of the breast tumor are quantified, and the suspected region of the breast tumor is described from the aspects of the morphology, edge, maximum diameter, aspect ratio value, resistance index, peak flow rate in the shrinkage period, substantial echo, cystic change, calcification foci, blood flow signals and the like of the breast tumor, so that a solid foundation is laid for subsequent diagnosis.
Second, the first pathology diagnosis module includes:
the palpation information calling unit is used for calling palpation information of a patient from a preset database;
the first pushing unit is used for pushing the breast tumor suspected region image with the risk score smaller than a certain threshold to an operation end of a medical staff to acquire auditing feedback information of the medical staff;
the report output unit is used for outputting a follow-up advice report when the palpation information and the audit feedback information of the patient indicate benign conclusions;
and the second pushing unit is used for pushing the palpation information and the audit feedback information of the patient to the next-stage pathological diagnosis module for processing when at least one item of palpation information and audit feedback information of the patient indicates a non-benign conclusion.
Next, a second pathology diagnosis module includes:
the multi-information calling and processing unit is used for calling one or more data among palpation information of a patient, audit feedback information of medical staff, tumor suspected region images, gene detection results and histopathological examination results from a preset database and carrying out normalization processing;
the multi-factor regression analysis unit is used for obtaining a plurality of risk factors through Logistic regression analysis on the palpation information of the normalized patient, the audit feedback information of medical staff, the tumor suspected region image, the gene detection result and the histopathological examination result;
the pathology result output unit is used for comparing each risk factor with a preset threshold value to obtain a pathology result;
among other things, pathological results include one or more data specifying age, tumor type, tumor stage, tumor size, molecular typing, regional lymph node metastasis, distant metastasis.
In the embodiment of the invention, the two-stage pathological diagnosis module is adopted to process the breast tumor data with different risk degrees in a grading way, which not only quickens the processing efficiency and the processing flow, but also saves medical resources, simultaneously adopts Logistic regression analysis to acquire main risk factors for cases with higher risks,
later personalized interventions were carried out against the main influencing factors.
Furthermore, the treatment plan output module includes:
a disease classification unit for determining at least one disease category based on the pathology results;
a related treatment case calling unit for calling related treatment case set from the database according to disease category
Wherein, the method comprises the steps of, wherein,x n is the firstnCase of item therapy>
，
For several characteristic data for each treatment case,iand (3) withnAre all positive integers; wherein the plurality of feature data includes: age, height, weight, combined disease, prior history, family history of tumors, adverse drug reactions, economic status, treatment waiting time, treatment effect, treatment side effects, treatment duration, and economic costsOne or more data.
A quantization processing unit for performing quantization processing on the current disease category and the patient information to form a current case
；
A treatment case screening unit for calculating current case
And related treatment case set->
Every treatment case->
Distance value of (2);
and the treatment scheme output unit is used for taking the treatment case with the smallest distance value as the treatment scheme.
In the above treatment plan output module, firstly, the treatment cases are stored in the preset data in a set form by a clustering mode; and secondly, calculating the distance between the current case and each case in the called related treatment case set, so as to judge the closest treatment case, and obtaining the treatment scheme which is most suitable for the current condition of the patient.
Then, the follow-up interaction module includes:
a follow-up unit for obtaining the prognosis sign index of the patient through follow-up;
the three-dimensional model building unit is used for building a three-dimensional patient model capable of simulating daily activities of a patient;
and the importing display unit is used for importing data in the follow-up advice report or the prognosis sign index into the virtual module and displaying the abnormal label at the corresponding position of the model.
In the embodiment of the invention, the current affected part condition of the patient is displayed by adopting the three-dimensional model, meanwhile, the current affected part condition can be updated in real time, and the affected part is marked on the model in a conspicuous mode, so that the patient can conveniently know the condition in time and follow up the diagnosis through the handheld client, and meanwhile, medical staff can master the disease development condition at any time.
In addition, the embodiment of the invention also provides a whole-course intelligent management method for the breast tumor patient, as shown in fig. 2, comprising the following steps:
s1, traversing an imaging examination image of each patient in a preset database, and outputting the suspected region image position and the risk score determined by extracting image features when a breast tumor suspected region is identified.
S2, outputting a follow-up advice report or pushing the follow-up advice report to a next-stage pathological diagnosis module for processing according to palpation information of a patient and/or audit feedback information of medical staff called from a database aiming at the breast tumor suspected region image with the risk score smaller than a certain threshold value.
S3, outputting a pathology result according to one or more data among palpation information of a patient, audit feedback information of medical staff, suspected region images, gene detection results and histopathology examination results, which are called from a preset database, aiming at the images of the suspected region of the breast tumor with the risk score not smaller than a certain threshold value or the images pushed by the first pathology diagnosis module.
S4, determining at least one disease category based on the pathological result, and outputting a treatment scheme by combining the patient information and the related treatment cases called from the database.
S5, establishing a three-dimensional patient model according to the follow-up advice report or the prognosis sign index of the patient obtained through follow-up, so that the patient can obtain the disease development condition at any time.
Meanwhile, the invention provides an intelligent whole-course management device for breast tumor patients, which comprises: at least one processor; and a memory communicatively coupled to the at least one processor; wherein the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method steps of a method of intelligent management of a breast tumor patient throughout the course as described above; and a computer readable storage medium having stored thereon computer executable instructions which when executed by a processor implement the steps of an intelligent management method for a breast tumor patient throughout the course as described above.
In summary, the invention provides an intelligent management system, method, device and medium for the whole course of a breast tumor patient, which are provided with a preliminary screening module capable of outputting the position and risk score of a suspected region image, a first pathological diagnosis module capable of diagnosing the suspected region image of the breast tumor with the risk score smaller than a certain threshold value, a second pathological diagnosis module capable of diagnosing the suspected region image of the breast tumor with the risk score not smaller than the certain threshold value or the image pushed by the first pathological diagnosis module, a treatment scheme output module capable of outputting a treatment scheme based on the output treatment scheme and a follow-up interaction module capable of interacting with the patient and constructing a three-dimensional patient model. Aiming at breast malignant tumors, the invention provides a full-process intelligent management scheme from discovery, diagnosis, intervention to follow-up, so that patients can screen symptoms in time, medical staff can intervene the symptoms in time, the method has great significance on the life cycle of breast tumor patients, the emotion of the patients is stabilized, the improvement of the illness state is promoted from the side, and the cure rate is improved.
Since the system/device described in the foregoing embodiments of the present invention is a system/device used for implementing the method of the foregoing embodiments of the present invention, those skilled in the art will be able to understand the specific structure and modification of the system/device based on the method of the foregoing embodiments of the present invention, and thus will not be described in detail herein. All systems/devices used in the methods of the above embodiments of the present invention are within the scope of the present invention.
It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions.
It should be noted that in the claims, any reference signs placed between parentheses shall not be construed as limiting the claim. The word "comprising" does not exclude the presence of elements or steps not listed in a claim. The word "a" or "an" preceding an element does not exclude the presence of a plurality of such elements. The invention may be implemented by means of hardware comprising several distinct elements, and by means of a suitably programmed computer. In the claims enumerating several means, several of these means may be embodied by one and the same item of hardware. The use of the terms first, second, third, etc. are for convenience of description only and do not denote any order. These terms may be understood as part of the component name.
Furthermore, it should be noted that in the description of the present specification, the terms "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., refer to a specific feature, structure, material, or characteristic described in connection with the embodiment or example being included in at least one embodiment or example of the present invention. In this specification, schematic representations of the above terms are not necessarily directed to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples. Furthermore, the different embodiments or examples described in this specification and the features of the different embodiments or examples may be combined and combined by those skilled in the art without contradiction.
While preferred embodiments of the present invention have been described, additional variations and modifications in those embodiments may occur to those skilled in the art upon learning the basic inventive concepts. Therefore, the appended claims should be construed to include preferred embodiments and all such variations and modifications as fall within the scope of the invention.
It will be apparent to those skilled in the art that various modifications and variations can be made to the present invention without departing from the spirit or scope of the invention. Thus, the present invention should also include such modifications and variations provided that they come within the scope of the following claims and their equivalents.
Claims (7)
1. An intelligent whole-course management system for a breast tumor patient, which is characterized by comprising:
the preliminary screening module is used for traversing the imaging examination image of each patient in a preset database, and outputting the position of the suspected region image and the risk score determined by extracting the image features when the suspected region of the breast tumor is identified;
the first pathological diagnosis module is used for outputting a diagnosis following suggestion report or pushing the diagnosis following suggestion report to a next pathological diagnosis module for processing according to palpation information of a patient and/or audit feedback information of medical staff called from a database aiming at a breast tumor suspected region image with a risk score smaller than a certain threshold value;
the second pathological diagnosis module is used for outputting pathological results according to one or more data among palpation information of a patient, audit feedback information of medical staff, a breast tumor suspected region image, a gene detection result and a histopathological examination result, which are called from a preset database, aiming at the breast tumor suspected region image with the risk score not smaller than a certain threshold value or the image pushed by the first pathological diagnosis module;
a treatment plan output module for determining at least one disease category based on the pathology results and outputting a treatment plan in combination with patient information and related past treatment cases recalled from a database;
the follow-up interaction module is used for establishing a three-dimensional patient model according to the follow-up advice report or the prognosis sign index of the patient obtained through follow-up, so that the patient can obtain the disease development condition at any time;
the second pathology diagnosis module includes:
the multi-information calling and processing unit is used for calling one or more data among palpation information of a patient, audit feedback information of medical staff, a breast tumor suspected region image, a gene detection result and a histopathological examination result from a preset database and carrying out normalization processing;
the multi-factor regression analysis unit is used for obtaining a plurality of risk factors through Logistic regression analysis on the palpation information of the normalized patient, the audit feedback information of medical staff, the suspected region image of the breast tumor, the gene detection result and the histopathological examination result;
the pathology result output unit is used for comparing each risk factor with a preset threshold value to obtain a pathology result;
wherein the pathological outcome comprises one or more data of definite age, tumor type, tumor stage, tumor size, molecular typing, regional lymph node metastasis, distant metastasis;
the treatment plan output module includes:
a disease classification unit for determining at least one disease category based on the pathology results;
a related treatment case calling unit for calling related treatment case set from the database according to disease category
The method comprises the steps of carrying out a first treatment on the surface of the Wherein (1)>
Is the firstnIn the case of the treatment of the item,
for several characteristic data for each treatment case,iand (3) withnAre all positive integers;
a quantization processing unit for performing quantization processing on the current disease category and the patient information to form a current case
；
A treatment case screening unit for calculating current case
And related treatment case set->
Every treatment case->
Distance value of (2);
a treatment plan output unit for taking the treatment case with the smallest distance value as the treatment plan at this time;
wherein, the treatment cases are stored in a preset database in a collective form in a clustering mode; and the number of feature data includes: age, height, weight, combined disease, past history, family history of tumors, adverse drug reactions, economic status, treatment waiting time, treatment effect, treatment side effects, treatment duration, and economic costs;
and, the follow-up interaction module comprises:
a follow-up unit for obtaining the prognosis sign index of the patient through follow-up;
the three-dimensional model building unit is used for building a three-dimensional patient model for simulating daily activities of the patient;
and the importing display unit is used for importing data in the follow-up advice report or the prognosis sign index into the virtual module and displaying the abnormal label at the corresponding position of the model.
2. The full-scale intelligent management system of a breast tumor patient according to claim 1, wherein the preliminary screening module comprises:
the tumor suspected region identification unit is used for traversing the imaging examination image of each patient and recording the position of the suspected region image when the breast tumor suspected region is identified;
the feature extraction unit is used for extracting key features on the image of the suspected region of the breast tumor;
and the risk determining unit is used for obtaining a risk score according to the key characteristics through a pre-trained risk prediction model.
3. The whole course intelligent management system for breast tumor patients according to claim 2, wherein,
the imaging examination image is image data acquired by CT, PET, magnetic resonance or ultrasonic imaging equipment;
the key features include: morphology, margin, maximum diameter, aspect ratio, resistance index, peak systolic flow rate, substantial echo, cystic changes, calcification lesions, and blood flow signals;
the risk prediction model is as follows: support vector machine, neural network, bayesian network, and decision tree.
4. The global intelligent management system for a breast tumor patient according to claim 1, wherein the first pathological diagnosis module comprises:
the palpation information calling unit is used for calling palpation information of a patient from a preset database;
the first pushing unit is used for pushing the breast tumor suspected region image with the risk score smaller than a certain threshold to an operation end of a medical staff to acquire auditing feedback information of the medical staff;
the report output unit is used for outputting a follow-up advice report when the palpation information and the audit feedback information of the patient indicate benign conclusions;
and the second pushing unit is used for pushing the palpation information and the audit feedback information of the patient to the next-stage pathological diagnosis module for processing when at least one item of palpation information and audit feedback information of the patient indicates a non-benign conclusion.
5. An intelligent whole-course management method for a breast tumor patient is characterized by comprising the following steps:
traversing an imaging examination image of each patient in a preset database, and outputting the suspected region image position and the risk score determined by extracting image features when the suspected region of the breast tumor is identified;
outputting a follow-up advice report or pushing the follow-up advice report to a next-stage pathology diagnosis module for processing according to palpation information of a patient and/or audit feedback information of medical staff called from a database aiming at a breast tumor suspected region image with risk score smaller than a certain threshold value;
outputting a pathology result according to one or more data among palpation information of a patient, audit feedback information of medical personnel, a breast tumor suspected region image, a gene detection result and a histopathology examination result, which are acquired from a preset database, aiming at the breast tumor suspected region image with the risk score not smaller than a certain threshold value or an image pushed by a previous pathology diagnosis module;
determining at least one disease category based on the pathology results and outputting a treatment plan in combination with patient information and related past treatment cases recalled from a database;
establishing a three-dimensional patient model according to the follow-up advice report or the prognosis sign index of the patient obtained through follow-up, so that the patient can obtain the disease development condition at any time;
outputting a pathology result depending on a second pathology diagnosis module for implementation according to one or more data among palpation information of a patient, audit feedback information of medical staff, a breast tumor suspected region image, a gene detection result and a histopathology examination result, wherein the palpation information is called from a preset database, aiming at the image of the breast tumor suspected region with the risk score not smaller than a certain threshold value or the image pushed by the upper-level pathology diagnosis module, wherein the second pathology diagnosis module comprises:
the multi-information calling and processing unit is used for calling one or more data among palpation information of a patient, audit feedback information of medical staff, a breast tumor suspected region image, a gene detection result and a histopathological examination result from a preset database and carrying out normalization processing;
the multi-factor regression analysis unit is used for obtaining a plurality of risk factors through Logistic regression analysis on the palpation information of the normalized patient, the audit feedback information of medical staff, the suspected region image of the breast tumor, the gene detection result and the histopathological examination result;
the pathology result output unit is used for comparing each risk factor with a preset threshold value to obtain a pathology result;
wherein the pathological outcome comprises one or more data of definite age, tumor type, tumor stage, tumor size, molecular typing, regional lymph node metastasis, distant metastasis;
determining at least one disease category based on the pathology results and implementing in combination with patient information and previously related treatment cases recalled from a database an output treatment plan dependent treatment plan output module comprising:
a disease classification unit for determining at least one disease category based on the pathology results;
a related treatment case calling unit for calling related treatment case set from the database according to disease category
The method comprises the steps of carrying out a first treatment on the surface of the Wherein (1)>
Is the firstnIn the case of the treatment of the item,
for several characteristic data for each treatment case,iand (3) withnAre all positive integers;
a quantization processing unit for performing quantization processing on the current disease category and the patient information to form a current case
；
A treatment case screening unit for calculating current case
And related treatment case set->
Every treatment case->
Distance value of (2);
a treatment plan output unit for taking the treatment case with the smallest distance value as the treatment plan at this time;
wherein, the treatment cases are stored in a preset database in a collective form in a clustering mode; and the number of feature data includes: age, height, weight, combined disease, past history, family history of tumors, adverse drug reactions, economic status, treatment waiting time, treatment effect, treatment side effects, treatment duration, and economic costs;
and establishing a three-dimensional patient model according to the follow-up advice report or the prognosis sign index of the patient acquired through follow-up, so that the patient can acquire the disease development condition at any time and rely on the follow-up interaction module for implementation, wherein the follow-up interaction module comprises:
a follow-up unit for obtaining the prognosis sign index of the patient through follow-up;
the three-dimensional model building unit is used for building a three-dimensional patient model for simulating daily activities of the patient;
and the importing display unit is used for importing data in the follow-up advice report or the prognosis sign index into the virtual module and displaying the abnormal label at the corresponding position of the model.
6. An intelligent whole-course management device for a breast tumor patient, which is characterized by comprising: at least one processor; and a memory communicatively coupled to the at least one processor; wherein the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method steps of a breast tumor patient whole-course intelligent management method according to claim 5.
7. A computer readable storage medium having stored thereon computer executable instructions which when executed by a processor perform the steps of a method for the global intelligent management of breast cancer patients according to claim 5.
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