Method for detecting pneumoconiosis nodules

Abstract

The invention relates to a pneumoconiosis nodule detection method, and belongs to the technical field of pneumoconiosis nodule detection, and the method comprises the following steps: S1, summarizing the detection case information of pneumoconiosis nodules of all hospitals, and importing the model learning case information into a pneumoconiosis nodule detection analysis model; s2, importing the basic information of the patient, the laboratory examination result and the lung function test result into a pneumoconiosis nodule detection and analysis model for comparison; s3, if no hidden danger exists in the examination result, determining that no pneumoconiosis nodule exists; s4, screening and analyzing; and S5, constructing learning information, deleting the person sensitive information of the patient after the diagnosis of the patient is completed, constructing model learning case information, and importing the model learning case information into a pneumoconiosis nodule detection and analysis model for diagnosis and learning. According to the method, the doctor seeing information and the examination reports of pneumoconiosis nodules of all hospitals are summarized to form the case information, the analysis model is constructed to perform mechanical learning on the pathological information, and the information features are extracted, so that doctors are assisted to perform judgment, and the diagnosis efficiency is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of pneumoconiosis nodule detection, and particularly to a method for detecting pneumoconiosis nodules. Background Art

[0002] Pneumoconiosis, also known as dust lung disease, is a disease caused by long-term inhalation of inorganic mineral dust, characterized by diffuse nodular or reticular fibrosis of the lung tissue. The causes are often attributed to occupational or environmental factors, such as silica dust, asbestos dust or carbon black dust, etc. Pneumoconiosis is divided into many types, such as silicosis, coal worker's pneumoconiosis, graphite pneumoconiosis, asbestosis, etc. The incidence factors are related to factors such as dust particle size, dust concentration, and exposure time.

[0003] When diagnosing existing pneumoconiosis nodules, it takes a long time to confirm the diagnosis. Pneumoconiosis nodules are difficult to diagnose in the early stage. To further improve the detection efficiency of pneumoconiosis nodules, a method for detecting pneumoconiosis nodules is proposed. By summarizing the medical visit information and examination reports of diagnosing pneumoconiosis nodules in each hospital to form case information, a component analysis model performs machine learning on the pathological information and extracts information features, thereby assisting doctors in making judgments, improving the diagnosis efficiency, and being able to understand and statistically analyze the medical visit information of patients with confirmed pneumoconiosis nodules, so as to identify high-risk groups and conduct key detections on patients with specific occupational histories, exposure to specific dust types, etc., so as to avoid misdiagnosis while excluding other diseases. The diagnostic detection method ranges from non-invasive to invasive, from basic to advanced, which can reduce the detection cost while being more comprehensive and reduce the misdiagnosis probability. Summary of the Invention

[0004] The present invention provides a method for detecting pneumoconiosis nodules, which solves the problems raised in the above-mentioned background art, further improves the detection efficiency of pneumoconiosis nodules, and the diagnostic detection method ranges from non-invasive to invasive, from basic to advanced, which can reduce the detection cost while being more comprehensive and reduce the misdiagnosis probability.

[0005] The solution of the present invention to the above technical problems is as follows: A method for detecting pneumoconiosis nodules includes the following steps: S1: Summarize the detection case information of pneumoconiosis nodules in each hospital, and form model learning case information from medical visit information, laboratory examination reports, pulmonary function test reports, X-ray chest film reports, high-resolution CT reports, pathological examination reports and result reports. Import the model learning case information into the pneumoconiosis nodule detection analysis model, and the pneumoconiosis nodule detection analysis model performs parameter statistical learning on the confirmed case information;

[0006] S2: The visiting patients fill in basic information and perform inflammatory marker, biomarker, sputum examination and pulmonary function test according to the information. Import the basic information, laboratory examination results and pulmonary function test results of the patients into the pneumoconiosis nodule detection analysis model for comparison, so as to give a judgment result to assist doctors in making a preliminary diagnosis;

[0007] S3: If there are no potential hazards in the inspection results, a judgment of no pneumoconiosis nodules is given. If the potential hazards are relatively small, X-ray chest radiography is continued for preliminary screening. If the potential hazards are relatively large, high-resolution CT is continued for accurate screening;

[0008] S4: If the X-ray chest radiography results are abnormal, with round small shadows or irregular shadows, pathological examination is carried out if necessary to distinguish between lung cancer and tuberculosis for diagnosis. If there is no abnormality, a judgment of no pneumoconiosis nodules is given. If the high-resolution CT results are abnormal, if small nodules, pulmonary interstitial fibrosis and early lesions are shown, pathological examination is carried out if necessary to distinguish between lung cancer and tuberculosis for diagnosis. If there is no abnormality, a judgment of no pneumoconiosis nodules is given;

[0009] S5: After the patient's diagnosis is completed, the sensitive information of the patient can be deleted, and the case information for model learning is constructed and imported into the pneumoconiosis nodule detection and analysis model for diagnostic learning.

[0010] Based on the above technical solutions, the present invention can also be improved as follows.

[0011] Further, the medical treatment information in S1 includes the occupational history of exposure to dust, the type of dust exposure, working years and protection measures, clinical symptoms, gender and age information.

[0012] Further, the basic information in S2 includes the occupational history of exposure to dust, the type of dust exposure, working years and protection measures, clinical symptoms, gender and age information of the patient.

[0013] The beneficial effects of the present invention are as follows: The present invention provides a method for detecting pneumoconiosis nodules, with the following advantages:

[0014] 1. By summarizing the medical treatment information and inspection reports for diagnosing pneumoconiosis nodules in each hospital to form case information, the component analysis model performs machine learning on the pathological information and extracts information features, thereby assisting doctors in making judgments and improving the diagnostic efficiency;

[0015] 2. Understand and count the medical treatment information of patients diagnosed with pneumoconiosis nodules, so as to identify high-risk groups, and conduct key detections on patients with specific occupational histories, exposure to specific types of dust, etc. Thus, while excluding other diseases, misdiagnosis can be avoided. The diagnostic detection method ranges from non-invasive to invasive, from basic to advanced, which can reduce the detection cost while being more comprehensive and reduce the probability of misdiagnosis.

[0016] The above description is only an overview of the technical solution of the present invention. In order to be able to understand the technical means of the present invention more clearly and implement it in accordance with the content of the description, the following takes the preferred embodiments of the present invention and combines with the accompanying drawings to elaborate in detail as follows. The specific implementation manner of the present invention is given in detail by the following embodiments and their accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] The accompanying drawings described herein are used to provide a further understanding of the present invention and form a part of this application. The schematic embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation to the present invention. In the drawings:

[0018] Figure 1 It is a system flowchart of a detection method for pneumoconiosis nodules provided by an embodiment of the present invention;

[0019] Figure 2 It is a learning flowchart of a pneumoconiosis nodule detection and analysis model in a detection method for pneumoconiosis nodules provided by an embodiment of the present invention. Detailed implementation manners

[0020] The principles and features of the present invention will be described below in conjunction with the attached Figure 1-2 The principles and features of the present invention are described below. The examples given are only for explaining the present invention and are not intended to limit the scope of the present invention. In the following paragraphs, the present invention will be described more specifically by way of example with reference to the accompanying drawings. The advantages and features of the present invention will be clearer according to the following description and the claims. It should be noted that the attached drawings are all in a very simplified form and use non-precise scales, and are only used to facilitate and clearly assist in explaining the purpose of the embodiments of the present invention.

[0021] It should be noted that when a component is referred to as being "fixed to" another component, it can be directly on the other component or there can also be an intermediate component. When a component is considered to be "connected to" another component, it can be directly connected to the other component or there may be an intermediate component at the same time. When a component is considered to be "disposed on" another component, it can be directly disposed on the other component or there may be an intermediate component at the same time. The terms "vertical", "horizontal", "left", "right" and similar expressions used herein are only for the purpose of illustration.

[0022] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those of ordinary skill in the technical field to which the present invention belongs. The terms used in the description of the present invention herein are only for the purpose of describing specific embodiments and are not intended to limit the present invention. The term "and / or" used herein includes any and all combinations of one or more of the related listed items.

[0023] As Figure 1-2 shown, the present invention provides a detection method for pneumoconiosis nodules, including the following steps:

[0024] S1: Build a model training file, summarize the detection case information of pneumoconiosis nodules in each hospital, and form model learning case information from the medical treatment information, laboratory test reports, pulmonary function test reports, X-ray chest film reports, high-resolution CT reports, pathological examination reports and result reports. Clean the sensitive information of patients and import the model learning case information into the pneumoconiosis nodule detection and analysis model. The model can extract and summarize and compare the occupational history of dust exposure, type of dust exposure, working years and protective measures, clinical symptoms, gender and age information of patients, so as to clarify the high-risk occupational types, high-risk dust types, high-risk working years and protective measures, high-risk clinical symptoms, high-risk gender and age information. The closer the patient information is to the high-risk model in the model, the higher the risk of pneumoconiosis nodules is judged.

[0025] S2: Conduct a preliminary diagnosis. The visiting patients fill in the basic information and undergo inflammatory markers, biomarker tests, sputum tests and pulmonary function tests according to the information. Import the basic information, laboratory test results and pulmonary function test results of the patients into the pneumoconiosis nodule detection and analysis model for comparison, so as to give a judgment result to assist doctors in making a preliminary diagnosis. The basic information includes the occupational history of dust exposure, type of dust exposure, working years and protective measures, clinical symptoms, gender and age information of the patients.

[0026] S3: Classified screening. If there is no hidden danger in the inspection result, a judgment of no pneumoconiosis nodules is given. If the hidden danger is small, continue with X-ray chest film detection for preliminary screening. If the hidden danger is large, continue with high-resolution CT detection for precise screening.

[0027] S4: Screening analysis. If the X-ray chest film detection result is abnormal, with round small shadows or irregular shadows, pathological examination is carried out if necessary to distinguish lung cancer and tuberculosis for confirmation. If there is no abnormality, a judgment of no pneumoconiosis nodules is given. If the high-resolution CT detection result is abnormal, if small nodules, pulmonary interstitial fibrosis and early lesions are shown, pathological examination is carried out if necessary to distinguish lung cancer and tuberculosis for confirmation. If there is no abnormality, a judgment of no pneumoconiosis nodules is given.

[0028] S5: Build learning information. After the patient's diagnosis is completed, the sensitive information of the patient can be deleted, and the model learning case information is formed and imported into the pneumoconiosis nodule detection and analysis model for diagnostic learning.

[0029] It should be noted that in this article, relational terms such as first and second are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. The content not described in detail in this specification belongs to the prior art well-known to those skilled in the art.

[0030] The above are only the preferred embodiments of the present invention and do not impose any form of limitation on the present invention. Any ordinary technician in the industry can smoothly implement the present invention according to the illustrations in the specification and the above description. However, any minor changes, modifications, and equivalent variations made by those skilled in the art within the scope of the technical solution of the present invention by using the technical content disclosed above are equivalent embodiments of the present invention. At the same time, any changes, modifications, and equivalent variations made to the above embodiments based on the essential technology of the present invention still fall within the scope of protection of the technical solution of the present invention.

Claims

1. A method for detecting pneumoconiosis nodules, characterized in that: The steps include: S1: Summarize the detection case information of pneumoconiosis nodules in various hospitals, and use the medical information, laboratory test reports, pulmonary function test reports, X-ray chest film reports, high-resolution CT reports, pathological examination reports and result reports to form model learning case information, and import the model learning case information into the pneumoconiosis nodule detection and analysis model, and the pneumoconiosis nodule detection and analysis model performs parameter statistical learning on the confirmed case information; S2: The patient fills in basic information and undergoes inflammatory markers, biomarkers, sputum tests and lung function tests based on the information. The patient's basic information, laboratory test results and lung function test results are imported into the pneumoconiosis nodule detection and analysis model for comparison, thereby providing a judgment result to assist doctors in making a preliminary diagnosis; S3: If the examination results show no hidden dangers, a judgment of no pneumoconiosis nodules will be given. If the hidden danger is minor, a chest X-ray examination will be continued for preliminary screening. If the hidden danger is major, a high-resolution CT examination will be continued for precise screening. S4: If the chest X-ray test results are abnormal, such as small round or irregular shadows, pathological examination should be performed to distinguish between lung cancer and tuberculosis to confirm the diagnosis. If there is no abnormality, a diagnosis of no dust lung nodules will be made. If the high-resolution CT test results are abnormal, such as small nodules, pulmonary interstitial fibrosis and early lesions, pathological examination should be performed to distinguish between lung cancer and tuberculosis to confirm the diagnosis. If there is no abnormality, a diagnosis of no dust lung nodules will be made. S5: After the patient is diagnosed, the patient's sensitive personal information can be deleted, and the case information of the model learning can be imported into the pneumoconiosis nodule detection and analysis model for diagnostic learning.

2. The detection method of pneumoconiosis nodules according to claim 1, characterized in that The medical information in S1 includes occupational history of dust exposure, dust exposure type, length of service and protective measures, clinical symptoms, gender and age information.

3. The detection method of pneumoconiosis nodules according to claim 1, characterized in that, The basic information in S2 includes the patient's occupational history of exposure to dust, type of dust exposure, length of service and protective measures, clinical symptoms, gender and age information.

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