CN112820398A - Artificial intelligence-based lung diagnosis auxiliary system and method - Google Patents
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Abstract
The system comprises a lung image input module, a lung diagnosis auxiliary module and a lung diagnosis suggestion output module, wherein the lung image input module receives an input lung image of a patient to be diagnosed; the lung diagnosis auxiliary module is used for comparing the lung picture of the patient to be diagnosed with the lung picture library in the target lung training model to obtain lung details; and the lung diagnosis suggestion output module outputs diagnosis suggestions corresponding to the lung details, wherein the diagnosis suggestions comprise lung diagnosis information corresponding to the lung patient picture with the highest similarity to the lung picture of the patient to be diagnosed and lung abnormality reason information corresponding to the difference characteristic compared with the lung health picture. The diagnosis of the doctor can be assisted, the missed diagnosis or misdiagnosis of the lung disease caused by inconsistent technical levels of the doctor is reduced, and the accuracy of judgment of the lung disease is improved.
Description
Technical Field
The application relates to the technical field of intelligent medical treatment, in particular to a lung diagnosis auxiliary system and a lung diagnosis auxiliary method based on artificial intelligence.
Background
At present, when a new coronary epidemic situation or a daily lung disease is diagnosed, a lung medical treatment scheme is to obtain a lung picture through detection equipment, and then a doctor analyzes the characteristics of the lung picture to judge the lung disease of a patient.
At this time, the medical technology of the doctor becomes a key factor for judging the lung disease condition; because the diagnosis levels of different doctors are different, missed diagnosis or misdiagnosis can occur for the same lung picture.
In addition, with the wide and deep application of the artificial intelligence technology, the deep learning technology of the artificial intelligence can be combined to participate in the lung disease diagnosis process, the condition of missed diagnosis or misdiagnosis of the lung disease caused by inconsistent technical levels of doctors is reduced, and an auxiliary diagnosis system is provided for the doctors.
The above is only for the purpose of assisting understanding of the technical solutions of the present application, and does not represent an admission that the above is prior art.
Disclosure of Invention
In order to solve the above problems, the present application provides an artificial intelligence-based lung diagnosis assistance system, which inputs a lung image to be analyzed into a lung training model for comparison and analysis, and outputs a reference opinion corresponding to the lung image; therefore, diagnosis by doctors is assisted, missed diagnosis or misdiagnosis of lung diseases caused by inconsistent technical levels of doctors is reduced, and the accuracy of judgment of the lung diseases is improved.
The first aspect of the application discloses an artificial intelligence based lung diagnosis assisting system, which comprises a lung picture input module, a lung diagnosis assisting module and a lung diagnosis suggestion output module, wherein,
the lung image input module receives an input lung image of a patient to be diagnosed;
the lung diagnosis auxiliary module is used for comparing the lung picture of the patient to be diagnosed with the lung picture library in the target lung training model to obtain lung details; the lung picture library comprises a plurality of lung health pictures and a plurality of types of lung patient pictures, and the lung details comprise the lung patient picture with the highest similarity with the lung picture of the patient to be diagnosed and the difference characteristics compared with the lung health pictures;
the lung diagnosis suggestion output module is used for outputting diagnosis suggestions corresponding to the lung details, wherein the diagnosis suggestions comprise lung diagnosis information corresponding to a lung patient picture with the highest similarity to a lung picture of a patient to be diagnosed and lung abnormality reason information corresponding to difference characteristics compared with the lung health picture.
In one possible embodiment, the lung features include a lung density feature, a lung texture feature, and a lung shape feature.
In one possible embodiment, the lung diagnosis assistance module comprises a lung diagnosis model training unit, wherein the lung diagnosis model training unit is configured to perform the following steps:
inputting the lung picture to be deeply learned into an initial lung training model, outputting training lung details and outputting a diagnosis suggestion corresponding to the training lung details; the lung pictures to be deeply learned comprise lung health pictures and lung disease pictures;
comparing the similarity of the training lung diagnosis advice and the target lung diagnosis advice; the target lung diagnosis suggestion comprises lung diagnosis information corresponding to a lung patient picture with the highest similarity to a lung picture to be deeply learned and lung abnormal reason information corresponding to the difference characteristic compared with the lung health picture;
and if the similarity is lower than a first preset similarity threshold, adding the lung picture to be deeply learned and the corresponding target lung diagnosis suggestion into the initial lung training model, and repeating the training process.
In one possible embodiment, the lung diagnosis model training unit is further configured to perform the following steps: if the similarity is greater than or equal to a first preset similarity threshold, finishing training of the initial lung training model, and obtaining an intermediate lung training model;
adjusting the first preset similarity threshold value to be a second preset similarity threshold value; wherein the second preset similarity threshold is greater than the first preset similarity threshold;
and repeating the training process according to the middle lung training model.
In one possible embodiment, the lung diagnosis model training unit is further configured to perform the following steps: and when the second preset similarity threshold is adjusted to be a target preset similarity threshold, and the similarity is greater than or equal to the target preset similarity threshold, obtaining a target lung training model.
In a second aspect, the present application discloses an artificial intelligence based lung diagnosis assistance method, applied in the system as described above, the method comprising:
receiving an input lung picture of a patient to be diagnosed;
comparing the lung picture of the patient to be diagnosed with the lung picture library in the target lung training model to obtain lung details; the lung picture library comprises a plurality of lung health pictures and a plurality of types of lung patient pictures, and the lung details comprise the lung patient picture with the highest similarity with the lung picture of the patient to be diagnosed and the difference characteristics compared with the lung health pictures;
and outputting a diagnosis suggestion corresponding to the lung details, wherein the diagnosis suggestion comprises lung diagnosis information corresponding to a lung patient picture with the highest similarity to a lung picture of a patient to be diagnosed and lung abnormality reason information corresponding to difference characteristics compared with the lung health picture.
In one possible implementation, the electronic device includes a memory and a processor. The memory and the processor communicate with each other via a bus, the memory storing program instructions executable by the processor, the processor invoking the program instructions to perform the method as described above.
In one possible implementation, the computer storage medium stores a computer program that, when executed by a computer processor, implements the method as described above.
The lung images to be analyzed are input into a lung training model for comparison and analysis, and reference opinions corresponding to the lung images are output; therefore, diagnosis by doctors is assisted, missed diagnosis or misdiagnosis of lung diseases caused by inconsistent technical levels of doctors is reduced, and the accuracy of judgment of the lung diseases is improved.
Drawings
The accompanying drawings, which are included to provide a further understanding of the application and are incorporated in and constitute a part of this application, illustrate embodiment(s) of the application and together with the description serve to explain the application and not to limit the application.
FIG. 1 is a schematic diagram of an artificial intelligence-based lung diagnosis assistance system;
fig. 2 is a schematic flow chart of a lung diagnosis assisting method based on artificial intelligence.
Detailed Description
In order to more clearly explain the overall concept of the present application, the following detailed description is given by way of example in conjunction with the accompanying drawings.
The terms "first," "second," and the like in the description and in the claims of the present application and in the above-described drawings are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It is to be understood that the order of such use may be interchanged under appropriate circumstances such that embodiments of the invention described herein may be practiced in other orders than those illustrated or described herein.
Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
The specification discloses an artificial intelligence based lung diagnosis assisting system which comprises a lung picture input module, a lung diagnosis assisting module and a lung diagnosis suggestion output module.
The lung image input module receives an input lung image of a patient to be diagnosed;
the lung diagnosis auxiliary module is used for comparing the lung picture of the patient to be diagnosed with the lung picture library in the target lung training model to obtain lung details; the lung picture library comprises a plurality of lung health pictures and a plurality of types of lung patient pictures, and the lung details comprise the lung patient picture with the highest similarity with the lung picture of the patient to be diagnosed and the difference characteristics compared with the lung health pictures;
the lung diagnosis suggestion output module is used for outputting diagnosis suggestions corresponding to the lung details, wherein the diagnosis suggestions comprise lung diagnosis information corresponding to a lung patient picture with the highest similarity to a lung picture of a patient to be diagnosed and lung abnormality reason information corresponding to difference characteristics compared with the lung health picture.
At the moment, the doctor can refer to the diagnosis suggestion, reduce the missed diagnosis probability of the lung diseases, and further improve the accuracy of the diagnosis of the lung diseases of the doctor.
In one example, the lung features include a lung density feature, a lung texture feature, and a lung shape feature.
It should be noted that the features recited in the present specification are conventional features, and the lung features may be increased or decreased according to actual needs; one lung feature is not listed here.
In one example, the lung diagnosis assistance module comprises a lung diagnosis model training unit, wherein the lung diagnosis model training unit is configured to perform the following steps:
inputting the lung picture to be deeply learned into an initial lung training model, outputting training lung details and outputting a diagnosis suggestion corresponding to the training lung details; the lung pictures to be deeply learned comprise lung health pictures and lung disease pictures;
comparing the similarity of the training lung diagnosis advice and the target lung diagnosis advice; the target lung diagnosis suggestion comprises lung diagnosis information corresponding to a lung patient picture with the highest similarity to a lung picture to be deeply learned and lung abnormal reason information corresponding to the difference characteristic compared with the lung health picture;
and if the similarity is lower than a first preset similarity threshold, adding the lung picture to be deeply learned and the corresponding target lung diagnosis suggestion into the initial lung training model, and repeating the training process.
In one example, the lung diagnosis model training unit is further configured to perform the steps of: if the similarity is greater than or equal to a first preset similarity threshold, finishing training of the initial lung training model, and obtaining an intermediate lung training model; adjusting the first preset similarity threshold value to be a second preset similarity threshold value; wherein the second preset similarity threshold is greater than the first preset similarity threshold; and repeating the training process according to the middle lung training model.
At this time, in the training of the lung diagnosis model, the similarity threshold value, namely the difficulty value of the training, is continuously adjusted; so that the results output by the model are more and more accurate.
In one example, the lung diagnosis model training unit is further configured to perform the steps of: and when the second preset similarity threshold is adjusted to be a target preset similarity threshold, and the similarity is greater than or equal to the target preset similarity threshold, obtaining a target lung training model.
The lung images to be analyzed are input into a lung training model for comparison and analysis, and reference opinions corresponding to the lung images are output; therefore, diagnosis by doctors is assisted, missed diagnosis or misdiagnosis of lung diseases caused by inconsistent technical levels of doctors is reduced, and the accuracy of judgment of the lung diseases is improved. In addition, the lung training model is iterated, so that the output reference opinions are more accurate, and diagnosis by a doctor is facilitated.
The present specification also discloses an artificial intelligence based lung diagnosis assistance method, comprising steps S201-S203.
S201, receiving an input lung picture of a patient to be diagnosed.
S202, comparing the lung picture of the patient to be diagnosed with a lung picture library in a target lung training model to obtain lung details; the lung picture library comprises a plurality of lung health pictures and a plurality of types of lung patient pictures, and the lung details comprise the lung patient picture with the highest similarity with the lung picture of the patient to be diagnosed and the difference characteristics compared with the lung health pictures.
S203, outputting diagnosis suggestions corresponding to the lung details, wherein the diagnosis suggestions comprise lung diagnosis information corresponding to a lung patient picture with the highest similarity to a lung image of a patient to be diagnosed and lung abnormality reason information corresponding to difference characteristics compared with the lung health picture.
In one example, the lung features include a lung density feature, a lung texture feature, and a lung shape feature.
In one example, the method further comprises: inputting the lung picture to be deeply learned into an initial lung training model, outputting training lung details and outputting a diagnosis suggestion corresponding to the training lung details; the lung pictures to be deeply learned comprise lung health pictures and lung disease pictures;
comparing the similarity of the training lung diagnosis advice and the target lung diagnosis advice; the target lung diagnosis suggestion comprises lung diagnosis information corresponding to a lung patient picture with the highest similarity to a lung picture to be deeply learned and lung abnormal reason information corresponding to the difference characteristic compared with the lung health picture;
and if the similarity is lower than a first preset similarity threshold, adding the lung picture to be deeply learned and the corresponding target lung diagnosis suggestion into the initial lung training model, and repeating the training process.
In one example, the method further comprises: if the similarity is greater than or equal to a first preset similarity threshold, finishing training of the initial lung training model, and obtaining an intermediate lung training model;
adjusting the first preset similarity threshold value to be a second preset similarity threshold value; wherein the second preset similarity threshold is greater than the first preset similarity threshold;
and repeating the training process according to the middle lung training model.
In one example, the method further comprises: and when the second preset similarity threshold is adjusted to be a target preset similarity threshold, and the similarity is greater than or equal to the target preset similarity threshold, obtaining a target lung training model.
In the above device embodiment, the same or similar parts to those in the above circuit embodiment are not described again.
The present specification also discloses an electronic device comprising a memory and a processor. The memory and the processor communicate with each other via a bus, the memory storing program instructions executable by the processor, the processor invoking the program instructions to perform the method as described above.
The present specification also discloses a computer storage medium storing a computer program which, when executed by a computer processor, implements the method as described above.
The embodiments in the present specification are described in a progressive manner, and the same and similar parts among the embodiments are referred to each other, and each embodiment focuses on the differences from the other embodiments. In particular, for the system embodiment, since it is substantially similar to the method embodiment, the description is simple, and for the relevant points, reference may be made to the partial description of the method embodiment.
Those of skill would further appreciate that the various illustrative components and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, computer software, or combinations of both, and that the various illustrative components and steps have been described above generally in terms of their functionality in order to clearly illustrate this interchangeability of hardware and software. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the implementation. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present application.
The above description is only an example of the present application and is not intended to limit the present application. Various modifications and changes may occur to those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the scope of the claims of the present application.
Claims (10)
1. An artificial intelligence-based lung diagnosis assistance system, which is characterized by comprising a lung picture input module, a lung diagnosis assistance module and a lung diagnosis suggestion output module, wherein,
the lung image input module receives an input lung image of a patient to be diagnosed;
the lung diagnosis auxiliary module is used for comparing the lung picture of the patient to be diagnosed with the lung picture library in the target lung training model to obtain lung details; the lung picture library comprises a plurality of lung health pictures and a plurality of types of lung patient pictures, and the lung details comprise the lung patient picture with the highest similarity with the lung picture of the patient to be diagnosed and the difference characteristics compared with the lung health pictures;
the lung diagnosis suggestion output module is used for outputting diagnosis suggestions corresponding to the lung details, wherein the diagnosis suggestions comprise lung diagnosis information corresponding to a lung patient picture with the highest similarity to a lung picture of a patient to be diagnosed and lung abnormality reason information corresponding to difference characteristics compared with the lung health picture.
2. The artificial intelligence based lung diagnostic support system of claim 1, wherein the lung features include a lung density feature, a lung texture feature, and a lung shape feature.
3. The artificial intelligence based lung diagnosis assistance system according to claim 1, wherein the lung diagnosis assistance module comprises a lung diagnosis model training unit, wherein the lung diagnosis model training unit is configured to perform the following steps:
inputting the lung picture to be deeply learned into an initial lung training model, outputting training lung details and outputting a diagnosis suggestion corresponding to the training lung details; the lung pictures to be deeply learned comprise lung health pictures and lung disease pictures;
comparing the similarity of the training lung diagnosis advice and the target lung diagnosis advice; the target lung diagnosis suggestion comprises lung diagnosis information corresponding to a lung patient picture with the highest similarity to a lung picture to be deeply learned and lung abnormal reason information corresponding to the difference characteristic compared with the lung health picture;
and if the similarity is lower than a first preset similarity threshold, adding the lung picture to be deeply learned and the corresponding target lung diagnosis suggestion into the initial lung training model, and repeating the training process.
4. The artificial intelligence based lung diagnosis assistance system according to claim 3, wherein the lung diagnosis model training unit is further configured to perform the steps of:
if the similarity is greater than or equal to a first preset similarity threshold, finishing training of the initial lung training model, and obtaining an intermediate lung training model;
adjusting the first preset similarity threshold value to be a second preset similarity threshold value; wherein the second preset similarity threshold is greater than the first preset similarity threshold;
and repeating the training process according to the middle lung training model.
5. The artificial intelligence based lung diagnosis assistance system according to claim 3 or 4, wherein the lung diagnosis model training unit is further configured to perform the steps of:
and when the second preset similarity threshold is adjusted to be a target preset similarity threshold, and the similarity is greater than or equal to the target preset similarity threshold, obtaining a target lung training model.
6. An artificial intelligence based lung diagnosis assistance method applied to any one of claims 1 to 5, the method comprising:
receiving an input lung picture of a patient to be diagnosed;
comparing the lung picture of the patient to be diagnosed with the lung picture library in the target lung training model to obtain lung details; the lung picture library comprises a plurality of lung health pictures and a plurality of types of lung patient pictures, and the lung details comprise the lung patient picture with the highest similarity with the lung picture of the patient to be diagnosed and the difference characteristics compared with the lung health pictures;
and outputting a diagnosis suggestion corresponding to the lung details, wherein the diagnosis suggestion comprises lung diagnosis information corresponding to a lung patient picture with the highest similarity to a lung picture of a patient to be diagnosed and lung abnormality reason information corresponding to difference characteristics compared with the lung health picture.
7. The artificial intelligence based lung diagnostic aid of claim 6, wherein the lung features include a lung density feature, a lung texture feature, and a lung shape feature.
8. The artificial intelligence based lung diagnostic aid of claim 6, wherein the method further comprises:
inputting the lung picture to be deeply learned into an initial lung training model, outputting training lung details and outputting a diagnosis suggestion corresponding to the training lung details; the lung pictures to be deeply learned comprise lung health pictures and lung disease pictures;
comparing the similarity of the training lung diagnosis advice and the target lung diagnosis advice; the target lung diagnosis suggestion comprises lung diagnosis information corresponding to a lung patient picture with the highest similarity to a lung picture to be deeply learned and lung abnormal reason information corresponding to the difference characteristic compared with the lung health picture;
and if the similarity is lower than a first preset similarity threshold, adding the lung picture to be deeply learned and the corresponding target lung diagnosis suggestion into the initial lung training model, and repeating the training process.
9. The artificial intelligence based lung diagnostic aid of claim 8, wherein the method further comprises:
if the similarity is greater than or equal to a first preset similarity threshold, finishing training of the initial lung training model, and obtaining an intermediate lung training model;
adjusting the first preset similarity threshold value to be a second preset similarity threshold value; wherein the second preset similarity threshold is greater than the first preset similarity threshold;
and repeating the training process according to the middle lung training model.
10. The artificial intelligence based lung diagnostic aid method according to claim 8 or 9, further comprising:
and when the second preset similarity threshold is adjusted to be a target preset similarity threshold, and the similarity is greater than or equal to the target preset similarity threshold, obtaining a target lung training model.
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