CN115035766B - Ophthalmic teaching training method, system and equipment - Google Patents
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Abstract
The invention relates to an ophthalmic teaching training method, system and equipment, wherein the method comprises the following steps: acquiring and storing a real fundus disease photo and corresponding disease information; training the generated countermeasure network model according to the real fundus disease photo and the corresponding disease information to generate a fundus disease image generator; generating and storing simulated fundus disease pictures according to the fundus disease image generator; training an eye doctor according to the real fundus disease picture and the simulated fundus disease picture. It can be appreciated that the technical scheme provided by the invention can generate simulated fundus disease pictures, increase the number of fundus pictures used for training, and help vast basic doctors to strengthen the knowledge of rare fundus diseases.
Description
Technical Field
The invention relates to the technical field of teaching and training, in particular to an ophthalmic teaching and training method, system and equipment.
Background
At present, in the ophthalmic daily teaching, regarding common symptoms in fundus diseases, a plurality of real images can be used for training ophthalmic doctors with insufficient experience, so that the doctors can have preliminary diagnosis and treatment capability on the common symptoms by watching the real images of a plurality of common symptoms and the explanation of ophthalmic specialists.
However, among fundus diseases, many rare fundus diseases have low incidence, few images of true rare fundus diseases, and a typical example is lacking in daily education of ophthalmology, so that a vast number of basic fundus doctors lack preliminary diagnosis and treatment capability for such diseases.
Therefore, in the current ophthalmic daily teaching, images of rare fundus diseases are lacking, and wide fundus doctors cannot be well trained, so that the current ophthalmic daily teaching is incomplete.
Disclosure of Invention
In view of the above, the invention aims to provide an ophthalmic teaching training method, system and device, which are used for solving the problems that the prior art lacks images of rare fundus diseases and cannot train vast fundus doctors well, so that the conventional ophthalmic daily teaching is incomplete.
According to a first aspect of an embodiment of the present invention, there is provided an ophthalmic teaching training method including:
acquiring and storing a real fundus disease photo and corresponding disease information;
training the generated countermeasure network model according to the real fundus disease photo and the corresponding disease information to generate a fundus disease image generator;
generating and storing simulated fundus disease pictures according to the fundus disease image generator;
training an eye doctor according to the real fundus disease picture and the simulated fundus disease picture.
Preferably, the generating and storing the simulated fundus disease picture according to the fundus disease image generator further includes:
generating a simulated fundus disease picture according to the fundus disease image generator;
judging whether the simulated fundus disease picture is correct or not;
if yes, marking as a correct simulated fundus disease picture and storing the correct simulated fundus disease picture;
if not, marking as an error simulation fundus disease picture and storing the error simulation fundus disease picture.
Preferably, the determining whether the simulated fundus disease picture is correct further includes:
transmitting the simulated fundus disease picture to an operating end of a fundus expert;
and acquiring a judgment result of the fundus expert on the simulated fundus disease picture, wherein the judgment result is whether the fundus disease picture is correct or not.
Preferably, the training of the ophthalmic doctor according to the real fundus disease photograph and the simulated fundus disease photograph further includes:
acquiring explanation content of fundus experts on the real fundus disease picture and the simulated fundus disease picture;
and training the eye doctor according to the explanation content, the real fundus disease photo and the simulated fundus disease photo.
Preferably, the training of the ophthalmic doctor according to the explanation content, the real fundus disease photo and the simulated fundus disease photo further includes:
judging whether the disease is common disease or not;
if yes, training and learning the ophthalmologist according to the real fundus disease photo and the explanation content;
and training and checking the ophthalmologist according to the simulated fundus disease picture.
Preferably, the training of the ophthalmic doctor according to the explanation content, the real fundus disease photo and the simulated fundus disease photo further includes:
judging whether the disease is common disease or not;
if not, training and learning the eye doctor according to the explanation content, the real fundus disease photo and the simulated fundus disease photo;
and training and checking the ophthalmologist according to the simulated fundus disease picture.
Preferably, the training of the ophthalmic doctor according to the real fundus disease photograph and the simulated fundus disease photograph further includes:
training an eye doctor by taking the correct simulated fundus disease picture as a positive example;
taking the error simulated fundus disease picture as a counterexample, training an eye doctor.
According to a second aspect of an embodiment of the present invention, there is provided an ophthalmic teaching training system comprising:
the photo acquisition module is used for acquiring and storing a real fundus disease photo and corresponding disease information;
the model training module is used for training the generated countermeasure network model according to the real fundus disease photo and the corresponding disease information to generate a fundus disease image generator;
the image generation module is used for generating and storing simulated fundus disease images according to the fundus disease image generator;
and the training module is used for training the ophthalmic doctor according to the real fundus disease picture and the simulated fundus disease picture.
Preferably, the system further comprises:
the auditing module is used for sending the simulated fundus disease picture to an operation end of a fundus expert; and the method is also used for acquiring a judging result of the fundus expert on the simulated fundus disease picture, and the judging result is whether the fundus expert is correct or not.
According to a third aspect of an embodiment of the present invention, there is provided an ophthalmic teaching training apparatus characterized by comprising:
an ophthalmic teaching training method capable of any one of the above;
alternatively, an ophthalmic teaching training system comprising any of the above.
The technical scheme provided by the embodiment of the invention can comprise the following beneficial effects:
according to the invention, the fundus disease image generator is generated by acquiring a real fundus disease photo and training the generated countermeasure network model according to the real fundus disease photo; generating and storing simulated fundus disease pictures according to the fundus disease image generator; training an eye doctor according to the real fundus disease picture and the simulated fundus disease picture. It can be appreciated that the technical scheme provided by the invention can generate simulated fundus disease pictures, increase the number of fundus pictures used for training, and help vast basic doctors to strengthen the knowledge of rare fundus diseases.
It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the invention as claimed.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the invention and together with the description, serve to explain the principles of the invention.
FIG. 1 is a schematic diagram illustrating steps of an ophthalmic teaching training method, according to an exemplary embodiment;
FIG. 2 is a graph of recognition results of an eye bottom photograph by an ophthalmic expert group, according to an exemplary embodiment;
FIG. 3 is a diagram showing the results of identifying a senior ophthalmic doctor group versus an fundus photograph in accordance with one exemplary embodiment;
FIG. 4 is a diagram showing the results of identifying a group of middle-aged ophthalmic doctors versus an fundus photograph, according to an exemplary embodiment;
FIG. 5 is a graph of the results of identifying low annual resource ophthalmologist groups versus bottom of eye photographs, according to an example embodiment;
FIG. 6 is a diagram of recognition results of a photo of the fundus by a medical team, shown according to an exemplary embodiment;
FIG. 7 is a schematic block diagram of an ophthalmic teaching training system, according to an exemplary embodiment.
Detailed Description
Reference will now be made in detail to exemplary embodiments, examples of which are illustrated in the accompanying drawings. When the following description refers to the accompanying drawings, the same numbers in different drawings refer to the same or similar elements, unless otherwise indicated. The implementations described in the following exemplary examples do not represent all implementations consistent with the invention. Rather, they are merely examples of apparatus and methods consistent with aspects of the invention as detailed in the accompanying claims.
Example 1
Referring to fig. 1, fig. 1 is a schematic diagram illustrating steps of an ophthalmic teaching training method according to an exemplary embodiment, and fig. 1 illustrates an ophthalmic teaching training method, including:
s11, acquiring a real fundus disease photo and corresponding disease information and storing the real fundus disease photo;
first, it is necessary to acquire a photograph of a real fundus disease and acquire disease information corresponding to the photograph, the disease information including at least the name of the disease. Fundus diseases can be divided into common diseases and rare diseases according to the morbidity, and due to the fact that the morbidity of the common diseases is higher, more real fundus disease pictures exist in medicine, and the morbidity of the rare diseases is lower, and corresponding real fundus disease pictures are fewer. Preferably, the system is capable of classifying the actual fundus disease photograph by disease name for use in subsequent steps.
Step S12, training the generated countermeasure network model according to the real fundus disease photo and the corresponding disease information to generate a fundus disease image generator;
the system is used for training the generated countermeasure network model according to a plurality of real fundus disease pictures corresponding to each disease, and can generate an image generator which can generate a simulated fundus disease picture similar to the real fundus disease picture after training each disease by the generated countermeasure network model.
Step S13, generating and storing simulated fundus disease pictures according to the fundus disease image generator;
and step S14, training an eye doctor according to the real fundus disease picture and the simulated fundus disease picture.
In specific practice, assuming that the actual fundus photos of two existing diseases are input into a system, a disease A is a common disease, a disease B is a rare disease, a staff of the system inputs a plurality of actual fundus photos corresponding to the disease A into the system, a plurality of actual fundus photos corresponding to the disease B are input into the system, the system acquires the actual fundus photos corresponding to the disease A, the system inputs all the actual fundus photos into a generated countermeasure network model, the generated countermeasure network model is trained, and then a picture generator is generated, and the picture generator can automatically generate simulated fundus disease pictures of the disease A according to the disease A, also can automatically generate simulated fundus disease pictures of the disease B according to the disease B, and stores the generated simulated fundus disease pictures. Then, the real fundus disease photograph and the simulated fundus disease photograph can be used in training of an ophthalmologist. When training the disease A, as the disease A is a common disease and more real fundus disease pictures exist, the real fundus disease pictures can be directly applied to training when training and learning the disease A, and the simulated fundus disease pictures are used for examination after training an ophthalmologist; when training the disease B, the simulated fundus disease pictures can be used for training and learning the eye doctors together because the disease B is a rare disease and the number of the real fundus disease pictures is insufficient, so that the preliminary diagnosis and treatment capability of the doctors to the disease is improved.
Referring to fig. 2 to 6, for verifying the reliability of the generated countermeasure network model, the reliability of the image generator generated by the generated countermeasure network model is verified, and the real fundus disease photo and the simulated fundus disease photo are used to be mixed, so that the ophthalmologist at different years can identify the authenticity. 70 simulated fundus disease pictures generated by the image generator are taken in 70 real normal fundus pictures. Human testing was categorized into the ophthalmologist group, senior ophthalmic doctor group, middle-aged ophthalmic doctor group, low-aged ophthalmic doctor group, and medical student group, with the objective of identifying virtually generated fundus photographs among 140 fundus photographs. The results show that the accuracy, sensitivity and specificity of the identification of each group are about 50%, which indicates that the generation of the virtual fundus photo generated against the network is enough to achieve the degree of 'false spurious'.
It can be understood that the invention generates the fundus disease image generator by acquiring the real fundus disease photo and training the generated countermeasure network model according to the real fundus disease photo; generating and storing simulated fundus disease pictures according to the fundus disease image generator; training an eye doctor according to the real fundus disease picture and the simulated fundus disease picture. It can be appreciated that the technical scheme provided by the invention can generate simulated fundus disease pictures, increase the number of fundus pictures used for training, and help vast basic doctors to strengthen the knowledge of rare fundus diseases.
In specific practice, the generating and storing the simulated fundus disease picture according to the fundus disease image generator further comprises:
generating a simulated fundus disease picture according to the fundus disease image generator;
judging whether the simulated fundus disease picture is correct or not;
if yes, marking as a correct simulated fundus disease picture and storing the correct simulated fundus disease picture;
if not, marking as an error simulation fundus disease picture and storing the error simulation fundus disease picture.
In the actual use process, a judging link for simulating fundus disease pictures can be added. Although the generated simulated fundus disease picture is sufficient to the extent of "in false spurious", there is a possibility that the generated simulated fundus disease picture does not coincide with the disease, for example, the disease B is a rare disease, and the generated simulated fundus disease picture is not a case where the disease B should be generated, which is extremely erroneous.
It can be appreciated that by judging the simulated fundus disease picture, the simulated fundus disease picture finally used for training can be made more accurate.
In specific practice, the determining whether the simulated fundus disease picture is correct further includes:
transmitting the simulated fundus disease picture to an operating end of a fundus expert;
and acquiring a judgment result of the fundus expert on the simulated fundus disease picture, wherein the judgment result is whether the fundus disease picture is correct or not.
In the actual use process, for example, the disease B is a rare disease, the system generates a simulated fundus disease picture, the picture is sent to an operation end (the operation end may be a mobile phone or a computer) of a fundus expert, the fundus expert determines whether the picture is the disease B, and sends a determination result to the system.
It can be understood that whether the simulated fundus disease picture is correct or not is judged by fundus specialists, so that the generated simulated fundus disease picture can be more ensured to be the picture of the corresponding disease, and the reliability of the system is improved.
In specific practice, the training of the eye doctor according to the real fundus disease photograph and the simulated fundus disease photograph further comprises:
acquiring explanation content of fundus experts on the real fundus disease picture and the simulated fundus disease picture;
and training the eye doctor according to the explanation content, the real fundus disease photo and the simulated fundus disease photo.
Specifically, in the actual training process, the explanation content of each picture can be obtained from the fundus expert, for example, three existing pictures are respectively a true fundus disease picture of the disease A, a true fundus disease picture of the disease B and a simulated fundus disease picture of the disease B, and the fundus expert respectively configures the explanation content for the three pictures to explain the concrete content of each picture for training an ophthalmologist.
It can be understood that by configuring the explanation content of the fundus expert, the training effect on the eye doctor can be made better.
In specific practice, the training of the eye doctor according to the explanation content, the real fundus disease photo and the simulated fundus disease photo further comprises:
judging whether the disease is common disease or not;
if yes, training and learning the ophthalmologist according to the real fundus disease photo and the explanation content;
and training and checking the ophthalmologist according to the simulated fundus disease picture.
It can be understood that the real fundus disease pictures of various fundus diseases are firstly used for training, and simulated fundus disease pictures generated by artificial intelligence are used for checking doctors after class, so that the phenomenon that the checking result is unreal due to limited real fundus disease pictures is prevented.
In specific practice, the training of the eye doctor according to the explanation content, the real fundus disease photo and the simulated fundus disease photo further comprises:
judging whether the disease is common disease or not;
if not, training and learning the eye doctor according to the explanation content, the real fundus disease photo and the simulated fundus disease photo;
and training and checking the ophthalmologist according to the simulated fundus disease picture.
Specifically, if the disease is rare, the number of real fundus disease photographs is insufficient for training study of an ophthalmic doctor, the simulated fundus disease photograph is used for training study of the ophthalmic doctor.
It will be appreciated that for some rare fundus diseases where fundus images are missing, high quality virtual images may be used as a supplement to the teaching, preventing a small number of real images from creating a clatter impression of a disease to the physician.
In specific practice, the training of the eye doctor according to the real fundus disease photograph and the simulated fundus disease photograph further comprises:
training an eye doctor by taking the correct simulated fundus disease picture as a positive example;
taking the error simulated fundus disease picture as a counterexample, training an eye doctor.
It will be appreciated that when the image generator generates a fundus image that does not correspond to a clinical manifestation of a disease, the fundus image that does not correspond to the clinical manifestation of the disease can be used as a counterexample for training a doctor, and the impression of the disease by the doctor can be enhanced.
Example two
The invention provides an ophthalmic teaching training system, comprising:
the photo acquisition module 100 is used for acquiring and storing a real fundus disease photo and corresponding disease information;
the model training module 101 is configured to train the generated countermeasure network model according to the real fundus disease photograph and the corresponding disease information, and generate a fundus disease image generator;
the image generation module 102 is used for generating and storing an analog fundus disease image according to the fundus disease image generator;
and the training module 103 is used for training the eye doctor according to the real fundus disease picture and the simulated fundus disease picture.
It can be understood that the present invention obtains a real fundus disease photo by the photo obtaining module 100, and trains the generated countermeasure network model by the model training module 101 according to the real fundus disease photo, so as to generate a fundus disease image generator; generating and storing simulated fundus disease pictures according to the fundus disease image generator through a picture generation module 102; and training the ophthalmic doctor according to the real fundus disease picture and the simulated fundus disease picture through a training module 103. It can be appreciated that the technical scheme provided by the invention can generate simulated fundus disease pictures, increase the number of fundus pictures used for training, and help vast basic doctors to strengthen the knowledge of rare fundus diseases.
In specific practice, the system further comprises:
the auditing module is used for sending the simulated fundus disease picture to an operation end of a fundus expert; and the method is also used for acquiring a judging result of the fundus expert on the simulated fundus disease picture, and the judging result is whether the fundus expert is correct or not.
The invention provides an ophthalmic teaching training device, comprising:
an ophthalmic teaching training method capable of performing any one of the above;
alternatively, an ophthalmic teaching training system comprising any of the above.
It can be understood that the establishment of the system provides great convenience for the teaching and clinical training of ophthalmologists and medical students, greatly contributes to the promotion of the application of artificial intelligence technology to the field of medical teaching, saves the teaching and learning forces for the cultivation of medical talents, and enhances the clinical knowledge of medical students on diseases. In addition, the system development is helpful for the vast grassroots doctors to strengthen the knowledge of the rare fundus diseases, and the fundus camera is popularized in most grassroots hospitals in China, so that the system has important significance for improving the diagnosis and treatment capability of the grassroots ophthalmologists in China on the fundus diseases.
It is to be understood that the same or similar parts in the above embodiments may be referred to each other, and that in some embodiments, the same or similar parts in other embodiments may be referred to.
It should be noted that in the description of the present invention, the terms "first," "second," and the like are used for descriptive purposes only and are not to be construed as indicating or implying relative importance. Furthermore, in the description of the present invention, unless otherwise indicated, the meaning of "plurality" means at least two.
Any process or method descriptions in flow charts or otherwise described herein may be understood as representing modules, segments, or portions of code which include one or more executable instructions for implementing specific logical functions or steps of the process, and further implementations are included within the scope of the preferred embodiment of the present invention in which functions may be executed out of order from that shown or discussed, including substantially concurrently or in reverse order, depending on the functionality involved, as would be understood by those reasonably skilled in the art of the present invention.
It is to be understood that portions of the present invention may be implemented in hardware, software, firmware, or a combination thereof. In the above-described embodiments, the various steps or methods may be implemented in software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, may be implemented using any one or combination of the following techniques, as is well known in the art: discrete logic circuits having logic gates for implementing logic functions on data signals, application specific integrated circuits having suitable combinational logic gates, programmable Gate Arrays (PGAs), field Programmable Gate Arrays (FPGAs), and the like.
Those of ordinary skill in the art will appreciate that all or a portion of the steps carried out in the method of the above-described embodiments may be implemented by a program to instruct related hardware, where the program may be stored in a computer readable storage medium, and where the program, when executed, includes one or a combination of the steps of the method embodiments.
In addition, each functional unit in the embodiments of the present invention may be integrated in one processing module, or each unit may exist alone physically, or two or more units may be integrated in one module. The integrated modules may be implemented in hardware or in software functional modules. The integrated modules may also be stored in a computer readable storage medium if implemented in the form of software functional modules and sold or used as a stand-alone product.
The above-mentioned storage medium may be a read-only memory, a magnetic disk or an optical disk, or the like.
In the description of the present specification, a description referring to terms "one embodiment," "some embodiments," "examples," "specific examples," or "some examples," etc., means that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the present invention. In this specification, schematic representations of the above terms do not necessarily refer to the same embodiments or examples. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
While embodiments of the present invention have been shown and described above, it will be understood that the above embodiments are illustrative and not to be construed as limiting the invention, and that variations, modifications, alternatives and variations may be made to the above embodiments by one of ordinary skill in the art within the scope of the invention.
Claims (3)
1. An ophthalmic teaching training system, comprising:
the photo acquisition module is used for acquiring and storing a real fundus disease photo and corresponding disease information;
the model training module is used for training the generated countermeasure network model according to the real fundus disease photo and the corresponding disease information to generate a fundus disease image generator;
the image generation module is used for generating and storing an analog fundus disease image according to the fundus disease image generator, and comprises the following steps: generating a simulated fundus disease picture according to the fundus disease image generator; judging whether the simulated fundus disease picture is correct or not; if yes, marking as a correct simulated fundus disease picture and storing the correct simulated fundus disease picture; if not, marking as an error simulation fundus disease picture and storing the error simulation fundus disease picture;
the training module is used for training the eye doctor according to the real fundus disease photo and the simulated fundus disease photo, and comprises the following steps: acquiring explanation content of fundus experts on the real fundus disease picture and the simulated fundus disease picture; training an ophthalmic doctor according to the explanation content, the real fundus disease photo and the simulated fundus disease photo;
training an ophthalmic doctor according to the explanation content, the real fundus disease photograph and the simulated fundus disease photograph, comprising: judging whether the disease is common disease or not; if yes, training and learning the ophthalmologist according to the real fundus disease photo and the corresponding explanation content, and training and checking the ophthalmologist according to the simulated fundus disease photo; if not, training and learning the ophthalmic doctor according to the explanation content, the real fundus disease photo and the simulated fundus disease photo, and training and checking the ophthalmic doctor according to the simulated fundus disease photo;
training an ophthalmic doctor according to the real fundus disease photograph and the simulated fundus disease photograph, further comprising: training an eye doctor by taking the correct simulated fundus disease picture as a positive example; taking the error simulated fundus disease picture as a counterexample, training an eye doctor.
2. The system of claim 1, further comprising:
the auditing module is used for sending the simulated fundus disease picture to an operation end of a fundus expert; and the method is also used for acquiring a judging result of the fundus expert on the simulated fundus disease picture, and the judging result is whether the fundus expert is correct or not.
3. An ophthalmic teaching training device, comprising:
the ophthalmic teaching training system of any of claims 1 or 2.
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