Eye acupoint data intelligent identification system

Abstract

The application discloses an eye acupoint data intelligent identification system, and an intelligent identifier comprises a head CT scanner, volunteers are seated on a chin support, the head CT scanner scans the volunteers to obtain a CT head image reconstruction, the intelligent identifier comprises a high-definition digital camera for photographing, the high-definition digital camera photographs the volunteers to obtain a photo, the photo fusion is transmitted to an eye acupoint auxiliary positioning through a signal, the eye acupoint auxiliary positioning has an AI eye acupoint intelligent identification model establishing module inside, the AI eye acupoint intelligent identification model establishing module models the photo fusion; the AI eye acupoint intelligent identification model establishing module is connected with an AI acupoint deep learning, the AI acupoint deep learning identifies the eye acupoint by using an AI technology, and a 3D printing model is formed, and the 3D printing model is verified clinically and then the achievements are outputted. Compared with the prior art, the application has the advantages that the application can more accurately judge, accurately position acupoints and quickly check.

Description

TECHNICAL FIELD

[0001] The application relates to an eye acupoint recognition method field, in particular to an eye acupoint data intelligent recognition system. BACKGROUND

[0002] With the development of mobile phones and digital products, people now rely more and more on electronic products, but as people spend more time looking at mobile phones or digital products, eye diseases will occur;

[0003] Eye examination refers to comprehensive examination of the eyes of a subject through medical means and methods, including visual acuity, eye position, fundus, conjunctiva, intraocular pressure and the like. Eye diseases can be effectively prevented and controlled, and eye health can be protected.

[0004] The existing eye examination has two kinds, the first kind is that the examiner needs to sit opposite the doctor, and the doctor watches the patient's eye disease with a detection instrument; the second kind is that the patient takes a CT, and then the doctor judges the disease condition according to the CT report.

[0005] However, the existing method has the following disadvantages:

[0006] (1) The first kind of method in the prior art can quickly judge, but the doctor can only judge by simple observation, and cannot accurately determine the cause, which may cause misjudgment. The second kind of method can be more accurate, but the doctor needs to hold the report to watch after the CT report is obtained, which may delay the disease condition, and the CT report is a film, which may also be inaccurate.

[0007] (2) The existing examination method cannot accurately position the eye acupoint, and can only rely on the doctor's professional judgment to determine the problem acupoint, which may cause misjudgment. SUMMARY

[0008] The technical problem to be solved by the application is to overcome the above technical defects, and to provide an eye acupoint data intelligent recognition system which can more accurately judge, accurately position acupoints and quickly examine.

[0009] In order to solve the above problems, the technical scheme of the application is as follows: an eye acupoint data intelligent recognition system, comprising an auxiliary support and an intelligent recognizer, the intelligent recognizer is located on the auxiliary support, the auxiliary support comprises a bottom plate, a machine case, a surrounding belt, a device clamp, a chin support and a wire hole, the machine case is located at one end of the bottom plate, the surrounding belt is located at the other end of the bottom plate, the device clamp is slidably connected with the surrounding belt, the device clamp is clamped with the intelligent recognizer, and a computer is inserted in the wire hole.

[0010] As preferably, the intelligent identifier comprises a head CT scanner, the recruitment volunteer is seated on a chin rest, the head CT scanner scans the recruitment volunteer to obtain a CT head image reconstruction, the CT head image reconstruction is transmitted to an eye periorbital acupoint auxiliary positioning through a signal, the eye periorbital acupoint auxiliary positioning has an AI eye periorbital acupoint intelligent identification model establishment module inside, and the AI eye periorbital acupoint intelligent identification model establishment module models the CT head image;

[0011] As preferably, the intelligent identifier comprises a high-definition digital camera, the recruitment volunteer is seated on a chin rest, the high-definition digital camera photographs the recruitment volunteer to obtain a photograph, the photograph is subjected to photograph fusion, the photograph fusion is transmitted to the eye periorbital acupoint auxiliary positioning through a signal, the eye periorbital acupoint auxiliary positioning has an AI eye periorbital acupoint intelligent identification model establishment module inside, and the AI eye periorbital acupoint intelligent identification model establishment module models the photograph fusion;

[0012] As preferably, the AI eye periorbital acupoint intelligent identification model establishment module is connected with AI acupoint deep learning, the AI acupoint deep learning identifies the eye periorbital acupoint by using AI technology, and a 3D printing model is formed, and the 3D printing model is subjected to clinical verification and then achievement output.

[0013] Further, the eye periorbital acupoint auxiliary positioning is connected with eye periorbital acupoint artificial optimization, and the eye periorbital acupoint artificial optimization can optimize the modeling of the AI eye periorbital acupoint intelligent identification model establishment module by manual adjustment.

[0014] Further, the eye clinical verification can be identified and compared by an acupoint accuracy expert.

[0015] Further, the eye CT head image reconstruction and the photograph fusion can be compared by mutual algorithm verification.

[0016] Further, the connection part of the eye equipment clamp and the intelligent identifier has a shock-absorbing block.

[0017] Further, the achievement output is connected with a data storage library, and the data storage library can record the clinical information of the recruitment volunteer.

[0018] Compared with the prior art, the application has the advantages that the application is based on the establishment of a 3D model of the whole head CT in the existing traditional head CT examination;

[0019] A set of acupoint distribution systems around the human eye is constructed by the model acupoint, and standard data of the acupoint are formed by 3D imaging technology;

[0020] When the system is used for diagnosis, image recognition can be directly performed, related detection data can be accurately obtained, and related personnel can be accurately assisted in judgment, so that beneficial effects are achieved. BRIEF DESCRIPTION OF DRAWINGS

[0021] Figure 1 is a flow chart of the eye acupoint data intelligent identification system of the present application. DETAILED DESCRIPTION

[0022] The specific embodiments of the present application will be further described below with reference to the accompanying drawings. The same parts are denoted by the same reference numerals.

[0023] It should be noted that the words "front", "back", "left", "right", "up" and "down" used in the following description refer to the directions in the drawings, and the words "in" and "out" refer to the directions towards or away from the geometric center of a particular part.

[0024] In order to make the content of the present application more easily understood, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present application.

[0025] As shown in Figure 1 , the eye acupoint data intelligent identification system comprises an auxiliary support and an intelligent identifier, the intelligent identifier is located on the auxiliary support, the auxiliary support comprises a bottom plate, a case, a wrap-around belt, a device clamp, a chin rest and a wire hole, the case is located at one end of the bottom plate, the wrap-around belt is located at the other end of the bottom plate, the device clamp is slidingly connected to the wrap-around belt, the device clamp is clamped to the intelligent identifier, and a computer is inserted inside the wire hole;

[0026] As shown in Figure 1 , the intelligent identifier comprises a head CT scanner, a volunteer is seated on the chin rest, the head CT scanner scans the volunteer to obtain a CT head image reconstruction, the CT head image reconstruction is transmitted to an eye acupoint auxiliary positioning through a signal, the eye acupoint auxiliary positioning has an AI eye acupoint intelligent identification model establishment module inside, and the AI eye acupoint intelligent identification model establishment module models the CT head image;

[0027] As shown in Figure 1 , the intelligent identifier comprises a high-definition digital camera, a volunteer is seated on the chin rest, the high-definition digital camera takes a photo of the volunteer to obtain a photo, the photo is fused, the photo fusion is transmitted to the eye acupoint auxiliary positioning through a signal, the eye acupoint auxiliary positioning has an AI eye acupoint intelligent identification model establishment module inside, and the AI eye acupoint intelligent identification model establishment module models the photo fusion;

[0028] As shown in Figure 1 , the AI eye acupoint intelligent identification model establishment module is connected to an AI acupoint deep learning, the AI acupoint deep learning identifies the eye acupoint by using AI technology, forms a 3D printing model, and then the 3D printing model is clinically verified and the results are output.

[0029] As shown in Figure 1 The eye perianal acupoint auxiliary positioning is connected with an eye perianal acupoint artificial optimization, and the eye perianal acupoint artificial optimization can be used to artificially adjust and optimize the modeling of the AI eye perianal acupoint intelligent recognition model establishment module.

[0030] As shown in Figure 1 The clinical verification can be identified and compared by an acupoint precision expert.

[0031] As shown in Figure 1 The CT head image reconstruction and photo fusion can be compared by mutual algorithm verification.

[0032] As shown in Figure 1 The connection between the device clamp and the intelligent identifier has a shock absorbing block.

[0033] As shown in Figure 1 The achievement output is connected with a data storage library, and the data storage library can record the clinical information of the collected volunteers.

[0034] In specific use,

[0035] The present application is based on the establishment of a comprehensive database based on head CT image and related clinical data, and the establishment of a standard model of the head CT of the research population and related clinical information for eye acupoint distribution. The CT image included in the database requires CT scanning site, image layer thickness, clinical information including gender, age, medical history, name and other auxiliary examinations (laboratory examination and imaging examination, etc.). The completion of the large-scale comprehensive database provides data preparation for the construction of the algorithm in the later stage, and provides a convenient and fast data interface for the research of other diseases in the future;

[0036] Based on the establishment of the above database, accurate data labeling and reasonable algorithm model construction are carried out: a professional physician team and an intelligent algorithm engineer team coordinate and cooperate, and the deep learning frontier theory and the medical frontier theory are used to construct the database basic framework and divide the image focus area. Subsequently, by means of the powerful fitting capacity of the algorithm and the powerful computing capacity of the graphics processor, the database multi-modal fitting is carried out, the multi-element information such as clinical, imaging and laboratory examination is combined for database self-deep learning, and the diagnostic performance of the model is improved through repeated training and optimization;

[0037] Build CT reconstruction technology and eye perianal acupoint positioning data based on artificial intelligence (AI) technology: based on the above auxiliary diagnosis model, design an efficient small neural network, further develop an embedded high-performance computing module, and make the system applicable to various detection scenes;

[0038] Prospective research verification: the CT scan results are used as the learning gold standard of the 3D model of the human head, the population of artificial intelligence auxiliary dynamic positioning of eye perianal acupoints is collected, the results are compared, and the diagnostic accuracy and specificity are verified.

[0039] Clinical application: the system is promoted to the market and applied in routine ophthalmic examination to further test its diagnostic efficiency in clinical application.

[0040] The above describes the present application and its embodiments, which is not limited, and the drawings only show one of the embodiments of the present application, and the actual structure is not limited. In general, if a person skilled in the art is inspired, without departing from the purpose of the present application, without creative design, similar structure and embodiments of the technical solution should belong to the protection scope of the present application.

Claims

1. An eye acupoint data intelligent identification system, comprising an auxiliary support and an intelligent identifier, the intelligent identifier being located on the auxiliary support, the auxiliary support comprising a bottom plate, a case, a wrap-around belt, a device clamp, a chin rest and a wire insertion hole, the case being located at one end of the bottom plate, the wrap-around belt being located at the other end of the bottom plate, the device clamp being slidingly connected to the wrap-around belt, the device clamp being clamped to the intelligent identifier, and the wire insertion hole being internally provided with a computer, characterized in that: the intelligent identifier comprises a head CT scanner, a recruitment volunteer being seated on the chin rest, the head CT scanner scanning the recruitment volunteer to obtain a CT head image reconstruction, the CT head image reconstruction being transmitted to an eye acupoint auxiliary positioning through a signal, the eye acupoint auxiliary positioning being internally provided with an AI eye acupoint intelligent identification model establishment module, and the AI eye acupoint intelligent identification model establishment module modeling the CT head image; the intelligent identifier comprises a high-definition digital camera, a recruitment volunteer being seated on the chin rest, the high-definition digital camera shooting the recruitment volunteer to obtain a photo, the photo being fused, the photo fusion being transmitted to the eye acupoint auxiliary positioning through a signal, the eye acupoint auxiliary positioning being internally provided with the AI eye acupoint intelligent identification model establishment module, and the AI eye acupoint intelligent identification model establishment module modeling the photo fusion; the AI eye acupoint intelligent identification model establishment module is connected to an AI acupoint deep learning, the AI acupoint deep learning identifying eye acupoints by using AI technology and forming a 3D printing model, and the 3D printing model being clinically verified and then outputted.

2. The eye acupoint data intelligent identification system according to claim 1, characterized in that: The eye acupoint auxiliary positioning is connected to an eye acupoint artificial optimization, the eye acupoint artificial optimization being capable of adjusting and optimizing the modeling of the AI eye acupoint intelligent identification model establishment module by using artificial adjustment.

3. The eye acupoint data intelligent identification system according to claim 1, characterized in that: The clinical verification can be identified and compared by an acupoint accuracy expert.

4. The eye acupoint data intelligent identification system according to claim 1, characterized in that: The CT head image reconstruction and the photo fusion can be compared by mutual algorithm verification.

5. The eye acupoint data intelligent identification system according to claim 1, characterized in that: The device clamp and the intelligent identifier are connected by a shock-absorbing block.

6. The eye acupoint data intelligent identification system according to claim 1, characterized in that: The output is connected to a data storage library, and the data storage library can record the clinical information of the recruitment volunteer.

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