A portable diabetic retinopathy diagnosis and treatment instrument

Abstract

The invention belongs to the field of ophthalmic diagnosis and treatment equipment, in particular to a portable diabetic retinopathy diagnosis and treatment instrument. The portable diabetic retinopathy diagnosis and treatment instrument of the present invention is composed of a detection module and a diagnosis and treatment module. The detection module includes blood sugar detection and fundus detection. Composed of system database, GA-BP neural network diagnosis model and interface display module, the data of the detection module is transmitted to the expert system for diagnosis and analysis and providing treatment plan, and also establishes the patient's personal file, which can dynamically track the disease data and give targeted treatment plan to avoid delays in treatment. The device has the advantages of high degree of automation, low misdiagnosis rate, no manual labor, and the ability to dynamically track patient data and quickly provide targeted treatment plans.

Description

technical field [0001] The invention belongs to the field of ophthalmic diagnosis and treatment equipment, in particular to a portable diabetic retinopathy diagnosis and treatment instrument. Background technique [0002] With the rapid development of social informatization, the penetration rate and utilization rate of electronic equipment are increasing day by day. People are highly dependent on computers, mobile phones and other devices with electronic screens in their work and life, which leads to an increasing incidence of many eye diseases, and this trend is getting younger and younger, and the diagnosis and treatment of eye diseases are getting more and more attention. Pay attention to. Diabetic retinopathy (DR) is a serious blinding eye disease at present, because long-term high blood sugar in patients will change the function of vascular endothelial cells, causing the destruction of the combination between retinal capillary endothelial cells and pigment epithelial c...

AI Technical Summary

Problems solved by technology

The use of unsupervised learning methods does provide a lot of convenience for image classification, such as manual feature extraction and avoiding misdiagnosis due to personal factors of doctors. Among diseases with a large amount of data, there are few studies on some rare diseases

[0006] Chinese patent application number: CN 202110281472.9 discloses an adaptive superpixel FCM method for image segmentation of fundus lint spots, which uses the initial clustering center optimized by combining the derived multi-population genetic algorithm, effectively avoiding the FCM The impact of the clustering center on the clustering results can solve the sensitive problem of the FCM clustering center more efficiently, so that it can accurately and efficiently detect the lint spot lesions in the fundus image, but it does not consider how to classify and give the fundus image. give an appropriate diagnosis

However, its film reading unit is used to receive the fundus images and judge the pathological changes of the retina, fundus blood vessels, optic nerve head, optic nerve fibers, macula on the retina, and choroid behind the retina. 2, 3, 4, 5 five grades, the process relies on manual experience, it is inevitable that there will be misdiagnosis, and the intelligent diagnosis unit is based on the disease stage Ci, disease duration Lj, genetic probability Nm, fundus Lesion degree Qk and age Rs establish a diabetes diagnostic model, f=(Ci , Lj , Nm , Qk , Rs), the diagnostic model established based on experience helps to give the disease level of diabetic retinopathy patients, and helps to give reasonable treatment However, the artificial experience formula is a way of auxiliary diagnosis, which is not necessarily suitable for all cases, especially for special cases and special circumstances, and it is easy to lead to misdiagnosis

[0009] In view of the existing technology, the images of the fundus of the eye are basically taken by independent detection equipment, and these analysis results are analyzed by the doctor to draw specific diagnostic conclusions and treatment plans, or based on the image diagnosis results combined with blood sugar and Years of illness, etc. can be used to obtain the value of the artificial experience model, so as to determine the grade of diabetic fundus lesions, which cannot cover individual special cases, and it is difficult to guarantee the accuracy of diagnosis. It can be seen that the existing technology mainly relies on doctor's experience for diagnosis, which is prone to occurrence In the event of misdiagnosis or inaccurate judgment, there is no portable diagnostic and therapeutic equipment specifically for diabetic retinopathy in the prior art

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