5 results about "Corneal nerve" patented technology

The invention provides an anterior segment transmission interference microscopic imaging device, method and system, and relates to the technical field of medical optical imaging and ophthalmic diagnosis. The single-frame imaging field of view is not less than 2mm * 1mm and is larger than that of a common clinical confocal microscope and a corneal endothelial mirror, so that the statistical reliability and the lesion detection rate can be improved; through a transmission interference mechanism, corneal nerves, epithelial cells and crystalline lens fine structures can be displayed in a high contrast manner, and the method is particularly suitable for low-scattering transparent tissues; a common-path optical structure is adopted, a reference arm or a high-speed scanning unit is not needed, and the system is small in size, low in cost and good in stability; in the examination process, contact with the ocular surface or introduction of a contrast agent is not needed, and discomfort and risks possibly caused by traditional contact type or intrusive type examination are avoided; the method can be used for corneal nerve evaluation, endothelial disease screening, crystalline lens microstructure observation and preoperative / postoperative follow-up visit.

The application relates to the field of biomedical technology, and discloses a Parkinson disease subtype staging inference system based on a corneal nerve image and a SuStaIn algorithm, which comprises an image data acquisition module, a health control module, an abnormality division module, a SuStaIn model construction module and an output module. The image data acquisition module acquires an original image data set, and extracts core effective features after preprocessing the original image data set. The health control module calculates the Z scores of each feature in the corresponding core effective features of Parkinson disease patients through a standardization method. The abnormality division module divides abnormality grades based on the calculation results of the Z scores, and constructs a feature event matrix. The SuStaIn model construction module constructs a disease progression model based on the feature event matrix, inputs the patient feature Z score matrix, sets related parameters, completes model training, and then determines the optimal subtype number through multi-index evaluation. The output module outputs the model prediction results. The application realizes accurate identification of Parkinson disease patient subtypes and objective inference of disease stages, and provides strong support for early diagnosis, individualized treatment and drug research and development of diseases.

This invention discloses a corneal neural image segmentation method and system based on Transformer. The method combines local feature extraction from CNNs with global feature extraction from Transformers, and constructs a corneal neural image segmentation network based on the ResUnet framework. This network includes a multi-scale deformation attention module, an external attention module, and a depthwise separable convolutional module. The multi-scale deformation attention module and the external attention module form a new cross-attention module. This invention combines the advantages of CNNs and Transformers to extract corneal neural features more comprehensively, solving the problems of fragmentation and noise cell interference in corneal neural image segmentation.

The application provides application of KGF-2 in treatment of dry eye, and proves that KGF-2 can improve the basic tear secretion of dry eye mice in a dose-dependent manner, prolong the tear film break-up time, reduce the corneal epithelium and corneal nerve injury, and can reduce the expression levels of inflammatory factors such as tear gland MMP-3, MMP-9 and TNF alpha, and improve the tear secretion function of the tear gland.

This invention discloses an intelligent analysis method for diabetic corneal neuropathy based on confocal microscopy, comprising: acquiring raw images of corneal nerve tissue using a confocal microscope; performing format conversion and preliminary feature extraction via an intelligent diagnostic platform for diabetic neuropathy; calling a multi-scale neuromorphic analysis model to perform multi-dimensional analysis of nerve fiber structure and obtain a morphological parameter matrix; conducting lesion risk correlation analysis using a corneal neuropathy early warning model to generate a feature mapping map; and optimizing image quality and completing structurally defective areas using a confocal image denoising and repair algorithm. The core steps of multi-scale neuromorphic analysis, lesion early warning, and denoising and repair all include processes such as layered capture, feature fusion, risk calculation, and noise filtering. This invention achieves accurate extraction of corneal nerve morphological parameters and objective assessment of lesion risk, providing a systematic intelligent analysis solution for the early diagnosis of diabetic corneal neuropathy.