582 results about "Fundus image" patented technology

The invention relates to the technical field of ophthalmologic image processing, and discloses a fundus blood vessel segmentation method based on multi-modal data. The method comprises the following steps: firstly, synchronously acquiring an optical coherence tomography image, a color fundus photographic image and a fluorescent angiography image to form multi-modal fundus data; preprocessing the eye fundus image data set to generate a standardized multi-mode eye fundus image data set; fusing heterogeneous features based on the data set, and constructing a multi-dimensional fundus feature space; using the space to train a deep learning segmentation network model, and generating an initial fundus blood vessel segmentation mask; carrying out confidence evaluation analysis on the initial mask to obtain a blood vessel segmentation result confidence distribution map; dividing blood vessel segmentation quality grades according to the distribution diagram, and defining a judgment rule; and performing interactive correction on the low-confidence region based on a rule to generate a final optimized fundus blood vessel segmentation result. And the final result is transmitted to a visual terminal for three-dimensional topology reconstruction. According to the method, the advantages of multi-modal data are integrated, and a more comprehensive fundus blood vessel segmentation result is formed.

The invention discloses a cross-modal eye fundus image generation method and system based on a generative adversarial network, relates to the technical field of medical image processing, and constructs an eye fundus focus perception and edge consistency generative adversarial network by taking a cyclic consistency generative adversarial network as a baseline. The core of the method is that a lesion perception mixed attention module is embedded in a bottleneck layer of a generator so as to strengthen the extraction capability of fine features of a lesion area; an edge information extraction module is designed, and key edge features are accurately extracted in combination with Roberts edge detection, wavelet transform and non-local mean denoising; and a joint loss function containing edge consistency loss is constructed, and the semantic consistency of a focus structure during cross-modal generation is ensured by minimizing the feature difference between the source image and the generated image. According to the method, the problems of disordered content, inconsistent structure and unstable training of the generated image in the prior art are effectively solved, and the simulation degree and clinical availability of the generated image are remarkably improved.

The invention discloses a heterogeneous double-flow fusion method and system for diabetic retinopathy grading. The method comprises the following steps: obtaining an output result of diabetic retinopathy grading by utilizing a heterogeneous double-flow architecture; processing an input fundus image into images with different resolutions; extracting global context features from the low-resolution image by using a lightweight visual Transform model distilled by composite knowledge, and extracting local focus features from the high-resolution image by using a convolutional neural network model; performing interactive fusion on the global context features and the local focus features of the double-branch architecture through a symmetric bidirectional cross attention fusion module to obtain enhanced fusion feature representation; and finally, inputting the fusion features into a classifier, and outputting a severity grading result of the lesion. The method aims at improving the accuracy and robustness of hierarchical diagnosis through deep analysis of global information and local details, and can be applied to the medical fields of clinical computer-aided diagnosis, eye image analysis and the like.

The invention belongs to the field of artificial intelligence and fundus image enhancement, and discloses a fundus image enhancement method and system based on machine learning, electronic equipment and a storage medium, and the method comprises the steps: obtaining an original fundus spectral image, and carrying out the preprocessing of the original fundus spectral image, and obtaining a preprocessed spectral image; constructing a backbone network based on a residual network, and extracting multi-scale features of the preprocessed spectral image in combination with cavity convolution; introducing a channel-space-spectrum multi-attention module into the backbone network, and performing multi-attention fusion on the multi-scale features to obtain an enhanced feature map; and performing adversarial training on the backbone network by using the generative adversarial network and the enhanced feature map, and performing image enhancement on the collected fundus spectral image by using the trained network to obtain an enhanced fundus spectral image. According to the method, more-dimensional image support is provided for medical diagnosis, the quality of the fundus image can be effectively improved, the diagnosis accuracy of a doctor on fundus lesions is improved, and the method has important clinical application value.

The invention discloses an automatic retinal vessel segmentation method fusing large-kernel multi-scale cavity convolution and an attention mechanism, which is suitable for accurate extraction of a fine-grained vessel structure in a fundus image. According to the method, an improved deep neural network (LKD-UNet) is constructed, a collaborative receptive field module is embedded in each layer of an encoder, and large kernel convolution and multi-scale cavity convolution are combined to enlarge the receptive field and enhance the texture analysis capability; an efficient Transform module is introduced into a bottleneck layer, a cross-regional semantic relationship is modeled through a multi-head self-attention mechanism, and the topological consistency of microvessels is improved; the decoder adopts a shallow symmetric structure and a kernel scale scheduling strategy, and realizes high-resolution reduction in combination with jump connection. In addition, a four-category pixel-level error visualization mechanism is introduced and is used for assisting segmentation error diagnosis and result interpretation. Experimental results show that the method is superior to an existing mainstream model in multiple public data sets, has remarkable advantages in the aspects of segmentation precision, structural coherence and deployment efficiency, and is suitable for remote fundus screening and blood vessel extraction tasks in embedded medical equipment.

The invention provides an eye fundus image intelligent enhancement method and system based on multi-modal image fusion. The method comprises the following steps of obtaining a color eye fundus photography CFP image and an optical coherence tomography OCT image and performing preprocessing; establishing an image space coordinate mapping relation for the preprocessed color fundus photography CFP image and the preprocessed optical coherence tomography OCT image, and performing feature space alignment; the first branch network structure and the second branch network structure of the multi-layer Transform feature fusion module are used for carrying out feature extraction and enhancement on the features of the processed color fundus photography CFP image and the features of the processed optical coherence tomography OCT image respectively, and the features of the color fundus photography CFP image and the features of the processed optical coherence tomography OCT image are fused; multi-modal fusion image features are obtained; according to the multi-modal fusion image features, a deep learning model is adopted to reconstruct an eye fundus image; and constructing an image enhancement model to obtain an enhanced fundus image.

The invention discloses a retina thickness prediction method and system based on a multi-modal image, and the method and system achieve the effective estimation of the retina thickness under a low-cost condition through feature alignment and fusion modeling, and improve the basic screening and follow-up visit capability. According to the invention, through fusion of the multi-mode retina image data, the structure and function information of the optic nerve can be more comprehensively obtained, and the prediction accuracy of the thickness of the retina nerve fiber layer (RNFL) is improved. The OCT high-resolution hierarchical structure and the wide-view texture features of the eye fundus image are combined, so that anatomy and pathological states of optic nerves can be truly restored. The method can be used as an auxiliary method for early screening and early warning of optic neurodegenerative diseases such as glaucoma, and provides support for low-cost and high-efficiency primary screening and clinical auxiliary decision making.

The invention relates to the technical field of intelligent medical treatment, and discloses a cataract surgery navigation control method based on multi-modal fusion, which comprises the following steps: carrying out lightweight preprocessing on an eye fundus image, ultrasonic probe data, a corneal topographic map and intraocular pressure parameters through an edge computing architecture, and constructing an efficient feature extraction network by adopting a knowledge distillation technology; self-adaptive feature fusion is realized based on an uncertainty quantification mechanism, real-time structure recognition is provided by utilizing an augmented reality technology, an intelligent navigation strategy is generated by pre-training a reinforcement learning model, an instrument control system with multilayer safety guarantee is established, and continuous optimization of the system is realized by adopting an online learning mechanism. According to the invention, the precision and safety of the operation are effectively improved, and an effective technical scheme is provided for intelligent navigation control of the cataract operation.

The disclosure describes a reading system and a reading method for fundus images, the reading system comprising: an input module for receiving fundus images; a screening module for outputting screening results based on the fundus images, the screening results at least including quality control judgment results and lesion judgment results; a first classification module for classifying the fundus images into screening qualified images and first images to be quality controlled based on the quality control judgment results, and taking at least one image from the first images to be quality controlled and the screening qualified images as an image to be quality controlled; and a quality control module for outputting quality control results based on the image to be quality controlled. According to the disclosure, the screening accuracy of the reading system can be improved.

The invention provides an image lesion attribute prediction model training method, a prediction method and a related device, and the method comprises the steps: carrying out the visual coding of a fundus image sample through a visual encoder, and obtaining a pseudo-image visual feature; performing text coding on each lesion attribute sub-category description text to obtain lesion attribute sub-category features; performing matching according to the visual features of the pseudo image and the lesion attribute sub-category features to obtain a pseudo matching probability; performing visual coding on the fundus image sample through the initial image lesion attribute prediction model to obtain initial image visual features; performing matching according to the initial image visual features and the lesion attribute sub-category features to obtain a predicted matching probability; carrying out loss calculation according to the pseudo matching probability and the predicted matching probability to obtain a target matching loss function; and updating the initial model based on the target matching loss function to obtain an image lesion attribute prediction model. In conclusion, the lesion analysis accuracy of the fundus image can be improved.

The invention provides a fundus image enhancement method based on adaptive fuzzy evaluation and frequency domain enhancement, and the method comprises the following steps: S1, obtaining cataract fundus image samples of different fuzzy levels, and constructing a training data set; s2, constructing an image adaptive enhancement model based on fuzzy evaluation, wherein the image adaptive enhancement model comprises a fuzzy evaluation module, a consistency keeping module and a frequency adaptive enhancement module; and S3, performing model training on the constructed image adaptive enhancement model by using the training data set to obtain a trained image adaptive enhancement model which is used for adaptive enhancement of the cataract eye fundus image. According to the method, the structural definition and color fidelity of the cataract blurred fundus image can be effectively improved, and the diagnosis availability and clinical value of the image are improved.

The invention discloses a premature infant retinopathy recognition system based on multi-modal data, and relates to the technical field of computers, artificial intelligence and image processing, and the system comprises a data set construction module which obtains an eye fundus image, carries out the preprocessing of the eye fundus image, and constructs a data set based on the eye fundus image obtained through the preprocessing and a structured prompt project; the primary training module is used for inputting the data set into a preset LLaVA-v1.5 model and training the LLaVA-v1.5 model based on a LoRA mechanism to obtain a primary training model; the secondary training module is used for constructing a knowledge enhanced thinking chain and training the primary training model based on the knowledge enhanced thinking chain to obtain a recognition model; and the recognition module inputs the fundus image to be recognized into the recognition model to obtain a recognition result. According to the novel intelligent diagnosis system, retina image analysis and medical knowledge reasoning are integrated, and high-precision and interpretable ROP screening is achieved.

The invention provides a multi-view eye fundus image splicing method suitable for multiple modalities, and the method comprises the steps: obtaining a plurality of to-be-spliced images of different modalities, carrying out the corresponding preprocessing according to the imaging characteristics of the modalities to which the images belong, and obtaining a preprocessed image group; selecting one of the preprocessed image groups as a reference image, and performing image registration on the other images and the reference image to obtain corresponding registration image groups; on the basis of a preset registration precision threshold value and the proportion of overlapping areas between the images, screening out the images meeting the requirements from the registration image group; and performing multi-scale fusion processing according to the spatial relationship between the screened images to obtain a spliced eye fundus image. According to the multi-view eye fundus image splicing method provided by the invention, through preprocessing, registration, screening and multi-scale fusion, images of different modes can be efficiently integrated, the splicing precision and the image quality are improved, and the method is suitable for accurate eye fundus analysis and medical diagnosis.

The invention discloses a focus recognition system and method for a diabetic retinopathy image, and relates to the technical field of medical image processing, and the system comprises a fundus image collection module, an image feature processing module, a focus feature extraction module, a network model training module and a focus model recognition module. By integrating the fundus image collection module, the image feature processing module, the lesion feature extraction module, the network model training module and the lesion model recognition module, full-process automation from original fundus image acquisition to lesion intelligent recognition is realized, and the efficiency of diabetic retinopathy lesion recognition is remarkably improved; in addition, errors possibly caused by manual intervention are reduced, the accuracy and consistency of recognition results are ensured, specifically, the fundus image collection module can automatically obtain images from ophthalmology equipment or a hospital image storage system, format unification and index construction are carried out, and great convenience is provided for follow-up processing.

The invention discloses a myopia image deep learning recognition model training method, particularly relates to the technical field of medical image processing and deep learning, and is used for solving the problem that an existing deep learning model lacks anatomical structure priori knowledge guidance in myopia eye bottom image analysis. The method comprises the following steps: acquiring a myopia eye bottom image and anatomical structure priori knowledge data, extracting a multi-scale feature map by using a deep learning model, analyzing the geometric morphology of a key anatomical component based on standard spatial relationship information, and generating a spatial constraint loss item; according to the method, key anatomical path topology coherence is evaluated based on topology connection information, topology constraint loss items are generated, a loss item fusion strategy is dynamically adjusted according to a training stage, finally, a model is iteratively trained to convergence through a gradient back propagation algorithm, and organic combination of medical priori knowledge and a deep learning model is realized. And the clinical rationality and reliability of model output are improved.

According to the glaucoma multi-mode auxiliary diagnosis device and the electronic equipment, firstly, first feature extraction and second feature extraction are carried out on an eye fundus image and an OCT image respectively, then semantic spaces of an eye fundus image mode and an OCT image mode are aligned by utilizing comparison loss, and the first feature and the second feature are fused by utilizing a cross attention mechanism, so that an eye fundus image is obtained. According to the method, the two modal data are subjected to fusion to obtain fusion features, then the fusion features are subjected to feature extraction to obtain third features, and glaucoma classification judgment is performed based on the third features, so that global modeling of the two modal data can be realized, effective features are extracted to perform glaucoma classification judgment, and the accuracy and timeliness of diagnosis are ensured.

The invention belongs to the technical field of image detection and recognition, and relates to an image detection and recognition method and device and a computer readable storage medium. The method comprises the following steps: taking fundus image sample pairs with same labels of left eye bottom images and right eye bottom images in fundus image sample pairs as a group of fundus image sample pairs, and endowing each group of fundus image sample pairs with a new label as a real label; inputting the left and right fundus images of the fundus image sample pair into two feature extraction branches, and outputting a first feature map and a second feature map; inputting the first feature map and the second feature map into two attention branches, and outputting a first depth feature map and a second depth feature map; and splicing the first depth feature map and the second depth feature map, inputting the spliced first depth feature map and the spliced second depth feature map into a classification module, outputting a prediction label of a fundus image sample pair, calculating a value of a detection and recognition loss function, and training a double-branch feature extraction module, a double-branch attention module and the classification module to obtain a trained fundus image detection and recognition model.

The disclosure describes a reading system and a reading method based on fundus image classification. The reading system includes an acquisition module for acquiring fundus images; a first classification module for classifying the fundus images using a first classification model to obtain a first classification result and a classification result type; a grouping module for grouping the fundus images into negative result images, positive result images and images to be reclassified; a first quality control module for obtaining a final classification result and images to be arbitrated based on a quality control model configured using a preset negative prediction rate and a preset positive prediction rate; a second classification module for classifying the images to be reclassified using a second classification model to obtain a final classification result and images to be arbitrated; and an arbitration module for arbitrating the images to be arbitrated to obtain an arbitration classification result. Thus, the classification accuracy can be improved.

Diabetes is a disease characterized by high levels of blood glucose. It is important to keep diabetes under control to avoid short- and long-term complications. Diabetes can affect vision due to the alterations it produces in the blood vessels of the retina. This is known as Diabetic Retinopathy (DR), which is one of the leading causes of impaired vision in developed countries. One of the complications of diabetic retinopathy is Diabetic Macular Edema (DME), which is the leading cause of vision loss in diabetic patients and can appear at any stage of diabetic retinopathy. This consists of the gradual accumulation of fluid in the macula, the most important area of the retina. The determination of diabetic macular oedema is very important for the retina. The determination of diabetic macular oedema is very important for adequate treatment of this condition. A variety of technological options are used for detecting diabetic retinopathy, although only the most sophisticated detect macular oedema, a complication that appears as a consequence of diabetic retinopathy and is one of the leading causes of blindness. The invention describes a portable system for detecting diabetic macular oedema by capturing a fundus image using a portable ophthalmoscope; said image is sent via wired or wireless means to an embedded system that has an algorithm based on artificial intelligence, which extracts information from the image and processes same to identify the presence of the condition being studied.

The invention discloses an eye fundus image detection method and device for diabetic retinopathy and intelligent equipment. The method comprises the steps of performing image preprocessing on an eye fundus image; differentiated enhancement processing is carried out on the standardized eye fundus images according to the pathological features; performing candidate area detection in each task image, determining a lesion unit of each task image, constructing a lesion unit set of each task image, and performing fusion processing on the lesion units in each lesion unit set; extracting feature parameters of each lesion unit in the global lesion unit set; performing category judgment on each lesion unit in the global lesion unit set through a preset judgment rule based on the feature parameters, and determining a pathological feature tag corresponding to each lesion unit; and mapping the pathological feature label to a corresponding position in the standardized fundus image, and generating a detection result distribution diagram of the diabetic retinopathy. According to the embodiment of the invention, the detection sensitivity of vascular lesions and exudative lesions can be improved.

The invention discloses an improved lightweight diabetic retina fundus image small target detection method based on RT-DETR. The detection method comprises the following steps: step 1, constructing a diabetic retina fundus image small target detection model; 2, obtaining a fundus image to be detected, and preprocessing the fundus image; 3, training and optimizing the detection model constructed in the step 1 by using a pre-training set, and inputting the to-be-detected eye fundus image preprocessed in the step 2 into the optimized detection model for detection reasoning; and step 4, outputting an eye fundus image with small target positions, classification and classification confidence. The invention further discloses a detection system for realizing the small target detection method and application of the small target detection method and the detection system, and the application prospect is wide.

The invention discloses an eye fundus image quality control method and device, a storage medium and electronic equipment, and relates to the technical field of image processing. The eye fundus image quality control method comprises the steps of determining a to-be-processed eye fundus image; based on an effective area of the to-be-processed eye fundus image, determining at least one type of quality quantification data corresponding to the to-be-processed eye fundus image, the effective area being used for representing an unshielded eye fundus structure area; and determining a quality control result of the to-be-processed eye fundus image based on the at least one type of quality quantification data corresponding to the to-be-processed eye fundus image, the quality control result comprising an image quality score and / or a quality problem corresponding to the image quality score. According to the eye fundus image quality control method provided by the embodiment of the invention, the score of the eye fundus image to be processed and the quality problem corresponding to the score can be intuitively known, so that related personnel can know the score and the quality problem, the normalization of the eye fundus image is improved, and standardization of eye fundus image data is facilitated.

The invention relates to a fundus image enhancement method and system based on high-frequency information guidance. The method comprises the following steps: acquiring fundus image data, and processing the fundus image data; constructing a generative adversarial network model based on high-frequency information guidance, and performing training iteration on the generative adversarial network model based on high-frequency information guidance to obtain a fundus image enhancement model; the generative adversarial network model based on high-frequency information guidance comprises four neural networks: two generators and two discriminators; and performing model reasoning on the processed eye fundus image through an eye fundus image enhancement model to obtain an enhanced eye fundus image. According to the method, a structurally powerful generator is used for modeling an eye fundus image, texture details such as blood vessels of the eye fundus image are different from those of a learning degraded generator, and in addition, a discriminator capable of paying attention to high-frequency component differences of the image is designed and used for assisting learning of the generator.

The invention discloses a cataract fundus image automatic grading method based on semi-supervised learning, and belongs to the field of medical artificial intelligence. Characteristic parameters are extracted through multi-modal feature fusion of the eye fundus image of a patient, a semi-supervised learning neural network model is constructed, a pseudo-label training model is formed in combination with a small amount of labeled data and a large amount of unlabeled data, automatic classification of the eye fundus image of cataract is realized, and the problems of low manual classification efficiency and high labeled data acquisition cost are solved. In the preprocessing stage, texture and color multi-dimensional feature parameters of an image are extracted, and image data are obtained through dimension reduction. In the model construction stage, network training data of double convolution activation layers and double maximum pooling layers are adopted, and the model outputs all levels of probabilities to realize grading. The semi-supervised iterative training adopts a pseudo tag generation and model optimization alternating strategy, and a high-confidence sample expansion training set is screened. In the evaluation stage, the performance of the model is comprehensively measured by using multiple indexes and a visualization technology, and finally the model is deployed and applied to provide reliable support for clinical diagnosis.

The invention belongs to the technical field of image processing, and particularly relates to a passive domain adaptive eye fundus image segmentation method based on adaptive mask and curvature regularization, which comprises the following steps: constructing a teacher-student self-training framework, and generating a pseudo tag by using a weak enhancement teacher model to guide student model training, an adaptive mask consistency strategy is introduced in the training process, adaptive mask processing is carried out on a target domain image, the mask proportion and size are adaptively adjusted according to the sample difficulty and the size of a pseudo-label area, a model is guided to keep prediction consistency under the shielding condition, and thus the pseudo-label reliability and the context modeling capability are improved; meanwhile, by introducing an average negative curvature regularization constraint, an irregular boundary in a prediction result is suppressed, the smoothness and structural continuity of a segmentation result are enhanced, and finally the precision and generalization ability of the model in a cross-device and cross-dataset fundus image segmentation task are improved.

The invention belongs to the field of myopia bottom-of-eye lesion prediction, and discloses a myopia bottom-of-eye lesion progress prediction method and system based on multi-modal sequence data, and the method comprises the steps: extracting fundus image features through employing a local structure guided multi-scale vision Transform; the method comprises the following steps: acquiring table data, extracting features through respective network modules, and carrying out cross-attention mechanism fusion; processing data of irregular time points by using Transform time coding and an LSTM pseudo sequence method, and converting the data into continuous time sequence input; estimating the contribution of each time point data in prediction by using time interval coding and time difference weighting; a weighted attention mechanism is adopted to endow modal information of different time points with different weights, and data features which have the most influence on the progress in different periods are identified; the image features and the table data features are fused through a cross-attention mechanism, and a unified feature vector is formed; and outputting the progress trend of future lesions of the patient. According to the method, the lesion occurrence or progress trend at a specific age or a future time point can be predicted, and a scientific basis is provided for individualized management.

A children myopia prediction method based on multi-modal data fusion comprises the following steps: acquiring and preprocessing children myopia original data including clinical data and color fundus image data; constructing a children myopia prediction model; the children myopia prediction model comprises a feature extraction module, a feature fusion module and a result prediction module; the feature extraction module extracts clinical features of the clinical data and image features of the color fundus image data; the feature fusion module fuses the clinical features and the image features to generate final fusion features; inputting the final fusion feature into a result prediction module to generate a prediction probability; training a children myopia prediction model according to the prediction probability; and inputting the verification set in the child myopia original data into the trained child myopia prediction model to generate a prediction result. According to the method, through a multi-head self-attention mechanism, the problem of dynamically weighting different modal features is solved, and the robustness of the model is remarkably improved.

The invention relates to a diffusion model-based adaptive eye fundus image classification method and system during continuous testing, and relates to the technical field of medical image classification, and the method comprises the steps: carrying out the forward noise addition of an eye fundus image through a diffusion model, obtaining a noise-added image, carrying out the reverse denoising of the noise-added image through the diffusion model, and obtaining a noise-added image; in the reverse denoising process, multiple gradient guides including content keeping guide, consistency guide and style alignment guide are introduced, gradient guide data are introduced into a denoised image for gradient fusion and image reconstruction, and finally, a classifier is utilized to predict the final reconstructed image. According to the method, the diffusion model is introduced to optimize the eye fundus image, so that structure maintenance, style alignment and classification stability optimization of the continuous label-free test image are realized, the continuous label-free test image is gradually close to source domain distribution, domain alignment of image levels is realized, source model parameters do not need to be modified, and system stability and deployability are ensured.

The invention discloses a retina fundus image generation method based on a diffusion model. Firstly, a training data set and a regularization data set are constructed; then, constructing a diffusion model which comprises a variational auto-encoder, a U-Net denoising network and a text encoder; the variational auto-encoder comprises a variational encoder and a variational decoder, and the U-Net denoising network is embedded between the variational encoder and the variational decoder; the structured text prompt passes through a text encoder to obtain a text embedding vector, and the vector is injected into the U-Net denoising network as condition information; the variational encoder compresses the retina fundus image into potential features, the U-Net denoising network carries out denoising on the potential features under the guidance of condition information, and the denoised potential features are reconstructed into a high-resolution retina fundus image through the variational encoder; and finally, generating a retina fundus image based on the pre-trained diffusion model. The controllability of image generation is improved, and the generated image is highly consistent between the focus form and the medical description.

The invention relates to the field of medical image processing and artificial intelligence, in particular to an inflammation active retinopathy prediction system and method based on artificial intelligence, and the system comprises an image processing module which comprises an image acquisition unit, a blood vessel density analysis unit, a blood-retina barrier integrity imaging analysis unit and a data integration module. The core innovation of the invention lies in that a blood vessel density analysis unit constructs a blood vessel structure analysis framework by introducing topology and differential geometry theories, comprises a topological manifold representation module, a curvature flow dynamic analysis module and a non-European interactive network module, maps fundus image data into a topological manifold structure, and extracts topological features of a blood vessel network; curvature flow evolution analysis is carried out based on the blood vessel network topology features, and blood vessel form dynamic features are generated; a non-European metric space of a vascular network and inflammatory factors is constructed, an interaction relationship between the vascular network and the inflammatory factors is analyzed, and vascular-inflammation interaction characteristics and blood-retina barrier integrity index data are integrated.