High-resolution intelligent ophthalmic diagnosis and treatment system and method thereof

Abstract

The invention relates to the technical field of medical treatment, and particularly discloses a high-resolution intelligent ophthalmology diagnosis and treatment system and method, and the system comprises a retina imaging module, an image analysis module, a biological parameter monitoring module, a treatment recommendation module, a data management module, and a patient end application module. The retina imaging module is used for acquiring targeted fundus images by using an advanced fundus imaging technology; the image analysis module is used for automatically analyzing the eye fundus image through a deep learning model to obtain an analysis result; the biological parameter monitoring module is used for monitoring eye biological parameters in real time through non-invasive equipment; the deep learning model is used for analyzing the collected eye fundus image, biological parameters are monitored in real time, data trend analysis is carried out, the diagnosis accuracy of ophthalmic diseases is improved, and a personalized and dynamically-adjusted treatment scheme is provided for a patient; through scientific treatment scheme recommendation and long-term data tracking, accurate decision support is provided for doctors.

Description

Technical Field

[0001] The present invention belongs to the field of medical technology, and specifically relates to a high-resolution intelligent ophthalmology diagnosis and treatment system and method thereof. Background Art

[0002] As an important branch of medicine, ophthalmology has made great progress in recent years. With the high incidence of ophthalmic diseases such as myopia, cataracts, and fundus lesions, people's demand for ophthalmic diagnosis and treatment is increasing, prompting the continuous improvement of ophthalmic technology and equipment. The current status of ophthalmology development is mainly reflected in the following aspects.

[0003] The first is the breakthrough in technological innovation. For example, in recent years, the application of new technologies such as laser treatment for myopia, laser treatment for glaucoma and phacoemulsification of cataracts has provided ophthalmic patients with safer, more accurate and effective treatment methods. The second is the advancement of instruments and equipment. For example, the widespread use of optical coherence tomography (OCT) imaging equipment allows doctors to observe and evaluate the condition of lesions more accurately: the introduction of laser surgical robots for corneal refractive surgery makes the surgical process safer and more effective. The third is the deepening of ophthalmic medical research. Ophthalmic medical research has flourished in recent years, involving cutting-edge fields such as artificial intelligence and genetic technology. Through in-depth research and exploration, people have a deeper understanding of the occurrence and treatment mechanisms of ophthalmic diseases, which provides more options and methods for ophthalmic diagnosis and treatment.

[0004] Retinal fundus images are an important basis for ophthalmologists to diagnose and treat diseases. The resolution of retinal fundus images is directly related to whether doctors can accurately capture the patient's lesion information. However, due to the limitations of shooting equipment, conditions and patient factors, the resolution of many retinal fundus images is not ideal. Retinal blood vessels provide the necessary blood supply to the retina and are the basis of retinal tissue metabolism. By observing and analyzing the structure, morphology, and blood flow of retinal blood vessels, ophthalmologists can preliminarily judge the health of the retina and whether there is a possibility of lesions. In addition, retinal vascular lesions are a common feature of many retinal diseases. For example, diseases such as retinal arteriosclerosis, venous occlusion, and vascular inflammation can cause significant changes in retinal blood vessels. By observing these changes, ophthalmologists can diagnose the type of disease and develop targeted treatment plans. However, low-resolution retinal fundus images are difficult to clearly show the subtle vascular structure and lesions in the retina.

[0005] Therefore, it is necessary to propose a high-resolution intelligent ophthalmic diagnosis and treatment system and method thereof to solve the problem in the prior art that low-resolution retinal fundus images are difficult to clearly show the subtle vascular structures and lesions in the retina.

[0006] The above information disclosed in this background technology is only used to increase the understanding of the background technology of the present invention and therefore, it may include information that does not constitute the prior art known to ordinary technicians in this field. Summary of the invention

[0007] The object of the present invention is to provide a high-resolution intelligent ophthalmic diagnosis and treatment system and method thereof to solve the problems raised in the above-mentioned background technology.

[0008] To achieve the above object, the present invention provides the following technical solutions:

[0009] A high-resolution intelligent ophthalmic diagnosis and treatment system, comprising: a retinal imaging module, an image analysis module, a biological parameter monitoring module, a treatment recommendation module, a data management module, and a patient-side application module;

[0010] The retinal imaging module is used to collect targeted fundus images using advanced fundus imaging technology and send them to the image analysis module;

[0011] The image analysis module is used to automatically analyze the fundus image through a deep learning model to obtain an analysis result;

[0012] The biological parameter monitoring module is used to monitor eye biological parameters in real time through non-invasive equipment and send them to the treatment recommendation module;

[0013] The treatment recommendation module is used to provide a personalized treatment plan for the doctor based on the analysis results and eye biological parameters;

[0014] The data management module is used to store all relevant data of patients, conduct trend analysis through big data, and predict disease development and treatment effects;

[0015] The patient-side application module is used to provide patients with an easy-to-use mobile application that provides functions for uploading health data, making appointments for diagnosis and treatment, viewing diagnosis results, and obtaining personalized health guidance.

[0016] Preferably, the fundus image is segmented using a pre-trained network to obtain a retinal vascular structure map, and the map is used as prior information to guide the generation of a super-resolution image; the fundus image is super-reconstructed using a generative adversarial network method based on the prior information; and the vascular structures and lesions at different levels of the retina are displayed through 3D image reconstruction.

[0017] Preferably, the image is classified at pixel level using a convolutional neural network to obtain segmentation maps of the retina and vascular areas; the analysis process of each segmentation map by the deep learning model is implemented by fine-tuning the pre-trained network; when there are multiple lesion areas, Mask R-CNN is used to perform instance segmentation to distinguish different lesion areas; an analysis report is automatically generated to provide lesion type, location, size, and morphological data; the lesions are staged according to the type, location, and size of the lesions, and the progression of the disease is predicted.

[0018] Preferably, the intraocular pressure, eye movement, and blood flow are monitored to infer the impact of changes in intraocular pressure on retinal health; during the patient's medical treatment, the changing trends of the eye biological parameter data are analyzed to generate a personalized health report.

[0019] Preferably, the corresponding treatment method is recommended according to the type and development stage of the lesion; personalized health knowledge and medication and follow-up reminders are pushed to the patient; the treatment plan is personalized adjusted considering the patient's health status and allergy history; the treatment effect is monitored in real time, and the patient's feedback is obtained to adjust the treatment plan.

[0020] A high-resolution intelligent ophthalmic diagnosis and treatment management method comprises the following steps:

[0021] Step 1: using advanced fundus imaging technology to collect fundus images of patients, and preprocessing the fundus images to optimize image quality;

[0022] Step 2: Use a convolutional neural network to perform pixel-level segmentation on the preprocessed fundus image to obtain a segmentation map of the retina and blood vessel areas, and perform feature extraction on the segmentation map to obtain morphological and structural features of the segmentation map;

[0023] Step 3: Use a deep learning model combined with the above features to classify the segmentation map, identify whether there is an ophthalmic disease, and confirm the type of the ophthalmic disease;

[0024] Step 4: Combine the patient's clinical data and use a machine learning algorithm to perform risk assessment on the ophthalmic disease, obtain risk assessment results, and predict the development trend of the disease to provide doctors with personalized treatment recommendations;

[0025] Step 5: Present the above diagnosis and treatment conclusions to doctors through a visual interface, build an interactive communication channel between doctors and patients, help patients understand their condition, and develop scientific health management and treatment plans;

[0026] Step 6: Regularly review the patient's treatment effect through newly collected fundus images, evaluate the changing trend of the ophthalmic disease, and adjust the treatment plan in a timely manner.

[0027] Preferably, the random noise in the eye image is removed by a Gaussian filtering algorithm; the brightness and contrast of the eye image are improved by using image enhancement technology; illumination normalization processing is performed to eliminate the problem of uneven image illumination caused by differences in illumination conditions; and the fundus image is locally cropped or enhanced as needed to highlight the key areas of the fundus image.

[0028] Preferably, the transfer learning is used to train and optimize the deep learning model using an existing high-quality fundus image dataset; in the case of multiple lesions, the Mask R-CNN instance segmentation algorithm is used to identify and distinguish different lesions in the segmentation map to obtain multiple lesion areas; the degree of lesions is graded according to the morphological and structural characteristics of the ophthalmic disease to help doctors determine the severity of the lesions.

[0029] Preferably, personalized treatment recommendations are generated based on the patient's risk assessment results and disease progression trends; during the treatment process, the content of the treatment recommendations is adjusted through patient feedback and monitoring of subsequent images.

[0030] Preferably, the method uses electronic archives to manage patient data and achieves continuous tracking and health management through a remote monitoring platform.

[0031] Compared with the prior art, the present invention has the following beneficial effects:

[0032] The present invention uses a deep learning model to analyze the collected fundus images, monitors biological parameters and data trend analysis in real time, improves the diagnostic accuracy of ophthalmic diseases, and provides patients with personalized and dynamically adjusted treatment plans; through scientific treatment plan recommendations and long-term data tracking, it provides doctors with accurate decision support, effectively promotes treatment effects, combines with patient-side applications, simplifies the diagnosis and treatment process, and improves patients' medical experience.

[0033] The above summary is for illustrative purposes only and is not intended to be limiting in any way. In addition to the illustrative aspects, embodiments and features described above, further aspects, embodiments and features of the present invention will be readily apparent by reference to the accompanying drawings and the following detailed description. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] Figure 1 This is a framework diagram of the high-resolution intelligent ophthalmic diagnosis and treatment system of the present invention;

[0035] Figure 2 This is a flow chart of the high-resolution intelligent ophthalmic diagnosis and treatment method of the present invention. DETAILED DESCRIPTION

[0036] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0037] Embodiment 1:

[0038] See also Figure 1 As shown, a high-resolution intelligent ophthalmic diagnosis and treatment system includes: a retinal imaging module, an image analysis module, a biological parameter monitoring module, a treatment recommendation module, a data management module, and a patient-side application module;

[0039] The retinal imaging module is used to collect targeted fundus images using advanced fundus imaging technology and send them to the image analysis module;

[0040] The image analysis module is used to automatically analyze the fundus images through a deep learning model to obtain analysis results;

[0041] The biological parameter monitoring module is used to monitor the eye biological parameters in real time through non-invasive equipment and send them to the treatment recommendation module;

[0042] The treatment recommendation module is used to provide doctors with personalized treatment plans based on the analysis results and ocular biological parameters;

[0043] The data management module is used to store all relevant patient data, conduct trend analysis through big data, and predict disease development and treatment effects;

[0044] The patient-side application module is used to provide patients with an easy-to-use mobile application that provides functions such as uploading health data, making appointments for medical treatment, viewing diagnosis results, and obtaining personalized health guidance.

[0045] The retinal imaging module uses advanced fundus imaging technologies such as fundus cameras, optical coherence tomography, fundus fluorescein angiography, and scanning laser ophthalmoscopes to collect targeted fundus images. The pre-trained network is used to segment the fundus image to obtain the retinal vascular structure map, and it is used as prior information to guide the generation of super-resolution images; the generative adversarial network method is used to reconstruct the fundus image in super-resolution based on the prior information; and the vascular structure and lesions at different levels of the retina are displayed through 3D image reconstruction.

[0046] The pre-trained network is a U-Net network trained on a dataset of blood vessel segmentation in prepared retinal fundus images. Through this network, the vascular structure map of the input retinal fundus image can be obtained. The U-Net network contains two parts: an encoder and a decoder. The encoder reduces the size of the feature map through downsampling operations to extract low-resolution information of the image, and the decoder increases the size of the feature map through upsampling operations to extract high-resolution information of the image. In addition, the jump connection used between the upsampling operation and the downsampling operation connects the low-resolution information with the high-resolution information of the corresponding layer to prevent the loss of low-resolution information.

[0047] During the reconstruction process, the vascular structure map is input into the conditional network to further extract the features of the vascular structure. The conditional network consists of a 1×1 convolutional layer, a ReLU activation function, and a 1×1 convolutional layer. The conditional network is used to extract the features of the vascular structure from the vascular structure map without changing the size of the feature map. The output of the conditional network is the prior condition. In the feature extraction part, the spatial feature transformation layer fully integrates the prior condition with the features obtained in the feature extraction part to retain more features of the retinal vascular structure in the feature extraction part. In addition, the feature extraction part also connects the shallow features with the deep features through jump connections, integrating the features extracted by the previous convolution to avoid the loss of shallow features.

[0048] The spatial feature transform layer is a neural network layer designed to enable the network to pay more attention to the spatial relationship in the input image, thereby improving the network's ability to learn the mapping relationship between the input image and the output image. Therefore, the spatial feature transform layer is used to combine the feature map obtained in the feature extraction part with the prior conditions instead of directly concatenating or summing them. The input of the spatial feature transform layer includes the feature map and a set of learnable parameters. These parameters are used to dynamically adjust the weights of each position in the feature map obtained in the feature extraction part, so that the network can pay more attention to specific spatial positions.

[0049] Use convolutional neural networks to classify images at the pixel level and obtain segmentation maps of the retina and vascular regions; implement the deep learning model's analysis of each segmentation map by fine-tuning the pre-trained network;

[0050] Deep learning model: First, use 3x3 convolution to extract features, and use three identical low-frequency feature extraction modules to extract low-frequency global features of the image. Each low-frequency feature extraction module includes a 6x6 convolution with a stride of 2, a spectral normalization layer, and a LeakyReLU activation function. Then use three identical high-frequency feature extraction modules to extract high-frequency detail features of the image. Each high-frequency feature extraction module includes a 3x3 convolution, a spectral normalization layer, and a LeakyReLU activation function. Through the pooling layer corresponding to the low-frequency feature extraction module and the high-frequency feature extraction module, two fully connected layers, and finally through input analysis. The L1 loss function is used to measure the pixel-level difference between the vascular structure map of the real high-resolution fundus image and the vascular structure map of the generated fake high-resolution fundus image.

[0051] When there are multiple lesion areas, Mask R-CNN is used for instance segmentation to distinguish different lesion areas; an analysis report is automatically generated to provide lesion type, location, size, and morphological data; lesions are staged according to their type, location, and size, and the progression of the disease is predicted.

[0052] Monitor intraocular pressure, eye movement, and blood flow to infer the impact of intraocular pressure changes on retinal health; use ophthalmic-specific equipment (such as fundus cameras, tonometers, OCT optical coherence tomography scanners, etc.) for examination. These devices automatically collect the patient's ophthalmic examination data and connect to the system via wired (USB, HDMI, etc.) or wireless (Wi-Fi, Bluetooth, etc.) methods.

[0053] During the patient's consultation, the changing trends of eye biological parameter data are analyzed to generate personalized health reports.

[0054] Recommend corresponding treatment methods based on lesion type and development stage; push personalized health knowledge to patients, as well as reminders for medication and follow-up examinations; make personalized adjustments to treatment plans based on patients' health conditions and allergy history; monitor treatment effects in real time and obtain feedback from patients to adjust treatment plans.

[0055] The patient-side application module transmits and calls data with the data management module in the following ways:

[0056] Health data upload: The patient-side application module uploads the patient's health data, such as vision, intraocular pressure, eye images, etc., through devices (such as smartphones, tablets, etc.). After the data is initially processed locally, it is uploaded to the data management module through encrypted transmission protocols (such as HTTPS, TLS, etc.). The data management module verifies, stores and encrypts the uploaded data to ensure the security and integrity of the data.

[0057] Data storage and processing: The data management module is responsible for storing all relevant data of the patient, including historical health records, diagnosis results, treatment plans, etc. This module also processes the uploaded health data through big data analysis to provide accurate diagnosis support and personalized health guidance.

[0058] Appointment and treatment services: The patient-side application provides appointment and treatment functions. Through interaction with the data management module, it can query the available time of the therapist, the patient's health data records and other information in real time to help patients complete online appointments and obtain treatment feedback. The patient-side application sends the appointment request to the data management module, which matches the data with the doctor's schedule according to the patient's needs and returns the appointment confirmation information.

[0059] Diagnostic results and personalized health guidance: The data management module generates a diagnostic report after the patient is diagnosed and treated, and transmits the diagnostic results to the patient-side application through the API interface. Patients can view their own diagnostic results, treatment recommendations, and personalized health guidance through the application. Patient-side applications usually use standard data formats (such as JSON or XML) to interact with the data management module to ensure accurate transmission and real-time synchronization of information.

[0060] Data synchronization and real-time update: To ensure that the data in the patient-side application and the data management module are consistent, the patient side will regularly pull the latest diagnosis results, treatment progress and health advice through the API interface for data synchronization. This data interaction usually uses technologies such as RESTful API or GraphQL to support efficient and flexible communication.

[0061] Embodiment 2:

[0062] See also Figure 2 As shown, a high-resolution intelligent ophthalmic diagnosis and treatment management method comprises the following steps:

[0063] Step 1: Use advanced fundus imaging technology to collect fundus images of patients, and pre-process the fundus images to optimize image quality;

[0064] Step 2: Use a convolutional neural network to perform pixel-level segmentation on the preprocessed fundus image to obtain a segmentation map of the retina and vascular area, and perform feature extraction on the segmentation map to obtain the morphological and structural features of the segmentation map;

[0065] Step 3: Use the deep learning model combined with the above features to classify the segmentation map, identify whether there is an ophthalmic disease, and confirm the type of ophthalmic disease;

[0066] Step 4: Combine the patient’s clinical data and use machine learning algorithms to conduct an eye disease risk assessment, obtain risk assessment results, and predict the development trend of the disease to provide doctors with personalized treatment recommendations;

[0067] Step 5: Present the above diagnosis and treatment conclusions to doctors through a visual interface, build an interactive communication channel between doctors and patients, help patients understand their condition, and develop scientific health management and treatment plans;

[0068] Step 6. Regularly review the patient's treatment effects through newly collected fundus images, evaluate the changing trends of ophthalmic diseases, and adjust the treatment plan in a timely manner.

[0069] The random noise in the eye image is removed by using the Gaussian filtering algorithm; the brightness and contrast of the eye image are improved by using image enhancement technology; illumination normalization processing is performed to eliminate the uneven illumination of the image caused by differences in illumination conditions; and the fundus image is locally cropped or enhanced as needed to highlight the key areas of the fundus image.

[0070] Through transfer learning, the existing high-quality fundus image dataset is used to train and optimize the deep learning model. In the case of multiple lesions, the Mask R-CNN instance segmentation algorithm is used to identify and distinguish different lesions in the segmentation map to obtain multiple lesion areas. The degree of lesions is graded according to the morphological and structural characteristics of ophthalmic diseases to help doctors determine the severity of the lesions.

[0071] The Mask R-CNN instance segmentation algorithm is used to infer the segmentation map. The inference process is divided into the following steps:

[0072] (1) Input image processing: preprocess the input image, including resizing and normalization;

[0073] (2) Generate candidate regions: The RPN network generates candidate regions based on the input image;

[0074] (3)RoIAlign: The candidate region is processed through the RoIAlign operation to obtain a fine feature map;

[0075] (4) Classification and bounding box regression: Classify each candidate region and predict the bounding box of the lesion area;

[0076] (5) Mask prediction: For each lesion area, the model generates a pixel-level mask that represents the specific area of ​​the lesion;

[0077] (6) Differentiation of multiple lesion instances: The model can not only segment the lesion area, but also distinguish multiple lesion instances in the image. Each lesion instance will have an independent mask to represent its different areas.

[0078] Generate personalized treatment recommendations based on the patient's risk assessment results and disease development trends; during the treatment process, adjust the treatment recommendations through patient feedback and follow-up image monitoring.

[0079] This method uses electronic archives to manage patient data and achieves continuous tracking and health management through a remote monitoring platform.

[0080] From the above, it can be seen that the present invention uses a deep learning model to analyze the collected fundus images, monitor biological parameters and data trend analysis in real time, improve the diagnostic accuracy of ophthalmic diseases, and provide patients with personalized and dynamically adjusted treatment plans; through scientific treatment plan recommendations and long-term data tracking, it provides doctors with accurate decision support, effectively promotes treatment effects, combines with patient-side applications, simplifies the diagnosis and treatment process, and improves patients' medical experience.

[0081] Embodiment 3:

[0082] The embodiment of the present invention further provides a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, each process of the above-mentioned diagnosis and treatment system embodiment is implemented, and the same technical effect can be achieved. To avoid repetition, it is not repeated here. Among them, the computer-readable storage medium, such as read-only memory (Read-Only Memory, referred to as ROM), random access memory (Random Access Memory, referred to as RAM), magnetic disk or optical disk, etc.

[0083] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "examples", "specific examples" or "some examples" etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine different embodiments or examples described in this specification and the features of different embodiments or examples, unless they are contradictory.

[0084] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems) and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor or other programmable data processing device to generate a program, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A way to specify functions in one or more boxes.

[0085] In the drawings of the embodiments disclosed in the present invention, only the structures involved in the embodiments disclosed in the present invention are involved, and other structures can refer to the general design. In the absence of conflict, the same embodiment and different embodiments of the present invention can be combined with each other.

[0086] The flowcharts shown in the accompanying drawings are only examples and do not necessarily include all the contents and operations / steps, nor must they be executed in the order described. For example, some operations / steps may also be decomposed, combined or partially merged, so the actual execution order may change according to actual conditions.

[0087] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A high-resolution intelligent ophthalmic diagnosis and treatment system, characterized in that: include: Retinal imaging module, image analysis module, biological parameter monitoring module, treatment recommendation module, data management module, and patient-side application module; The retinal imaging module is used to collect targeted fundus images using advanced fundus imaging technology and send them to the image analysis module; The image analysis module is used to automatically analyze the fundus image through a deep learning model to obtain an analysis result; The biological parameter monitoring module is used to monitor eye biological parameters in real time through non-invasive equipment and send them to the treatment recommendation module; The treatment recommendation module is used to provide a personalized treatment plan for the doctor based on the analysis results and eye biological parameters; The data management module is used to store all relevant data of patients, conduct trend analysis through big data, and predict disease development and treatment effects; The patient-side application module is used to provide patients with an easy-to-use mobile application that provides functions for uploading health data, making appointments for diagnosis and treatment, viewing diagnosis results, and obtaining personalized health guidance.

2. A high-resolution intelligent ophthalmic diagnosis and treatment system according to claim 1, characterized in that: The retinal imaging module is also used for: Using a pre-trained network to perform image segmentation on the fundus image to obtain a retinal vascular structure map, and using the map as prior information to guide the generation of a super-resolution image; Performing super-resolution reconstruction on the fundus image using a generative adversarial network method according to the prior information; The vascular structure and lesions at different levels of the retina are displayed through 3D image reconstruction.

3. The high-resolution intelligent ophthalmic diagnosis and treatment system according to claim 2, characterized in that: The image analysis module is also used for: Use convolutional neural networks to classify images at the pixel level and obtain segmentation maps of the retina and vascular regions; Implementing the analysis operation of the deep learning model on each of the segmentation maps by fine-tuning the pre-trained network; When there are multiple lesion areas, Mask R-CNN is used for instance segmentation to distinguish different lesion areas; Automatically generate analysis reports, providing lesion type, location, size, and morphology data; The lesions are staged according to their type, location and size, and the progression of the disease is predicted.

4. The high-resolution intelligent ophthalmic diagnosis and treatment system according to claim 3, characterized in that: The biological parameter monitoring module is also used for: Monitor intraocular pressure, eye movement, and blood flow to infer the impact of changes in intraocular pressure on retinal health; During the patient's consultation, the changing trends of the eye biological parameter data are analyzed to generate a personalized health report.

5. The high-resolution intelligent ophthalmic diagnosis and treatment system according to claim 4, characterized in that: The treatment recommendation module is also used to: Recommend corresponding treatment methods according to the type and stage of the lesion; Push personalized health information to patients, as well as reminders for medication and follow-up examinations; The treatment plan is individually adjusted according to the patient's health status and allergy history; Monitor the treatment effect in real time and obtain patient feedback to adjust the treatment plan.

6. A high-resolution intelligent ophthalmic diagnosis and treatment management method, characterized in that: The following steps are involved: Step 1: using advanced fundus imaging technology to collect fundus images of patients, and preprocessing the fundus images to optimize image quality; Step 2: Use a convolutional neural network to perform pixel-level segmentation on the preprocessed fundus image to obtain a segmentation map of the retina and blood vessel areas, and perform feature extraction on the segmentation map to obtain morphological and structural features of the segmentation map; Step 3: Use a deep learning model combined with the above features to classify the segmentation map, identify whether there is an ophthalmic disease, and confirm the type of the ophthalmic disease; Step 4: Combine the patient's clinical data and use a machine learning algorithm to perform a risk assessment of the ophthalmic disease, obtain a risk assessment result, and predict the development trend of the disease to provide doctors with personalized treatment recommendations; Step 5: Present the above diagnosis and treatment conclusions to doctors through a visual interface, build an interactive communication channel between doctors and patients, and formulate scientific health management and treatment plans; Step 6: Regularly review the patient's treatment effect through newly collected fundus images, evaluate the changing trend of the ophthalmic disease, and adjust the treatment plan in a timely manner.

7. A high-resolution intelligent ophthalmic diagnosis and treatment management method according to claim 6, characterized in that: The preprocessing of the fundus image to optimize the image quality includes: Removing random noise in the eye image by using a Gaussian filtering algorithm; Improving the brightness and contrast of the eye image using image enhancement technology; Perform illumination normalization to eliminate uneven image illumination caused by differences in illumination conditions; The fundus image is locally cropped or enhanced according to actual needs.

8. A high-resolution intelligent ophthalmic diagnosis and treatment management method according to claim 7, characterized in that: The method of using a deep learning model in combination with the above features to classify the segmentation map and identify whether an ophthalmic disease exists includes: Use transfer learning to train and optimize deep learning models using existing high-quality fundus image datasets; In the case of multiple lesions, the Mask R-CNN instance segmentation algorithm is used to identify and distinguish different lesions in the segmentation map to obtain multiple lesion regions; The severity of the lesions is graded according to the morphological and structural characteristics of the ophthalmic disease.

9. A high-resolution intelligent ophthalmic diagnosis and treatment management method according to claim 8, characterized in that: The prediction of the disease development trend provides doctors with personalized treatment suggestions, including: Generate personalized treatment recommendations based on the patient's risk assessment results and disease development trends; During treatment, treatment recommendations are adjusted based on patient feedback and follow-up image monitoring.

10. A high-resolution intelligent ophthalmic diagnosis and treatment management method according to claim 9, characterized in that: The method adopts electronic archives to manage patient data and realizes continuous tracking and health management through a remote monitoring platform.

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