AI ophthalmology auxiliary diagnosis interactive instrument

Abstract

The invention discloses an AI ophthalmology auxiliary diagnosis interactive instrument. The invention belongs to the technical field of ophthalmologic diagnosis, and discloses two full-automatic fundus cameras, transverse guide rails are arranged at the upper ends and the lower ends of the two full-automatic fundus cameras, and transverse guide blocks are installed at the upper ends and the lower ends of the two full-automatic fundus cameras. The full-automatic fundus camera is movably connected with the upper transverse guide rail and the lower transverse guide rail in a clamped mode through the transverse guide blocks. Through the cooperation among the two transverse guide blocks, the single full-automatic fundus camera and the linkage assembly, during data acquisition, the wearing process of the two full-automatic fundus cameras can be rapidly completed only by pulling the movable plate downwards and keeping the wearing posture, and the data acquisition efficiency is improved. The two full-automatic fundus cameras are kept at the front positions of the eyes for a long time, hand holding is not needed, equipment shaking caused by long-time hand holding can be avoided, the two full-automatic fundus cameras can be used for collecting data of the two eyes at the same time, the collecting time is further shortened, and the collecting quality and the collecting efficiency are further improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of ophthalmic diagnosis, and specifically relates to an AI ophthalmic auxiliary diagnosis interactive instrument. Background Art

[0002] The Ophthalmology Auxiliary Diagnostic Interactive Device is an advanced medical device designed to provide ophthalmologists with an accurate eye health assessment tool. Combining high-resolution imaging technology, artificial intelligence algorithms, and an interactive interface, it quickly and comprehensively scans the patient's eyes, covering multiple areas including the retina, cornea, and fundus. Through non-contact testing, the device captures detailed ophthalmic data, such as visual acuity, intraocular pressure, and fundus images, assisting doctors in early screening and disease diagnosis.

[0003] Conventional interactive ophthalmic auxiliary diagnostic instruments mainly use fully automatic fundus cameras to collect fundus information. However, the current collection method is to use a handheld fully automatic fundus camera to collect information from both eyes separately. Due to the long collection time, holding the camera for a long time will also cause hand soreness, causing the device to shake and affect the accuracy of information collection. At the same time, it is impossible to achieve simultaneous collection of information from both eyes, and the collection efficiency is low.

[0004] At the same time, conventional ophthalmic auxiliary diagnostic interactive instruments still have shortcomings in actual use, namely, image annotation relies on manual labor and has confusing standards. For example, different institutions have different definitions of lesion grading, which leads to limited model generalization ability and increased misdiagnosis rate at the grassroots level. Secondly, multi-source data (such as OCT and fundus color photos) are difficult to integrate and analyze due to incompatible formats and varying resolutions of equipment manufacturers, and privacy barriers hinder cross-institutional data sharing, further compressing the size of the effective training set and restricting the improvement of algorithm accuracy.

[0005] At the same time, deep learning only outputs results but cannot explain the basis (such as whether the misjudgment is due to image artifacts or lesion characteristics), which causes doctors to doubt the credibility of the diagnosis. On the other hand, the model relies too much on static images and lacks the ability to integrate and analyze the patient's symptoms, medical history and disease course. For example, dry eye ignores tear test data, and glaucoma staging is separated from the time dimension tracking, which ultimately reduces the diagnostic accuracy of complex cases. Summary of the Invention

[0006] The purpose of the present invention is to provide an AI ophthalmology-assisted diagnosis interactive instrument to solve the problems raised in the above-mentioned background technology.

[0007] In order to achieve the above-mentioned objectives, the present invention provides the following technical solutions: an AI ophthalmic auxiliary diagnosis interactive instrument, comprising two fully-automatic fundus cameras, wherein the upper and lower ends of the two fully-automatic fundus cameras are provided with transverse guide rails, and the upper and lower ends of the two fully-automatic fundus cameras are both equipped with transverse guide blocks, and the fully-automatic fundus cameras are movably connected with the upper and lower transverse guide rails through the transverse guide blocks. A linkage component is provided on the front of the two fully-automatic fundus cameras, and the linkage component is connected to the two fully-automatic fundus cameras. A movable plate is installed on the front of the linkage component, and a longitudinal guide rail is fixedly installed in the middle of the bottom end of the transverse guide rail below. A longitudinal guide block is fixedly installed at the rear end of the movable plate, and the movable plate is movably connected with the longitudinal guide rail through the longitudinal guide block. The movable plate moves up and down relative to the longitudinal guide rail, and when the movable plate moves up and down, the two fully-automatic fundus cameras are driven to move left and right relative to the transverse guide rail through the linkage component.

[0008] As a further technical solution of the present invention, the linkage assembly includes a first fixed seat, which is connected to the middle part of the front of the upper transverse guide block, and the end of the first fixed seat away from the transverse guide block is movably connected to a connecting rod through a rotating shaft.

[0009] As a further technical solution of the present invention, one end of the connecting rod away from the first fixing seat is movably connected to the second fixing seat via a rotating shaft, and the bottom end of the second fixing seat is connected to the top end of the movable plate.

[0010] As a further technical solution of the present invention, a limit spring is fixedly installed on the top of the inner cavity of the longitudinal guide rail, the bottom end of the limit spring is connected to the top of the longitudinal guide block, and a threaded sleeve is fixedly installed on the front of the movable plate, and the internal thread of the threaded sleeve is connected to a screw rod.

[0011] As a further technical solution of the present invention, an extension rod is movably installed at the bottom end of the screw rod, and a base is fixedly installed at the bottom end of the extension rod. A splint is fixedly installed on the relatively distant sides of the two fully automatic fundus cameras. When the limit spring is in the initial state, the distance between the base and the transverse guide rail is the minimum and the distance between the two fully automatic fundus cameras is the maximum.

[0012] An AI ophthalmology-assisted diagnostic interactive instrument also includes the following diagnostic methods: S1: Multimodal data normalization and enhancement framework; S2: Explainable Algorithms and Dynamic Decision Systems; S3: Clinical validation and continuous optimization mechanism; S4: Cross-platform deployment and privacy protection; The multimodal data standardization and enhancement framework includes a unified multi-source data acquisition protocol, a semi-automatic annotation tool chain, and a dynamic data enhancement strategy. The explainability algorithm and dynamic decision-making system include a knowledge graph-driven diagnostic logic tree, contrastive learning-enhanced feature decoupling, and a multimodal dynamic reasoning engine. The clinical verification and continuous optimization mechanism includes multi-center blind test verification and model iterative optimization protocol. The cross-platform deployment and privacy protection include a hybrid cloud deployment architecture and dynamic permission management.

[0013] As a further technical solution of the present invention, the unified multi-source data acquisition protocol includes requiring all equipment to be compatible with international medical imaging standards, with an image resolution of not less than 2048×2048 and a sampling rate of not less than 40 frames / second. Based on the standards of the International Society of Ophthalmology, a labeling knowledge base of 30 types of labels such as the 5-level classification of diabetic retinopathy and glaucoma staging is constructed. The labeling accuracy must reach the pixel level, and the error is controlled within 5 microns. The semi-automatic labeling tool chain includes a deep learning model with an encoder depth of 4 and an initial learning rate of 1e-4 for lesion pre-segmentation, reducing the manual correction time by 70%. Each batch of data must be cross-reviewed by 3 deputy chief physicians, and the labeling consistency Kappa value is not less than 0.85. The dynamic data enhancement strategy includes applying random rotation of ±30 degrees, scaling by 0.8 to 1.2 times, adding Gaussian noise σ=0.01, and adjusting the brightness by ±15%. The data set size is expanded to 10 times the original, and cross-modal paired data is generated based on a generative adversarial network model.

[0014] As a further technical solution of the present invention, the knowledge graph-driven diagnostic logic tree includes constructing a knowledge graph containing 5,000 clinical pathways. For example, the diagnosis of macular degeneration requires that the foveal thickness of the optical coherence tomography scan is more than 300 microns and cystic edema is present. When outputting the diagnostic pathway diagram, the nodes are associated with disease features, the edge weights show confidence, and click-through tracing to the original image area is supported. The contrast learning enhanced feature decoupling includes using a contrast loss function with a temperature parameter τ=0.07 and a batch size of 512 to separate the normal and pathological feature spaces, and displaying the key lesion areas through heat map overlays with a transparency of 0.6 and a threshold of 0.3. The multimodal dynamic inference engine includes deploying a long and short-term memory network with 128 hidden layers and a time window of 3 consecutive examinations to analyze the progression of the disease. The initial diagnostic confidence threshold is set at 90%, and the multi-expert consultation process is automatically triggered when it is lower than the threshold.

[0015] As a further technical solution of the present invention, the multi-center blind test verification includes covering 10 hospitals, with a sample size of no less than 50,000 cases, and using traditional manual diagnosis as the gold standard for comparison. The performance indicators require AUC to be no less than 0.98, F1-score to be no less than 0.93, and misdiagnosis rate to be no more than 5%. The model iterative optimization protocol includes incremental learning updates every month at a learning rate of 1e-5 and a new data ratio of 20%, establishing a misdiagnosis database with a capacity of no less than 1PB, automatically labeling error types and triggering retraining.

[0016] As a further technical solution of the present invention, the hybrid cloud deployment architecture includes the use of NVIDIA Jetson AGX Xavier with a computing power of 32TOPS on the local side to achieve real-time inference within 3 seconds. The federated learning framework needs to be connected to more than 50 hospitals, and differential privacy noise ε=0.1 is applied to ensure data security. The dynamic permission management includes the doctor's permission to modify the confidence threshold, the technician's permission to only view the heat map, the patient's permission to preview the report, the data transmission layer uses AES-256 encryption, and the storage end deploys the SGX trusted execution environment.

[0017] The beneficial effects of the present invention are as follows: (1) The present invention utilizes the cooperation between two lateral guide blocks and a single fully automatic fundus camera and a linkage assembly, so that when collecting data, the user only needs to pull down the movable plate and maintain the wearing posture to quickly complete the wearing process of the two fully automatic fundus cameras. The two fully automatic fundus cameras can be kept in the front position of the eyes for a long time without holding them, which can avoid the device shaking caused by long-term holding. In addition, the two fully automatic fundus cameras can simultaneously collect data from both eyes, further shortening the collection time and improving the collection quality and efficiency.

[0018] (2) This invention significantly improves the accuracy and efficiency of ophthalmic disease diagnosis through standardized data integration and intelligent analysis. The multimodal data fusion framework breaks through the limitations of traditional single-device analysis, achieves a deep correlation between image features and pathological information, and accurately identifies the cross-features of early lesions and complex diseases. The intelligent annotation system combines medical consensus and algorithm prediction to significantly reduce manual annotation bias and provide a high-quality data foundation for model training. The dynamic enhancement strategy simulates the optical interference and equipment differences in real clinical scenarios, enhancing the model's adaptability to diverse imaging conditions. The knowledge graph-driven reasoning system constructs a logical network between disease features, enhances diagnostic transparency and traceability through visual decision paths, and helps doctors quickly verify the medical basis of AI judgments. The time series analysis model captures the dynamic evolution of the disease course and provides a scientific basis for the timing of intervention for chronic eye diseases.

[0019] (3) The present invention takes into account both real-time diagnostic response and deep computing requirements through a hybrid deployment model, adapts to the equipment conditions of medical institutions at different levels, and builds a full-process data security barrier through dynamic authority management to ensure the controllable flow of sensitive information in sharing. The interpretable algorithm establishes a trust foundation for human-computer collaboration through feature decoupling and heat map positioning, assisting doctors in accurately identifying key evidence. The incremental learning mechanism enables the system to continuously track changes in disease spectrum and updates to diagnosis and treatment standards, maintaining technological cutting-edge capabilities. The targeted optimization strategy for misdiagnosis cases strengthens the model's ability to identify rare lesions and promotes the continuous evolution of the diagnostic system. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Figure 1 It is a schematic diagram of the overall structure of the present invention; Figure 2 It is an exploded schematic diagram of the longitudinal guide rail, movable plate and screw rod structure of the present invention; Figure 3 This is an exploded schematic diagram of the fully automatic fundus camera and the transverse guide rail structure of the present invention; Figure 4 Schematic diagram of the coordination of the fully automatic fundus camera and linkage assembly structure of the present invention; Figure 5 Schematic diagram of the overall diagnostic method of the present invention; Figure 6 Schematic diagram of the process of the multimodal data standardization and enhancement framework of the present invention; Figure 7 Schematic diagram of the process of the explainability algorithm and dynamic decision-making system of the present invention; Figure 8 Schematic diagram of the process of clinical verification and continuous optimization mechanism of the present invention; Figure 9 Schematic diagram of the cross-platform deployment and privacy protection process of the present invention.

[0021] In the figure: 1. Fully automatic fundus camera; 2. Horizontal guide rail; 3. Longitudinal guide rail; 4. Linkage assembly; 401. First fixed seat; 402. Second fixed seat; 403. Connecting rod; 5. Movable plate; 6. Threaded sleeve; 7. Longitudinal guide block; 8. Limit spring; 9. Screw rod; 10. Extension rod; 11. Bottom support; 12. Clamping plate; 13. Horizontal guide block. DETAILED DESCRIPTION

[0022] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. 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 making creative efforts are within the scope of protection of the present invention.

[0023] like Figures 1 to 9 As shown, in an embodiment of the present invention, an AI ophthalmological auxiliary diagnosis interactive instrument includes two fully-automatic fundus cameras 1, and transverse guide rails 2 are provided at the upper and lower ends of the two fully-automatic fundus cameras 1. Transverse guide blocks 13 are installed at the upper and lower ends of the two fully-automatic fundus cameras 1. The fully-automatic fundus cameras 1 are movably connected to the upper and lower transverse guide rails 2 through the transverse guide blocks 13. A linkage component 4 is provided on the front of the two fully-automatic fundus cameras 1. The linkage component 4 is connected to the two fully-automatic fundus cameras 1. A movable plate 5 is installed on the front of the linkage component 4. A longitudinal guide rail 3 is fixedly installed in the middle of the bottom end of the lower transverse guide rail 2. A longitudinal guide block 7 is fixedly installed at the rear end of the movable plate 5. The movable plate 5 is movably connected to the longitudinal guide rail 3 through the longitudinal guide block 7. The movable plate 5 moves up and down relative to the longitudinal guide rail 3. When the movable plate 5 moves up and down, the two fully-automatic fundus cameras 1 are driven to move left and right relative to the transverse guide rail 2 through the linkage component 4.

[0024] Before use, the two fully automatic fundus cameras 1 are powered by built-in batteries, and the device is adjusted to ensure that the base 11 can support the chin.

[0025] like Figure 1 and Figure 2 as well as Figure 3 and Figure 4 As shown, the linkage assembly 4 includes a first fixed seat 401, the first fixed seat 401 is connected to the middle part of the front of the upper transverse guide block 13, the end of the first fixed seat 401 away from the transverse guide block 13 is movably connected to a connecting rod 403 through a rotating shaft, and the end of the connecting rod 403 away from the first fixed seat 401 is movably connected to a second fixed seat 402 through a rotating shaft, the bottom end of the second fixed seat 402 is connected to the top of the movable plate 5, and the top end of the inner cavity of the longitudinal guide rail 3 is fixedly installed with a limit spring 8, and the limit spring The bottom end of 8 is connected to the top end of the longitudinal guide block 7, the front of the movable plate 5 is fixedly installed with a threaded sleeve 6, the internal thread of the threaded sleeve 6 is connected with a screw rod 9, the bottom end of the screw rod 9 is movably installed with an extension rod 10, the bottom end of the extension rod 10 is fixedly installed with a base 11, and the two fully automatic fundus cameras 1 are fixedly installed with a splint 12 on the relatively distant side. When the limit spring 8 is in the initial state, the distance between the base 11 and the transverse guide rail 2 is the minimum and the distance between the two fully automatic fundus cameras 1 is the maximum.

[0026] At this time, the distance between the base 11 and the fully automatic fundus camera 1 can be adjusted according to the distance between the eyes and the chin of the person to be tested, that is, by rotating the screw 9, the extension rod 10 at the bottom and the base 11 can be moved upward or downward until the distance between them and the fully automatic fundus camera 1 meets the requirements; When fundus data photography is required, the two fully-automatic fundus cameras 1 can be placed in the front position of the eye, and a downward pulling force can be applied to the movable plate 5 at the same time. At this time, the screw rod 9, the extension rod 10 and the base support 11 can be moved downward synchronously, and the limit spring 8 is stretched, and the two connecting rods 403 are deflected toward the middle and a thrust is applied to the two fully-automatic fundus cameras 1. At this time, the two fully-automatic fundus cameras 1 can be relatively close to each other under the action of the transverse guide rail 2, and the two transverse guide blocks 13 are driven to be relatively close to each other until the two transverse guide blocks 13 contact the temporal bones on both sides and clamp the temporal bones through the two transverse guide blocks 13. At this time, the base support 11 can be brought into contact with the chin, and the chin can be used to prevent the base support 11 from automatically moving upward, that is, to prevent the limit spring 8 from resetting. At this time, the two transverse guide blocks 13 and the single base support 11 can complete the triangular fixation, and keep the two fully-automatic fundus cameras 1 in the front of the eye, and complete the data acquisition process.

[0027] By utilizing the cooperation between the two lateral guide blocks 13 and the single fully-automatic fundus camera 1 and the linkage component 4, when collecting data, it is only necessary to pull down the movable plate 5 and maintain the wearing posture to quickly complete the wearing process of the two fully-automatic fundus cameras 1. The two fully-automatic fundus cameras 1 can be kept in the front position of the eyes for a long time without having to be held by hand, which can avoid the device shaking caused by long-term holding. In addition, the two fully-automatic fundus cameras 1 can be used to collect data from both eyes at the same time, further shortening the collection time and improving the collection quality and efficiency.

[0028] An AI ophthalmology-assisted diagnostic interactive instrument also includes the following diagnostic methods: S1: Multimodal data normalization and enhancement framework; S2: Explainable Algorithms and Dynamic Decision Systems; S3: Clinical validation and continuous optimization mechanism; S4: Cross-platform deployment and privacy protection; The multimodal data standardization and enhancement framework includes a unified multi-source data acquisition protocol, a semi-automated annotation tool chain, and a dynamic data enhancement strategy. The explainability algorithm and dynamic decision-making system include a knowledge graph-driven diagnostic logic tree, contrastive learning-enhanced feature decoupling, and a multimodal dynamic reasoning engine. The clinical verification and continuous optimization mechanism includes multi-center blind test verification and model iterative optimization protocol. Cross-platform deployment and privacy protection include a hybrid cloud deployment architecture and dynamic permission management.

[0029] Through intelligent reconstruction of the entire process, the accuracy of early screening and the effectiveness of differentiated diagnosis of ophthalmic diseases are effectively improved. A deep fusion framework for multimodal heterogeneous data transcends the limitations of a single signal dimension, constructing a multidimensional mapping model of anatomical structure and functional metabolism, significantly enhancing the ability to detect hidden lesions. A knowledge graph-driven dynamic inference engine establishes a spatiotemporal correlation network of disease evolution trajectories, enabling progressive tracing and differential diagnosis of complex lesions. Semi-automated annotation and enhancement strategies dynamically expand the feature space during data flow, overcoming the bottleneck of model generalization in small sample scenarios. A lightweight deployment architecture, leveraging a two-tiered computing system with intelligent decision-making at the edge and collaborative optimization in the cloud, ensures real-time diagnosis while maintaining high-level analytical performance. The device trust verification system employs a dual-security mechanism of decision-making process visualization and misdiagnosis risk prediction, strengthening clinical trust in AI diagnostic results. A privacy-preserving computing framework, leveraging a data-available-but-invisible approach, enables the secure sharing and value release of diagnostic and treatment information, supporting the compliant circulation and joint modeling of ophthalmic big data across institutions.

[0030] like Figure 6 As shown, the unified multi-source data acquisition protocol requires all equipment to be compatible with international medical imaging standards, with an image resolution of no less than 2048×2048 and a sampling rate of no less than 40 frames per second. Based on the standards of the International Society of Ophthalmology, a labeling knowledge base containing 30 types of labels such as the five-level classification of diabetic retinopathy and glaucoma staging is constructed. The labeling accuracy must reach the pixel level, and the error is controlled within 5 microns. The semi-automated labeling tool chain includes a deep learning model with an encoder depth of 4 and an initial learning rate of 1e-4 for lesion pre-segmentation, reducing manual correction time by 70%. Each batch of data must be cross-reviewed by three deputy chief physicians, and the labeling consistency Kappa value must be no less than 0.85. The dynamic data enhancement strategy includes applying random rotation of ±30 degrees, scaling by 0.8 to 1.2 times, adding Gaussian noise σ=0.01, and adjusting the brightness by ±15%. The dataset size is expanded to 10 times the original size, and cross-modal paired data are generated based on the generative adversarial network model.

[0031] Standardized data acquisition and processing procedures significantly improve the accuracy and clinical applicability of ophthalmic imaging diagnosis. Unified data compatibility standards ensure the temporal and spatial comparability of multi-source heterogeneous images, laying a solid foundation for subsequent in-depth analysis. High-precision image acquisition standards effectively preserve subtle structural features such as fundus vascular texture and the optic nerve fiber layer, enhancing the identification of early pathological changes. A structured annotation knowledge base systematically integrates clinical diagnostic experience, establishing a precise mapping between disease characterization and grading criteria, and eliminating subjective judgment. Intelligent pre-segmentation algorithms and a human-assisted collaboration mechanism ensure precise localization of lesion boundaries while significantly improving annotation efficiency, achieving a deep coupling of medical prior knowledge with data features. A multi-faceted review mechanism establishes a three-dimensional quality control network to ensure the medical authority of training data and the reliability of annotations. A composite data augmentation strategy effectively expands the coverage of lesion morphology by simulating the optical variations of real clinical scenarios, enhancing the model's robustness to device variations and imaging interference. Cross-modal data generation technology overcomes the limitations of traditional single-modality analysis, establishing correlations between anatomical structure and functional metabolism, providing a multi-dimensional chain of evidence for cross-validation of complex diseases.

[0032] like Figure 7 As shown, the knowledge graph-driven diagnostic logic tree includes the construction of a knowledge graph containing 5,000 clinical pathways. For example, the diagnosis of macular degeneration requires that the foveal thickness of the optical coherence tomography scan is more than 300 microns and there is cystic edema. When outputting the diagnostic pathway diagram, the nodes are associated with disease features, the edge weights show the confidence, and click-through tracing to the original image area is supported. Contrast learning enhances feature decoupling, including the use of a contrast loss function with a temperature parameter τ=0.07 and a batch size of 512 to separate the normal and pathological feature spaces. The key lesion areas are displayed by overlaying a heat map with a transparency of 0.6 and a threshold of 0.3. The multimodal dynamic reasoning engine includes the deployment of a long-short-term memory network with 128 hidden layers and a time window of 3 consecutive examinations to analyze the progression of the disease. The initial diagnostic confidence threshold is set at 90%, and when it is lower than the threshold, the multi-expert consultation process is automatically triggered.

[0033] Through structured knowledge integration and intelligent reasoning mechanisms, the logical rigor and transparency of clinical decision-making in ophthalmic diagnosis and treatment are significantly improved. The knowledge-driven diagnostic logic tree systematically integrates massive clinical experience and evidence-based evidence, constructs a three-dimensional correlation network between disease features, and realizes dynamic optimization and visual traceability of diagnostic pathways. Feature decoupling technology effectively suppresses feature confusion in complex lesions by strengthening the distinction between normal and abnormal physiological representations, assisting doctors in accurately locating key pathological changes. The dynamic reasoning engine integrates time series analysis and multimodal association to establish a spatial-temporal model of disease evolution, enhancing the ability to predict the progression trend of chronic eye diseases. The intelligent diagnostic support system automatically identifies difficult cases through a confidence assessment mechanism, promptly initiates collaborative diagnosis and treatment processes, and reduces the risk of missed diagnosis and misjudgment. The visual traceability function establishes a mapping bridge between diagnostic conclusions and original images, enhancing the verifiability of the AI ​​reasoning process for clinicians.

[0034] like Figure 8 As shown, the multi-center blind test validation includes covering 10 hospitals, with a sample size of no less than 50,000 cases, and using traditional manual diagnosis as the gold standard for comparison. The performance indicators require AUC to be no less than 0.98, F1-score to be no less than 0.93, and misdiagnosis rate to be no more than 5%. The model iterative optimization protocol includes incremental learning updates at a learning rate of 1e-5 and a new data ratio of 20% every month, establishing a misdiagnosis database with a capacity of no less than 1PB, automatically labeling error types and triggering retraining.

[0035] Through rigorous clinical validation and continuous evolution mechanisms, a deep coupling bridge between intelligent diagnostic systems and clinical practice is established. The multi-center collaborative verification mechanism eliminates single-center data bias and establishes an objective evaluation benchmark for diagnostic efficacy based on real-world diagnosis and treatment scenarios, ensuring the universality and clinical applicability of technological achievements. The dynamic optimization framework continuously absorbs the latest clinical data to enable the diagnostic model to dynamically track the evolution of the disease spectrum and maintain technological cutting-edge capabilities. The closed-loop feedback system intelligently identifies diagnostic blind spots and borderline cases, forming a self-optimizing capability evolution path and targeted enhancement of the recognition ability of rare lesions. The in-depth analysis mechanism of misdiagnosed cases reveals the weak links in the model's decision-making and promotes the refined iterative upgrade of diagnostic logic. The full-process quality control system transforms the accumulated experience of clinical experts into quantifiable optimization indicators, realizing the two-way empowerment of artificial intelligence and medical wisdom.

[0036] like Figure 9As shown, the hybrid cloud deployment architecture includes the use of NVIDIA Jetson AGXXavier with a computing power of 32TOPS on the local side to achieve real-time inference within 3 seconds. The federated learning framework needs to be connected to more than 50 hospitals, and differential privacy noise ε=0.1 is applied to ensure data security. Dynamic permission management includes doctor permissions to modify the confidence threshold, technician permissions to only view heat maps, patient permissions to preview reports with restrictions, AES-256 encryption at the data transmission layer, and SGX trusted execution environment deployed on the storage side.

[0037] Through a layered security architecture and collaborative computing model, intelligent scheduling and privacy protection of ophthalmic diagnosis and treatment resources are achieved. The hybrid computing framework effectively integrates the value of distributed medical data while ensuring real-time diagnosis, breaking through the bottleneck of single-point computing power. The dynamic permission control system establishes a hierarchical access mechanism based on clinical role characteristics, ensuring traceability and closed-loop accountability throughout the entire lifecycle of diagnosis and treatment data. End-to-end encrypted transmission and a trusted computing environment form a dual protection barrier for data circulation, preventing the leakage of sensitive information during collaboration. The distributed learning mechanism breaks down data barriers between medical institutions and enables cross-domain knowledge sharing and collaborative evolution through privacy protection technology. The elastic deployment architecture takes into account the differences in equipment conditions among grassroots medical institutions, enabling the universal access of high-quality diagnostic resources. The local decision-making capabilities of intelligent computing nodes effectively alleviate network dependence and ensure the continuity of diagnosis and treatment services in remote areas.

[0038] While 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 these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. An AI ophthalmology-assisted diagnosis interactive instrument, comprising two fully automatic fundus cameras (1), characterized in that: The upper and lower ends of the two fully automatic fundus cameras (1) are provided with transverse guide rails (2), and the upper and lower ends of the two fully automatic fundus cameras (1) are both installed with transverse guide blocks (13). The fully automatic fundus cameras (1) are movably connected to the upper and lower transverse guide rails (2) through the transverse guide blocks (13). The front faces of the two fully automatic fundus cameras (1) are provided with a linkage assembly (4), and the linkage assembly (4) is connected to the two fully automatic fundus cameras (1). A movable plate (5) is installed on the front face of the linkage assembly (4). A longitudinal guide rail (3) is fixedly installed in the middle of the bottom end of the transverse guide rail (2) below. A longitudinal guide block (7) is fixedly installed at the rear end of the movable plate (5). The movable plate (5) is movably connected to the longitudinal guide rail (3) through the longitudinal guide block (7). The movable plate (5) moves up and down relative to the longitudinal guide rail (3). When the movable plate (5) moves up and down, it drives the two fully automatic fundus cameras (1) to move left and right relative to the transverse guide rail (2) through the linkage assembly (4).

2. The AI ​​ophthalmology-assisted diagnosis interactive instrument according to claim 1, characterized in that: The linkage assembly (4) comprises a first fixing seat (401), the first fixing seat (401) being connected to the middle portion of the front face of the upper transverse guide block (13), and one end of the first fixing seat (401) away from the transverse guide block (13) being movably connected to a connecting rod (403) via a rotating shaft.

3. The AI ​​ophthalmology-assisted diagnosis interactive instrument according to claim 2, characterized in that: One end of the connecting rod (403) away from the first fixed seat (401) is movably connected to the second fixed seat (402) via a rotating shaft, and the bottom end of the second fixed seat (402) is connected to the top end of the movable plate (5).

4. The AI ​​ophthalmology-assisted diagnosis interactive instrument according to claim 3, characterized in that: A limit spring (8) is fixedly installed at the top end of the inner cavity of the longitudinal guide rail (3), and the bottom end of the limit spring (8) is connected to the top end of the longitudinal guide block (7). A threaded sleeve (6) is fixedly installed on the front side of the movable plate (5), and a screw rod (9) is connected to the internal thread of the threaded sleeve (6).

5. The AI ​​ophthalmology-assisted diagnosis interactive instrument according to claim 4, characterized in that: An extension rod (10) is movably mounted on the bottom end of the screw rod (9), a base support (11) is fixedly mounted on the bottom end of the extension rod (10), and a clamping plate (12) is fixedly mounted on the relatively distant sides of the two fully automatic fundus cameras (1). When the limit spring (8) is in the initial state, the distance between the base support (11) and the transverse guide rail (2) is the minimum value and the distance between the two fully automatic fundus cameras (1) is the maximum value.

6. The AI ​​ophthalmology-assisted diagnosis interactive instrument according to claim 1, characterized in that: The following diagnostic methods are also included: S1: Multimodal data normalization and enhancement framework; S2: Explainable Algorithms and Dynamic Decision Systems; S3: Clinical validation and continuous optimization mechanism; S4: Cross-platform deployment and privacy protection; The multimodal data standardization and enhancement framework includes a unified multi-source data acquisition protocol, a semi-automatic annotation tool chain, and a dynamic data enhancement strategy. The explainability algorithm and dynamic decision-making system include a knowledge graph-driven diagnostic logic tree, contrastive learning-enhanced feature decoupling, and a multimodal dynamic reasoning engine. The clinical verification and continuous optimization mechanism includes multi-center blind test verification and model iterative optimization protocol. The cross-platform deployment and privacy protection include a hybrid cloud deployment architecture and dynamic permission management.

7. The AI ​​ophthalmology-assisted diagnosis interactive instrument according to claim 1, characterized in that: The unified multi-source data acquisition protocol requires all equipment to be compatible with international medical imaging standards, with an image resolution of no less than 2048×2048 and a sampling rate of no less than 40 frames per second. Based on the standards of the International Society of Ophthalmology, a labeling knowledge base of 30 types of labels such as the 5-level classification of diabetic retinopathy and glaucoma staging is constructed. The labeling accuracy must reach the pixel level, and the error is controlled within 5 microns. The semi-automatic labeling tool chain includes a deep learning model with an encoder depth of 4 and an initial learning rate of 1e-4 for lesion pre-segmentation, which reduces manual correction time by 70%. Each batch of data must be cross-reviewed by 3 deputy chief physicians, and the labeling consistency Kappa value must be no less than 0.

85. The dynamic data enhancement strategy includes applying random rotation of ±30 degrees, scaling by 0.8 to 1.2 times, adding Gaussian noise σ=0.01, and adjusting brightness by ±15%. The data set size is expanded to 10 times the original, and cross-modal paired data is generated based on a generative adversarial network model.

8. The AI ​​ophthalmology-assisted diagnosis interactive instrument according to claim 1, characterized in that: The knowledge graph-driven diagnostic logic tree includes constructing a knowledge graph containing 5,000 clinical pathways. For example, the diagnosis of macular degeneration requires that the foveal thickness of the optical coherence tomography scan exceeds 300 microns and cystic edema is present. When outputting the diagnostic pathway diagram, the nodes are associated with disease features, the edge weights show confidence, and click-through tracing to the original image area is supported. The contrast learning enhanced feature decoupling includes using a contrast loss function with a temperature parameter τ=0.07 and a batch size of 512 to separate the normal and pathological feature spaces, and displaying key lesion areas through heat map overlays with a transparency of 0.6 and a threshold of 0.

3. The multimodal dynamic inference engine includes deploying a long-short-term memory network with 128 hidden layers and a time window of 3 consecutive examinations to analyze the progression of the disease. The initial diagnostic confidence threshold is set at 90%, and a multi-expert consultation process is automatically triggered when it is lower than the threshold.

9. The AI ​​ophthalmology-assisted diagnosis interactive instrument according to claim 1, characterized in that: The multi-center blind test verification includes covering 10 hospitals, with a sample size of no less than 50,000 cases, and using traditional manual diagnosis as the gold standard for comparison. The performance indicators require AUC to be no less than 0.98, F1-score to be no less than 0.93, and misdiagnosis rate to be no more than 5%. The model iterative optimization protocol includes incremental learning updates every month at a learning rate of 1e-5 and a new data ratio of 20%, establishing a misdiagnosis database with a capacity of no less than 1PB, automatically labeling error types and triggering retraining.

10. The AI ​​ophthalmology-assisted diagnosis interactive instrument according to claim 1, characterized in that: The hybrid cloud deployment architecture includes the use of NVIDIA Jetson AGX Xavier with a computing power of 32TOPS on the local side to achieve real-time inference within 3 seconds. The federated learning framework needs to be connected to more than 50 hospitals, and differential privacy noise ε=0.1 is applied to ensure data security. The dynamic permission management includes doctors' permissions to modify confidence thresholds, technicians' permissions to view heat maps only, and patients' permissions to preview reports with limited restrictions. The data transmission layer uses AES-256 encryption, and the storage end deploys the SGX trusted execution environment.

Images