881 results about "Retina" patented technology

A computer-implemented method for operating an artificial intelligence voice agent system includes receiving voice input through communication channels; analyzing converted text through natural language processing (NLP) pipelines implementing intent recognition and sentiment analysis detecting emotional cues using a multimodal large language model (LLM); generating response content using machine learning models trained on domain-specific corpora; converting generated responses to synthetic speech through text-to-speech (TTS) engines; integrating with a customer relationship management (CRM) platforms or an enterprise resource planning (ERP) database; and implementing continuous learning by updating language understanding models using conversation logs, voice recognition parameters based on user feedback, and response generation patterns. One implementation is a computer-implemented system and method that operates a suite of intelligent interactive devices and platforms including an artificial intelligence voice agent, enhanced communication platforms, an intimacy companion system, and augmented / virtual reality eyeglasses. Further, one implementation includes AR / VR eyeglasses that project visual content onto interchangeable lenses or directly onto the user's retina via laser-based retinal projection, provide prescription adjustments, incorporate ear-mounted sensors for monitoring physiological parameters like heart rate, oxygen saturation, and blood pressure, and utilize wireless data transmission, onboard environmental sensing, and remote calibration, all designed to offer dynamically adaptive, secure, and context-aware interactions across communication, personal assistance, health monitoring, and immersive augmented or virtual reality environments.

The present application provides a method for diagnosing and / or evaluating the presence or absence, severity or degree of the improvement of a retinal disease in a subject, which comprises determining and / or evaluating circulatory parameters, retinal function, retina morphology and / or visual relating quality of life (QOL).

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 relates to the technical field of additive manufacturing, and discloses an additive manufacturing lens surface optimization method based on real-time feedback control. The method comprises the following steps: firstly, acquiring eye biometric data and personalized visual demand parameters of a wearer, establishing a personalized optical model of eyeballs according to the eye biometric data and the personalized visual demand parameters, and deducing a retina defocusing amount distribution map; and determining spatial arrangement and geometric morphology parameters of the distributed out-of-focus microstructure layer on the lens according to the atlas. In the additive manufacturing process, the optical material is deposited and solidified layer by layer, and the three-dimensional shape information of the surface of the optical material is obtained in real time after deposition of each layer. And comparing and analyzing the morphology information with a theoretical model to generate a morphology deviation result. And then, a correction instruction of the printing parameters is calculated by using the correction model, and the printing parameters of the additive manufacturing equipment are adjusted in real time, so that real-time compensation and optimization of the morphology deviation are realized, and finally, a high-precision lens product meeting personalized optical design requirements is manufactured.

The invention discloses an OCT (Optical Coherence Tomography) retina image denoising method based on a high-frequency enhanced diffusion model, which is characterized in that a double-branch diffusion model network constructed based on frequency perception Fourier transform attention (FFTA) is used for separating different frequency domains, enhancing specific frequency domain features and fusing the specific frequency domain features into a spatial domain feature map to realize stronger frequency domain perception and processing capability; the invention relates to a time step T coding method for a module of frequency domain attention. The time step T of the diffusion model is used as a parameter to be coded into a frequency domain attention module to participate in self-attention matrix calculation, so that frequency domain feature processing is aligned with the iteration step number of the diffusion model, and different frequency domain information is pointedly processed at different time steps T; the frequency selective hopping mechanism uses pooling operation to obtain a high / low frequency characteristic pattern, so that the network has adaptive frequency domain retention capability for different local parts.

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.

Various implementations disclosed herein include devices, systems, and methods that initiate an action based on a detected gaze direction oriented towards a target area (e.g., a hot corner / zone). For example, an example process may include producing a reflection by directing light towards an eye using an illuminator, receiving sensor data from a sensor, wherein a direction of sensing by the sensor (e.g., the optical axis of the camera) and a direction from the eye to a target area are approximately aligned, determining a reflective property (e.g., a spectral property) of the reflection based on the sensor data, detecting that a gaze direction of the eye is approximately oriented towards the target area (e.g., a hot zone / spot) based on the reflective property, and initiating an action based on detecting that the gaze direction is approximately oriented towards the target area.

The invention provides a method and a system for predicting an Alport syndrome through a multi-modal retina image based on deep learning, and relates to the technical field of deep learning. According to the method, a retina image, an OCT image and clinical text data are fused, a dual-channel deep neural network of an image encoder and a text encoder is constructed, semantic alignment pre-training is carried out by adopting multi-modal contrast learning, and an adversarial sample enhancement mechanism is introduced to improve model robustness; unified standardized training of different device images is achieved through the style migration module, the occurrence probability and the renal function progress risk level are finally output, and high-reliability prediction under the cross-modal and cross-device condition is achieved.

The invention relates to the technical field of visual health management, in particular to a ciliary muscle adjustment training system and method based on eye movement tracking, and the method comprises the steps: collecting eye movement data of a fixation point track, an adjustment amplitude change rate and pupil response time in real time, and combining personalized parameters such as the age of a user, diopter and a training target; inputting a ciliary muscle training dynamic planning algorithm module, dynamically generating a stage scheme containing a training action type, a single-group duration, a stimulation interval and a focal length adjustment strategy, and adaptively adjusting the strategy according to real-time adjustment stability and a historical ability curve; through multi-dimensional matching analysis of a pre-stored standard eye movement template, a training specification score is calculated from the track goodness of fit, speed uniformity, amplitude standard-reaching rate and binocular coordination, visual fatigue parameters are obtained based on continuous training duration, pupil fluctuation and retina reflex change, a scoring result is dynamically corrected, and a visual fatigue evaluation result is obtained. Personalized, self-adaptive and quantitative safety control of the training process is realized.

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 discloses an electric power meter intelligent reading system based on image recognition, and relates to the technical field of computer vision. The method is used for solving the problems of poor instrument reading stability and low range identification accuracy in a complex environment. The method comprises the following steps: firstly, performing multi-scale retina processing and four-direction gradient integral response analysis through an anti-shielding dial plate detection module, generating a probability heat map based on symmetry distribution, and outputting an accurate dial plate region segmentation mask through channel fusion; secondly, extracting a single-pixel wide pointer skeleton through non-maximum suppression and morphological refinement by using a meta-learning skeleton extraction module, and obtaining scale line distribution characteristics based on radial gradient analysis; thirdly, performing three-level progressive verification through a context range analysis module in combination with a geometrical relationship, a knowledge graph and a logical relationship, and outputting a range parameter; multi-dimensional information fusion is adopted to evaluate segmentation quality and skeleton integrity and verify consistency, reliable reading is generated, credibility evaluation is carried out, and high-precision stable reading in a complex environment is realized.

The invention discloses a multi-modal information fused high-myopia retina image classification method and system. The method comprises the following steps: acquiring an OCT image of a to-be-classified high-myopia retina and structured numerical data corresponding to the image; the OCT image is preprocessed, the preprocessed image is input into an image encoder for high-dimensional semantic representation learning, and overall semantic vector representation of the image is obtained; converting the structured numerical data into a medical language description text, and inputting the medical language description text into a text encoder for deep semantic modeling to obtain overall semantic vector representation of the text; inputting to a multi-modal fusion module, carrying out feature interaction and fusion through a bidirectional cross attention mechanism, and generating a fused multi-modal feature; and outputting a retina splitting stage category corresponding to the OCT image through a classification module. According to the method, the OCT image, the structured numerical data and the split staging definition text are utilized to perform multi-modal feature fusion, so that the accuracy of image staging recognition is improved.

A computer-implemented system and method for pupil-directed retinal projection in near-eye displays are disclosed. The computer-implemented system provides smart glasses with directed physical pixels that project light beams / signals directly onto user's retina based on pupil position and size. The glasses comprise frame, lenses with directed pixel layers, sensors for tracking pupil movement. Each directed pixel generates multiple virtual pixels by rapidly changing its emission angle. Each directed pixel with pupil's real-time tracking, provides automatic adjustments to virtual contents, ensuring accurate alignment of images with field of view of users while identifying vergence-accommodation conflict. The glasses function as prescription lenses, virtual reality displays, augmented reality devices without traditional optical systems. Additional features include depth sensors, cameras, connectivity to peripheral devices. The glasses enable seamless blend of virtual and real-world experiences, creating immersive “Mixverse” environment. Various input methods, including gesture recognition and brain-computer interfaces, allow for intuitive control and interaction.

The invention discloses a medical image analysis method and system based on a visual language model, and belongs to the technical field of medical image intelligent diagnosis, and the system comprises an image preprocessing unit which carries out the down-sampling of an original retina OCT image to 256 * 256 and carries out the normalization of the original retina OCT image; the feature encoding unit comprises an image encoder based on RET Found in combination with LoRA optimization and a text encoder based on BioClinicalBERT; the class balance comparison learning unit is used for adjusting loss through class balance coefficients so as to relieve the class imbalance problem; the uncertainty estimation unit is used for calculating confidence quality and uncertainty scores based on Dirichlet distribution, and determining a threshold value in combination with an improved Youden index; and the model training unit adopts a total loss function of class balance loss and uncertainty loss, outputs a diagnosis result and an uncertainty score through transfer learning, and further comprises an image input module, a result display module and a data storage module. Rare disease classification performance and reliability are improved, training efficiency is improved through LoRA optimization, and an accurate and reliable scheme is provided for detection of the rare retina diseases.

The invention discloses a prediabetic prediction method based on tongue image-retina image bimodal feature fusion. The method comprises the steps of constructing a tongue image and retina image pair data set and performing preprocessing; constructing a pre-diabetes prediction model, wherein the pre-diabetes prediction model comprises two multi-scale Mamba modules with the same structure, a bimodal feature fusion module and a classifier; the multi-scale Mama module extracts hierarchical multi-scale semantic features of the tongue image and the retina image of the same patient, the dual-mode feature fusion module is used for splicing and fusion, and the fused features are input into the classifier to output a prediction result of the patient in the early stage of diabetes; training the model through the training set, and monitoring convergence by using the test set to obtain a trained model; and inputting the verification set into the trained model to obtain a pre-diabetes prediction result. The problems that in the prior art, the single medical image feature prediction rate is not high, meanwhile, the convolutional neural network is difficult to capture the long-distance dependency relationship, and the calculation cost based on Transform is high are solved.

A system for hypertensive retinopathy (HTNR) detection includes a processor and a memory, including instructions stored thereon, which when executed by the processor, cause the system to: preprocess a retinal image using contrast enhancement, noise reduction and / or resolution normalization; segment a plurality of vessels from the preprocessed retinal image to generate a vessel segmentation map; detect a retinal marker, a vascular marker and / or an optic disc marker in the preprocessed retinal image using a first machine learning model; generate a severity score based on the detections; determine that the severity score exceeds a predefined threshold; and generate an output indicating a presence of HTNR based on the vessel segmentation map and the severity score using a second machine learning model.

The invention provides an implantable retina photon nerve repairing method and device, and relates to the field of implantable nerve regulation and control. The method comprises the following steps: acquiring a target image in a current visual field of a user based on external wearing equipment; adopting an edge detection algorithm to extract visual key features in the target image; based on the visual key features, time control pulse coding is adopted to generate a target stimulation pattern; performing neural feedback acquisition based on the target stimulation pattern, and acquiring neural electrical activity information through a feedback electrode preset on an implanted chip; based on the neural electrical activity information, the target stimulation pattern is dynamically adjusted through an adaptive optimization algorithm to perform optic nerve repair. The problem that a traditional implantable retina photon nerve repairing method is poor in retina photon nerve repairing effect is solved.

The application provides a synergic dynamic lighting method and its adjustable light direct current incandescent lamp, relates to the field of vision and lighting, and aims to ensure that light is uniform and dynamic in the field of vision; the color temperature is strictly imitated candlelight; the core is direct current adjustable light incandescent lamp technology; and the light in the field of vision is mostly non-uniform and static in the present night desktop lighting, and the light quality is never as good as direct current incandescent lamp; this easily leads to eye accommodation laxity and induces axial myopia; the application mainly comprises two direct current incandescent lamp platforms, one of which illuminates a book, and the other of which illuminates a white wall in front of the eyes; the light intensity of each is adjustable to ensure the uniformity of light in the field of vision, so that the fovea and peripheral rod cells of the retina are stimulated at the same time; the two lamps can also realize the synchronous adjustment of illumination according to the program, thereby guiding the dynamic focusing of the eyes and realizing dynamic lighting; the dynamic also reflects the low color temperature mammal lighting imitating candlelight according to the biological clock and the anti-alzheimer's lighting with 40Hz deep frequency flash.

The invention discloses a multi-point defocus management method for intelligent training glasses, and particularly relates to the technical field of defocus management, and the method comprises the following steps: S1, data collection; s2, scene recognition and intention judgment; s3, generating a dynamic defocusing scheme; s4, performing out-of-focus stimulation presentation; and S5, performing effect evaluation and adaptive adjustment. Original isolated eye movement tracking, scene recognition and adjustable optical technologies are organically connected in series through an intelligent decision-making core, an autonomous system capable of managing defocus like automatic driving is formed, the fundamental pain point that a traditional technology is static and rigid and cannot adapt to the dynamic requirement of a user is solved, and the user experience is improved. According to the invention, qualitative change of defocus stimulation from fixing to following is realized, it is ensured that the defocus stimulation can continuously and accurately act on the most critical area of the retina, meanwhile, the definition of the central view is ensured to the maximum extent, the contradiction between the defocus effect and the visual comfort is solved, and accurate intervention is realized.

A lens element intended to be worn in front of an eye of a wearer, the lens element comprising:-a holder, and-a plurality of optical elements superimposed on the holder, each optical element simultaneously provides a first optical function based on a prescription of the wearer for correcting refractive abnormalities of the eye and a second optical function that does not focus an image on the retina of the eye, characterized in that each optical element comprises a central region and a plurality of outer regions surrounding the central region, a maximum height of the central region is smaller than a maximum height of the outer region.

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 provides a large-view-field high-resolution compound eye camera array system based on bionic human eye vision. The system is composed of a curved surface support and an industrial camera array arranged on the curved surface support. Reverse extension lines of optical axes of all the cameras intersect at the same point to form a concave surface structure similar to the retina, and the fixed view field overlapping rate of the adjacent cameras is kept. The industrial camera array is used for collecting a target scene image, large-view-field information is obtained by fusing and splicing all camera images, and a high-resolution image is obtained by conducting super-resolution reconstruction on a plurality of related images in a view field center interested area. The system provides three working modes, namely a low-resolution mode, a conventional-resolution mode and a super-resolution mode, which can be switched by adjusting camera parameters. Finally, the system can perceive the dynamic change of the edge of the field of view in a high-frame-rate and low-resolution mode, magnify the details in the center of the field of view in a super-resolution mode, realize efficient non-uniform imaging, and optimize the information acquisition and processing efficiency and the structure compactness.

The invention discloses a structure perception mask reconstruction learning system and method for OCT image segmentation, and belongs to the technical field of image processing. According to the system, in a pre-training stage, an encoder fused with ViT-Base and a multi-expert self-adaptive reconstruction module is utilized to perform mask reconstruction on a label-free OCT image. In the pre-training stage, two-stage training is performed through a random mask strategy and an attention-guided dynamic mask strategy, so that an encoder-decoder can be transited from random learning to focusing on a structural key area, and optimization is performed by combining a structural perception loss function, thereby forcing a model to learn fine boundary and texture information of a retina. After training is completed, an encoder which learns rich structure knowledge serves as a backbone network to be migrated and is combined with a segmentation decoder of a downstream task, fine adjustment is carried out on a small amount of annotation data, and the accuracy of downstream segmentation is remarkably improved.

The invention relates to a 3D disordered microstructure-based lens for regulating and controlling point diffusion in a partitioned manner to inhibit ocular axis growth and a preparation method thereof, and belongs to the technical field of ocular optics and myopia prevention and control. The unordered dynamic micro-structure lens comprises a lens base body and an unordered dynamic micro-structure layer arranged on the lens base body, a point spread function is processed on the inner surface of the lens base body, the disordered dynamic microstructure layer comprises a plurality of microstructure units which are non-periodically arranged in space, and phase modulation and scattering are carried out on incident light so as to regulate and control the point spread function of imaging on the inner surface of the lens; the optical characteristics of the microstructure units can dynamically change in response to external stimulation, so that the form of the point spread function of the inner surface of the lens is dynamically adjusted, and a dynamic stimulation signal for inhibiting eye axis growth is provided for the retina. A disordered dynamic microstructure is introduced, a point spread function (PSF) of light entering the eye is intelligently regulated and controlled, and dynamically-changing, non-periodic and optimized optical signal distribution is formed around the retina, so that the growth of the axis of the eye is more effectively inhibited.

For see-through AR displays, the current state of art uses geometric correction to lower the display location error, however this is far from an ideal solution and requires knowing range or pose of the devices. A dual-function optic for eye display & camera is disclosed, wherein a sensor camera imaging optics pipeline with a virtual projection point at an eye's retina reduces parallax errors to zero.

The invention discloses a video jitter determination method and a video jitter processing method, relates to the field of video image processing, and is applied to application scenes of fundus retina injection surgery, and the application scenes comprise an IMU (inertial measurement unit) and a microscope. The video jitter determination method comprises the following steps: acquiring a first initial acceleration and a first initial angular velocity of a microscope and a second initial acceleration and a second initial angular velocity of the head of a patient in real time through an IMU (Inertial Measurement Unit); extracting a first jitter frequency of the microscope and a second jitter frequency of the head of the patient based on the first initial acceleration, the first initial angular velocity, the second initial acceleration and the second initial angular velocity; a dither source is determined based on a first dither frequency of the microscope and a second dither frequency of the patient's head. According to the video jitter determination method, the multi-source jitter source in the actual fundus retina injection operation is comprehensively analyzed, the limitation of a single determination mode is effectively overcome, and a guarantee is provided for subsequently realizing multi-source jitter processing with higher distinguishing precision.

The invention discloses a retina OCT (Optical Coherence Tomography) image denoising method based on zero sample learning. The method comprises the following steps: generating a noise independent image pair for a single noisy image by adopting a CDIS (Coherent Discrete Identifier) The method comprises the following steps of: carrying out de-noising by using an MLFSnet network (AF-MSDSConv, Aap-LSM, SA, a reconstruction module); using RMSE symmetry and consistency joint loss zero sample training; in the reasoning stage, an original image is directly input, and a same-resolution de-noising result is output. The speckle noise can be significantly suppressed and the layered structure of the retina can be retained without noise-free true values or pairwise data, so that the retina can be used while being shot.

The invention discloses a scattering lens and a design method thereof, which are applied to the technical field of optical devices and visual correction, and aims to solve the technical contradiction that the traditional myopia control lens is difficult to give consideration to the central visual definition and the effective intervention of peripheral retina. Unit density, size and scattering angle are configured in a differentiated manner, and optical artifacts are suppressed by adopting aperiodic arrangement; a pupil adaptive modulation layer and a task mode switching module are combined to realize dynamic response of illumination and vision tasks; in design, based on individual physiological parameter reverse optimization, a multi-target genetic algorithm is adopted for iterative calibration, the peripheral retina contrast regulation and control efficiency is remarkably improved by more than three times, meanwhile, it is guaranteed that the contrast loss of a central area is lower than 5%, and the high-precision, personalized and all-weather myopia control effect is achieved.

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.

A surgically implanted ocular optical array that can be used in both therapeutic and diagnostic applications is described. A device configured to be implanted in an eye includes: an imaging system that receives visible light incoming to the eye; optical source generating circuitry that generates an optical signal based on the light received by the imaging system; and an optical phased array (OPA) that generates and projects an image onto a retina of the eye in which the device is implanted, the image being based on the optical signal generated by the optical source generating circuitry.