1470 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).

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 a user's retina based on pupil position and size. The glasses comprise a frame, lenses with directed pixel layers, and sensors for tracking pupil movement. Each directed pixel may generate multiple virtual pixels by rapidly changing its emission angle. The glasses function as prescription lenses, virtual reality displays, and augmented reality devices without traditional optical systems. Additional features include depth sensors, cameras, and connectivity to peripheral devices. The glasses enable a seamless blend of virtual and real-world experiences, creating an immersive “Mixverse” environment. Various input methods, including gesture recognition and brain-computer interfaces, allow for intuitive control and interaction.

There are provided a method for designing a spectacle lens and a related technique thereof, the method including: a modeling step of dividing a pattern into multiple models with a deviation from a normally worn state of the spectacle lens as decentering, in which a reference model, a decenter model, and a tilt model are prepared as the multiple model; a sensitivity calculation step of calculating a decenter sensitivity and a tilt sensitivity; and a design step of using a base curve value in the vicinity of a balance solution as a base curve of the spectacle lens, wherein a base curve value c [unit: diopter (D)] which is a curvature of a surface in a region H where the on-retinal non-convergence region is not provided on an object-side surface is taken as x-axis, and the decenter sensitivity and the tilt sensitivity [unit: diopter (D)] are taken as y-axis, and an intersection of a plot of the decenter sensitivity and a plot of the tilt sensitivity is taken as the balance solution.

The invention relates to the technical field of image processing, in particular to a super-resolution reconstruction system and method based on a pulse neural network, and the system comprises an image pulse encoder, a pulse feature enhancer and a differentiable pulse decoder. An image pulse encoder simulates a receptive field of a retina by using DoG response, performs adaptive pulse distribution on an input low-resolution image in combination with an image gradient, and converts the low-resolution image into a space-time pulse sequence reflecting a high-frequency region and a low-frequency region in the low-resolution image; a pulse feature intensifier performs coarse-grained and fine-grained structure reconstruction of a low-resolution image on the space-time pulse sequence by using a pulse time sequence dependent plasticity mechanism to obtain a granularity feature pulse; and the differentiable pulse decoder converts the granularity characteristic pulse to obtain a corresponding high-resolution image. According to the method, efficient and low-consumption image super-resolution reconstruction is realized through a bionic retina coding mechanism and a pulse time sequence optimization strategy.

The invention belongs to the field of image data processing, and relates to a diabetic retinopathy image classification system and method based on deep learning, the system is obtained by improving a VMama network model, and a VSS module of the VMama network model is replaced by a VSS-SL module; the VSS-SL module is of a multi-branch structure and comprises a VSS module, an SE attention module and a Local local attention module, one path of input data is input into the VSS module, the other path of input data is input into the Local local attention module and the SE attention module in sequence, the VSS module comprises a first branch path and a second branch path which are parallel, and the second branch path is connected with the first branch path. The features output by the second branch path after standardization of the LN normalization layer are fused with the features output by the first branch path and the features output by the SE attention module; the fused features are input into a linear layer, and the features output by the linear layer and the original input data are subjected to feature addition and then output. The system can classify the diabetic retinopathy pictures at a higher speed, and has a higher accuracy rate.

The invention belongs to the technical field of fundus imaging, and discloses an ultra-wide-angle ellipsoid confocal laser scanning fundus imaging system, which comprises a light splitting module, a light condensing module, a scanning module, a transmitting mirror group, an ellipsoid reflector and a detection module, wherein the center of the scanning module and the center of the transmitting mirror group are located on the same optical axis A, the pupil of the human eye is located at the first focus of the ellipsoidal reflector, and the entrance pupil of the transmitting mirror group is located at the second focus of the ellipsoidal reflector; and the transmitting lens group comprises a movable focusing lens and is used for changing the position of a first image surface of light reflected by the retina of the human eyes when moving along the optical axis A so as to realize clear imaging of the retina of the human eyes with different diopters. Under the condition that a free-form surface is not introduced, ultra-wide-angle multi-wavelength confocal laser scanning fundus imaging with the single intraocular field angle of 200 degrees is achieved, and the full-field optical resolution reaches 15 micrometers. And by moving the focusing lens, imaging compensation can be carried out on human eyes with diopters of-15D to + 15D.

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 provides a subjective optometry prediction method combining wavefront aberration and optical signals. According to the method, eyeball wavefront aberration data are measured through a wavefront sensor, cornea, crystal and retina biological parameters are obtained by combining multi-view-field scanning of a galvanometer system, and after high-credibility data points are screened, a gradient refractive index eyeball three-dimensional model is constructed by adopting an NURBS algorithm; using finite element analysis to optimize cornea stress distribution, iteratively correcting the model through a deformation energy function, and dynamically compensating eyeball motion errors based on eye movement tracking data; and generating a multi-field-angle visual chart image through ray tracing simulation, and predicting a subjective optometry result in combination with an image quality evaluation algorithm. According to the method, high-precision modeling, dynamic error suppression and intelligent simulation technologies are fused, the optometry prediction accuracy, the personalized adaptation degree and the dynamic scene adaptability are remarkably improved, and an efficient and reliable solution is provided for refraction correction.

The invention relates to the technical field of medical image processing and artificial intelligence, and discloses an artificial intelligence-based retinochoroid disease course structure change monitoring system, which comprises an image acquisition unit, a feature extraction unit, a lesion segmentation unit and the like. The image acquisition unit acquires retina optical coherence tomography sequence image data and fundus color image data; the feature extraction unit extracts a retina choroidal structure feature tensor through a three-dimensional convolutional neural network; the lesion segmentation unit outputs a lesion area probability distribution diagram by using a U-Net segmentation network; the time sequence alignment unit is used for registering the multi-time-point images to generate a displacement change matrix; the dynamic analysis unit extracts related indexes; an abnormal scoring unit constructs a disease course progress score; the decision grading unit outputs disease course stage classification labels; the multi-modal fusion unit fuses the multi-modal features; the report generation unit generates a structured disease course monitoring report. And favorable support is provided for diagnosis and treatment of ophthalmic diseases and illness state tracking.

The invention discloses a diabetes retina image classification method and system based on deep learning, and relates to the technical field of image classification, and the specific steps are as follows: collecting and preprocessing a retina image data set, and constructing a training data set; constructing an initial image classification model based on a deep neural network, and performing iterative training on the initial image classification model by using the training data set to obtain an image classification model; the initial image classification model takes a pre-trained OfficientNet network as a backbone network, and integrates a feature pyramid network structure, a lesion attention module and a classification module; and obtaining a to-be-classified image, preprocessing the to-be-classified image, inputting the preprocessed to-be-classified image into the image classification model, and outputting a classification result. According to the method, the dependence of the model on a specific data source is reduced by using a data enhancement strategy, the diagnostic performance and stability of the model on unseen retina images are improved, and the universality of clinical application is enhanced.

The present disclosure relates to diagnosis of ocular diseases by leveraging an attention-based multimodal graphical neural network (AM-GNN) based on multimodal data that includes one or more image-text pairs. Each image of the image-text pair may depict a retinal scan of a subject that may correspond to one or more imaging modalities. A multimodal dataset may be collected from different imaging modalities by data crawling to automatically collect relevant data for the targeted diseases from identified sources. In some aspects, a concept bank, comprising concepts or terminologies associated with the targeted diseases, may be provided to the AM-GNN to generate a multimodal graph. The AM-GNN may be configured to find an association between a set of nodes including image-text pairs, a set of diseases, and the concepts using an attention mechanism. Based on the multimodal graph, an output may be generated, including a predicted disease and related concept(s).

The invention relates to the technical field of optical elements, and discloses a fog display point diffusion optical system and a preparation method thereof, and the preparation method comprises the steps: obtaining user visual parameters, and building a visual behavior probability density model through kernel density estimation; designing the front surface of the lens based on the model, and generating an asymmetric defocus system coupled with a gazing habit; the rear surface of the lens is designed, and the distribution density of a fog display point diffusion unit is cooperatively modulated by the front surface defocusing amount, the fixation probability and the pupil diameter; and finally integrating front and rear surface design to form an optical lens body. According to the method, the asymmetric defocus field on the front surface and the fog display point diffusion field on the rear surface are subjected to collaborative design, and the defocus intensity and the contrast modulation intensity are accurately applied to the effective retina area of the user through the visual behavior probability density model; the technical problem that a traditional out-of-focus lens is fixed in signal and cannot adapt to individual staring habits and physiological parameter changes is solved.

The invention provides a diabetic retinopathy grading method based on a vision-language pre-training model and a ranking perception prompt strategy. According to the method, an eye ground color image and a corresponding DR grading label are converted into image features and text embedding through an image and text dual-coding mode, matching is carried out through the similarity between the image and the text, and accurate DR grading is achieved. In order to solve the problems of class imbalance and natural sequence modeling, a ranking perception prompt module and a similarity matrix smoothing module are introduced, ranking perception prompt ensures that the model can learn and maintain the sequence relation between different DR levels, and an SMS module effectively relieves the influence caused by data imbalance. The method disclosed by the invention can show relatively high accuracy and robustness in various DR grading tasks, especially under the condition of few samples or unbalanced categories. The method not only improves the grading accuracy, but also provides an effective auxiliary tool for clinical diagnosis, and has a wide application prospect.

Optical lens intended to be worn by a wearer An optical lens intended to be worn by a wearer comprising: —a refraction area having a refractive power based on a prescription for said eye of the wearer; —a plurality of optical elements having a transparent optical function of not focusing an image on the retina of the eye of the wearer when the optical lens is worn in standard wearing conditions, —a zone of interest comprising a subset of the optical elements, each optical elements in the subset bearing a cylinder component on its surface, wherein the zone of interest is of at least 50 mm2, and the standard deviation of the orientations of the cylinder axis of each optical element comprised in the zone of interest is smaller than or equal to 15° with respect to a common predefined direction.

A method for designing a spectacle lens and a related technique thereof, including: a modeling step of dividing a pattern into multiple models with a deviation from a normally worn state of the spectacle lens as decentering, wherein a reference model, decenter model, and tilt model are prepared as the multiple model; a sensitivity calculation step of calculating a decenter and a tilt sensitivity; and a design step of using a base curve value in the vicinity of a balance solution as a base curve of the spectacle lens, wherein a base curve value which is a curvature of a surface in a region where the on-retinal non-convergence region is not provided on an object-side surface is taken as x-axis, and the decenter and tilt sensitivities are taken as y-axis, and an intersection of a plot of the decenter sensitivity and the tilt sensitivity is taken as the balance solution.

The invention relates to the technical field of computer vision, and discloses a robot image recognition system based on a convolutional neural network, and the system comprises a dynamic retina blur sensing module which is used for estimating a pixel-level dynamic blur kernel parameter from an input blur image; the differentiable physical deblurring layer is used for performing feature reconstruction on the blurred image based on the dynamic blurring kernel parameters to generate deblurred image features; and the fuzzy robust CNN backbone network dynamically adjusts the voidage of a convolutional layer according to the dynamic fuzzy kernel parameters so as to adapt to the local fuzzy intensity. The problem of parameter distortion of a data-driven model is solved through an optical flow constraint fuzzy perception module, dilated convolution is dynamically adjusted based on fuzzy intensity to expand a high fuzzy region receptive field, an end-to-end joint training framework is constructed to realize classification error reverse optimization fuzzy kernel parameters, and a two-stage curriculum learning strategy is combined to improve the robustness of the system. The physical model is solidified first and then global optimization is carried out, and the bottleneck of traditional staged training is broken through.

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 belongs to the technical field of medical image analysis, and discloses a diabetic retinopathy grading model system using a frequency domain attention mechanism, which comprises a data set construction module, a data preprocessing module, an evaluation index module, a feature extraction module, a frequency domain channel attention module and a frequency domain space attention module. The invention provides a DR grading model based on deep learning, and aims to more accurately identify and classify lesions in fundus images. By introducing a frequency domain channel attention module (FCAM) and a frequency domain space attention module (FSAM), the model can more effectively capture weak lesion features. FCAM improves the sensitivity of a model to a lesion area by fusing frequency domain and channel domain information; according to the FSAM, key pixels are emphasized in a spatial domain and a frequency domain, so that the extraction capability of the model on fine texture features is enhanced.

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.

The invention discloses a retina image unsupervised anomaly detection method for early screening of diabetes mellitus. The method comprises the following steps: carrying out registration and multi-scale attention-guided blood vessel segmentation on a longitudinal time sequence retina image of a patient; extracting a vascular skeleton and constructing a time sequence vascular topological graph, calculating geometric morphology and hemodynamic attributes of each vascular segment, identifying vascular morphology evolution characteristics by comparing topological graphs of adjacent time points, and calculating hemodynamic characteristics such as wall shear stress through simulation; the evolution and hemodynamic characteristics are jointly input into a time sequence encoder for unsupervised learning, and an early diabetes risk score is comprehensively generated by analyzing a reconstruction error, an abnormal score based on density estimation and a time sequence trajectory deviation degree of a potential space; the scheme of the invention does not depend on lesion labels, and can sensitively detect the tiny anomalies at the early stage of pathology from multi-dimensional dynamic changes, thereby providing an objective and quantitative new way for early screening and intervention of diabetes.

A color checker device is disclosed. The color checker device includes a substrate configured to be inserted into a model eye and to conform to a surface curvature of the model eye when inserted therein. A color test target is located on the substrate. The color test target comprises a plurality of color sections configured for generating a color characterization profile of a fundus imaging camera configured to capture a color image of a human retina. A method of generating a color characterization profile using the color checker device and a fundus imaging system having the color characterization profile stored thereon are also disclosed.

A system and / or method uses a trained U-Net neural network to remove flow artifacts from optical coherence tomography (OCT) angiography (OCTA) data. The trained U-Net receives as input both OCT structural volume data and OCTA volume data, but expands the OCTA volume data to include depth information. The U-Net applies dynamic pooling along the depth direction and weighs more heavily the portion of the data that follows (e.g., along the contours of) select retinal layers. In this manner the U-Net applies contextually different computations at different axial locations based at least in part on the depth index information and / or (e.g., proximity to) the select retinal layers. The U-net outputs OCT volume data of reduced flow artifacts as compared with the input OCTA data.

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 embodiment of the invention discloses an image display method, an image display device, electronic equipment and a storage medium. The image display method comprises the following steps: acquiring a to-be-processed image; at least part of the area of the to-be-processed image is subjected to fuzzy processing, an out-of-focus image is obtained, and the out-of-focus image is used for being imaged on the retina of a user when the vision of the user is normal; and displaying the out-of-focus image. According to the embodiment of the invention, the image content seen on the retina of the user is restored through the blurred image when the myopia is out of focus, so that the myopia out-of-focus image is seen when the human eyes are normally focused, and the effect of protecting the eyesight through out-of-focus is further realized.

The application discloses a microstructure lens and glasses, comprising: a lens body having an optical center; the first microstructure unit is connected with the lens body, the first microstructure unit surrounds the optical center, and the geometric center of the first microstructure unit coincides with the optical center; the plurality of second microstructure units are connected with the lens body, and the plurality of second microstructure units are arranged around the first microstructure unit; the geometric center of any one of the plurality of second microstructure units does not coincide with the geometric center of the first microstructure unit, and the geometric centers of any two of the plurality of second microstructure units do not coincide. According to the micro-structure spectacle lens, the first micro-structure unit and the second micro-structure unit have the function of not focusing light on the retina, and the eccentric asymmetric layout can introduce stimulation signal micro-fluctuation of the micro-structure area on the surface of the lens, so that the time of a high-intensity stimulation effect can be kept; and the durability of the lens in intervening the refractive error development is improved.

The invention provides a glaucoma optic cup and optic disc segmentation method based on contour injection content. The method comprises the following steps: inputting a fundus retina color image into a coding module for coding; using a contour decoder to carry out visual cup and optic disk contour decoding on the multi-level features stage by stage; performing content segmentation decoding of the optic cup and the optic disk on the multi-level features by using a content decoder; a contour injection module is used for receiving the contour decoding features of the corresponding optic cup and optic disk in different stages to conduct contour injection on the content decoding features of the optic cup and optic disk in the first stage, and content decoding of a content decoder in the next stage is guided in an iteration mode to obtain optic cup and optic disk content segmentation mask graphs. According to the invention, the encoder and the decoder based on the large-size convolution kernel are different from a design normal form that a traditional convolution network takes a 3 * 3 window as a main part, the receptive field is effectively expanded, and a more global feature can be obtained by using a calculation amount smaller than global self-attention.

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.