14 results about "Corneal topography" patented technology

A personalized prediction method for the curative effect of a corneal cross-linking operation comprises the steps that a high-resolution OCT image and a corneal topographic map of a patient are obtained, and a personalized three-dimensional eyeball model containing cornea and sclera parts is constructed; based on clinical data, establishing a biomechanical material model of the cornea, a keratoconus progress model and a cornea cross-linking mechanism model; constructing a finite element model of the eyeball through grid division and boundary condition determination; a corneal crosslinking operation is simulated by using a finite element method and an artificial intelligence technology, a real-time prediction system is constructed, and a postoperative curative effect prediction result is generated. According to the method, on the basis of real patient data, numerical simulation and artificial intelligence technologies are combined, personalized real-time prediction of the operation curative effect is achieved, preoperative patient science popularization education, doctor treatment decision selection and remote medical treatment are assisted, and the accuracy and the personalized level of operation planning are remarkably improved.

The invention provides a keratoconus image classification method based on an improved deep learning self-attention mechanism, and the method comprises the steps: obtaining a four-refraction report graph through a three-dimensional anterior segment analysis and diagnosis system, carrying out the image preprocessing of the four-refraction report graph through employing a method for extracting a region of interest, and obtaining a four-refraction report graph data set; constructing a network model based on a multi-directional axis attention mechanism, and inputting the four-refraction report graph data set into the constructed network model based on the multi-directional axis attention mechanism for training to obtain a trained network model based on the multi-directional axis attention mechanism; and carrying out image preprocessing and data enhancement on the four-refraction report graph to be processed, and inputting the four-refraction report graph to be processed into the trained network model based on the multi-directional axis attention mechanism to carry out cornea conus classification. According to the method, unique directional feature information in the corneal topographic map is efficiently captured, the information of the whole image can be comprehensively considered, and the diagnosis accuracy is improved.

The invention relates to a keratoconus diagnosis model construction method and system, and the method comprises the steps: collecting corneal topographic map data of two eyes, and determining corneal measurement features in the corneal topographic map data; preprocessing to obtain a sample set; dividing a training set and a test set; training the training set; respectively inputting the training set with the convergent loss function into a plurality of single models for training, and fusing the plurality of trained single models to obtain a fusion model; performing hyper-parameter optimization on the fusion model; retraining the fusion model; evaluating performance indexes of the fusion model; and determining a final diagnosis model, obtaining a definite diagnosis symptom, and outputting a diagnosis result. The method has great innovation in the aspects of feature extraction and fusion, and can be used for diagnosing the early screening of the keratoconus; the diagnosis method has high robustness and accuracy, is convenient to use, efficient and convenient, saves time and labor in the diagnosis process, can avoid the misdiagnosis rate, effectively improves the diagnosis and treatment efficiency, and has important clinical value and significance.

The invention discloses an orthokeratology parameter optimization method and system, a medium and a product, and relates to the technical field of data processing. By implementing the method, the system is optimized to obtain the corneal topographic maps of the patient before and after the standardized mechanical stress is applied, and point-to-point comparison is carried out, so that the corneal dynamic response deformation map for quantifying actual corneal deformation is obtained. The individualized corneal shaping response coefficient calculated based on the corneal dynamic response deformation map objectively reflects the sensitivity of the cornea of the patient to the mechanical stress. Different from related technologies which only depend on static data and empirical formulas, the method has the advantages that the orthokeratology lens parameter set is subjected to targeted correction through the individualized orthokeratology response coefficient, on one hand, the shaping effect deviation caused by the difference of individual cornea biomechanical characteristics (such as elasticity and viscosity) can be corrected, and on the other hand, the orthokeratology lens parameter set can be corrected; and on the other hand, a finally generated personalized orthokeratology lens parameter set can be more predicated and matched with an actual orthokeratology result.

Systems and methods for determining IRA candidacy and optimizing autologous corneal graft location and orientation to standardize and improve IRA procedures. A novel digital virtual model (i.e., a digital twin) of the eye is provided that integrates corneal imaging, pachymetry, topography and limbus / white-to-white measurements, and automates key surgical variables such as trephine size, autologous corneal graft rotations, and curvature and thickness matching, reducing cognitive load on the surgeon and improving accuracy. The embodiments herein provide precise graft placement parameters to maximize the amount of clear cornea in the visual axis. By providing real-time, patient specific guidance, the present embodiments enhance surgical precision and visual outcomes.

The invention belongs to the field of laser processing, and discloses a visual axis center automatic alignment system, device and method. The system comprises an imaging module used for obtaining a corneal topographic map; the adjusting module is used for S1, determining deflection angles of the visual axis around the x axis and the y axis according to the corneal topographic map; generating primary scanning track data according to the corneal topographic map and preset micro-lens parameters; performing three-dimensional rotation transformation on the primary scanning track data; s2, segmenting the three-dimensional point cloud data obtained in the S1 into slice point cloud data of different z-axis planes, performing fitting to obtain each layer of fitting elliptical trajectory, and judging light emitting point information on the fitting elliptical trajectory; and generating a plurality of concentric elliptical trajectories according to each layer of fitting elliptical trajectory and the light emitting point information, and forming a shape envelope of a preset micro lens. According to the system, one-time scanning track data is resampled and fitted, motion compensation of the eyeball off-axis is achieved, and therefore the precision and effect of automatic alignment of the visual axis center are improved.

A corneal topographic map reconstruction system relates to the technical field of corneal measurement and comprises a first scanning unit and a second scanning unit, the first scanning unit is composed of a laser, an MEMS micromirror and a photoelectric sensor, the laser is used for emitting a laser beam to the MEMS micromirror, the MEMS micromirror is used for reflecting the laser beam to a human cornea, and the photoelectric sensor is used for transmitting the laser beam to the second scanning unit. The second scanning unit is composed of a TOF sensor and a receiver, the TOF sensor is used for emitting a laser beam to the cornea of a person and positioning and measuring a scanning light beam emitted by the MEMS micromirror, micromirror scanning, TOF distance measurement and a computer are combined, during use, the micromirror is used for high-resolution scanning of a Lissajous track, and the laser beam emitted by the MEMS micromirror is positioned and measured. According to the corneal topography measuring device, the distance is accurately measured by means of the TOF sensor, corneal data of a patient can be effectively collected and transmitted to the computer in a non-contact collecting mode, and then the computer generates a detailed corneal topography map according to the data.

The invention provides a corneal curvature determination method and device for intraocular lens degree calculation of an eye after an LASIK operation and electronic equipment, and relates to the technical field of medical data processing, and the corneal curvature determination method comprises the following steps: obtaining a corneal topographic map; extracting data points of the axial curvature of the front surface of the cornea based on the corneal topographic map, and screening out all curvature data points in a first diameter circular area from the data points of the axial curvature of the front surface of the cornea; calculating a curvature average value of two intersection points of the flat axis and the edge of the target area as a flat axis diopter, and calculating a curvature average value of two intersection points of the steep axis and the edge of the target area as a steep axis diopter; based on the steep axis diopter and the flat axis diopter, the intraocular lens degree of the LASIK postoperative eye is calculated. According to the method, the axial curvature data of the front surface of the cornea is smoothed, a K value calculation method is improved based on delimitation of a target area and an effective optical area, and system errors of IOL calculation are reduced.

The embodiment of the present application provides a kind of based on deep learning's corneal local crosslinking irradiation position determination method and device, the method comprises: obtaining the corneal topographic image of target object;The corneal topographic image is carried out block segmentation, obtain multiple image block regions;Respectively, image block region is input to the preset image classification model, determines the corneal protruding region and non-protruding region of each image block region;Based on preset constraint condition, the image segmentation of corneal protruding region and non-protruding region is carried out, determines the irradiation position of target object's corneal local crosslinking.The embodiment of the present application carries out the division of corneal protruding region and non-protruding region to the corneal topographic image after block segmentation by image classification model, and by increasing preset constraint condition, the image segmentation of corneal protruding region and non-protruding region is realized, to more accurately determine the irradiation position of target object's corneal local crosslinking, guarantee the realization of corneal local crosslinking.

Embodiments of the present application provide a method, device and equipment for determining corneal cross-linking irradiation position, and a storage medium. The method comprises: obtaining a corneal topographic image of a target object, and determining a corneal microelement surface according to the corneal topographic image; forming a projection microelement of the corneal microelement surface on a preset reference plane, and determining a correlation between each projection microelement in the projection microelement surface; and determining a corneal local cross-linking irradiation position according to the determined correlation. The present application determines the corneal microelement surface by obtaining the corneal topographic image of the target object, and determines the corneal local cross-linking irradiation position of the target object by the correlation between each projection microelement in the projection of the corneal microelement surface on the preset reference plane, so as to ensure the realization of the corneal local cross-linking by using a more accurate corneal local cross-linking irradiation position.

Modular Hardware & Radius Measurement Modular attachment system for a portable handheld device for optometric diagnosis, characterized by an optical unit for measuring the radius of the cornea (topography), wherein the projection unit is optionally designed as a point array, as a Placido ring system or as a combination thereof.

The invention discloses a keratoconus element learning diagnosis method under an unbalanced small sample condition. The method comprises two parts of model training and auxiliary reasoning. According to the model training part, firstly, an acquired corneal topographic map is preprocessed and labeled, and then a meta-learning task ensuring complete categories is constructed by adopting a balance task sampler. And a pre-trained Swin Transform is used as a feature extractor to acquire the multi-scale depth features of the image. A plurality of prototypes are generated for early keratoconus categories with high diagnosis difficulty by using K-means clustering, and other categories are uniformly grouped. Classification is completed by calculating the distance between a query sample and a prototype and carrying out probability aggregation, and meta-learning optimization is carried out by adopting a joint loss function combining enhanced focus loss and contrast learning loss. And the auxiliary reasoning part is used for extracting input image features by using the trained model, comparing the input image features with the learned prototype, and outputting a diagnosis category and confidence. According to the method, the auxiliary diagnosis accuracy and generalization ability of the keratoconus are remarkably improved.

A personalized prediction method for the efficacy of corneal cross-linking surgery, including: obtaining high-resolution OCT images and corneal topography of patients, constructing a personalized three-dimensional eyeball model including cornea and sclera; establishing a biomechanical material model of the cornea, a keratoconus progression model, and a corneal cross-linking mechanism model based on clinical data; constructing a finite element model of the eyeball by meshing and boundary condition determination; simulating corneal cross-linking surgery using finite element method and artificial intelligence technology, constructing a real-time prediction system, and generating postoperative efficacy prediction results. The present invention is based on real patient data, combined with numerical simulation and artificial intelligence technology, to achieve personalized real-time prediction of surgical efficacy, assist preoperative patient science education, doctor treatment decision-making selection, and telemedicine, and significantly improve the accuracy and personalization of surgical planning.

The invention relates to the technical field of corneal topography map detection, in particular to a space scanning type corneal topography map detection method and equipment, and the method comprises the steps: shooting a reflection bright area image of a light transmitting device on a cornea with the radius RC through a camera device, and recording the Y-axis distance wd between the light-transmitting device and the cornea, the radius l1 of the bright area edge of the light-transmitting device and the distance m1 between the bright area edge of the light-transmitting device and the camera device when each reflection bright area image is shot, and completing reconstruction of the cornea surface topography based on the detection data. According to the scheme, the corneal topography can be accurately scanned and captured, so that the corneal topography map with high resolution and high precision is generated.