7 results about "Eye anterior segment" patented technology

This invention discloses a method and device for anterior segment Sham elastography. This invention combines micron-level spatial resolution, high cost-effectiveness, and clinical operability. It can obtain quantitative elastic distribution of anterior segment tissues such as the cornea in vivo and in situ, providing reliable biomechanical basis for ophthalmic diagnosis and treatment scenarios such as early screening of keratoconus, refractive surgery planning, and glaucoma risk assessment. Compared with the prior art, the system structure of this invention is simplified, the cost is significantly reduced, and it has higher resolution and achieves truly non-contact measurement.

The application discloses a kind of early warning method and device of children's eye disease and systemic genetic syndrome, it is related to ophthalmic examination equipment technical field.The method includes: collection ocular anterior segment image data set, utilize the multi-task segmentation model after training, obtain multiple congenital eye disease quantitative feature data, then based on the risk index calculator of pre-established index calculation is carried out, and the congenital eye disease risk index is obtained, classification processing is carried out, matches eye abnormal phenotype, and obtains systemic disease early warning report.The application can realize more than 10 kinds of congenital eye disease and rare but important lesion in one examination screening, and simultaneously establishes the intelligent early warning system of eye phenotype and systemic genetic syndrome.

This invention relates to the field of medical image analysis technology, specifically disclosing a method and system for anterior segment image analysis. The method includes: acquiring anterior segment images and extracting features to obtain shared features; inputting the shared features into coarse-grained and fine-grained branches respectively to obtain output features of the coarse-grained and fine-grained branches; performing asymmetric feature fusion with gradient stopping, and then passing the results through corresponding classifiers to obtain anatomical classification prediction results of the coarse-grained branch and disease classification prediction results of the fine-grained branch. This invention jointly models coarse-grained anatomical features and fine-grained disease representations, which can significantly improve recognition accuracy and model generalization ability, realize automated analysis of ophthalmic images, and promote the widespread application of intelligent medical technology in clinical practice.

The application relates to the field of image processing and discloses an OCT conjugate mirror image removing method and device based on deep learning, which comprises the following steps: constructing a complex conjugate image of spectral data, constructing a true anterior eye segment image of the spectral data, performing image preprocessing on the complex conjugate image and the true anterior eye segment image; analyzing a pseudo-anterior eye segment image corresponding to the preprocessed conjugate image, calculating an absolute value loss between the pseudo-anterior eye segment image and the preprocessed anterior eye segment image; discriminating the falsity between the pseudo-anterior eye segment image and the preprocessed conjugate image, discriminating the authenticity between the preprocessed conjugate image and the preprocessed anterior eye segment image, calculating a generative network loss of a generative network; performing model training on a pre-trained neural network model to obtain a trained neural network model; performing model verification on the trained neural network model to obtain a verified neural network model, calculating a peak signal-to-noise ratio of the verified neural network model, and selecting a final neural network model. The application can reduce the difficulty of removing the OCT conjugate mirror image.

This application relates to the field of intelligent intraocular lens (IOL) selection, specifically disclosing an intelligent IOL selection system for cataract surgery based on ocular bioparameters. The system is not limited to the acquisition of traditional static geometric data such as axial length and corneal curvature, but further utilizes a deep convolutional neural network to extract texture features and perform dimensionality reduction mapping on anterior segment tomographic images, thereby resolving the capsular bag compliance feature vector characterizing the biomechanical properties of the ocular soft tissue. Based on this, the geometric parameters of the patient's eye, capsular bag compliance features, and the mechanical and physical properties of the IOL are combined. By introducing a physical constraint mechanism, the mechanical equilibrium state after lens implantation is simulated, thus accurately predicting the effective lens position. This effectively addresses the fitting challenges of non-standard eyeballs or special lens models, significantly improving the accuracy and stability of postoperative refractive prediction.

A method for determining the relationship between preoperative measurements of the anterior segment of a patient's eye and the postoperative position and optional tilt of the implanted intraocular lens (IOL) is described, based on quantitative optical coherence tomography (OCT) of the patient's eye and a machine learning architecture for determining the optimal regression model. Formulas obtained using this method are further described to determine estimated lens position (ELP) and optional tilt from preoperative measurements in the patient, to be incorporated into IOL refractive power calculation formulas or ray-tracing-based IOL refractive power selection.