A method for predicting orthokeratology lens parameters based on multi-modal chain tags

By integrating corneal morphology and clinical parameters through a multimodal chain tagging method, and combining static images and dynamic video assessments, the problem of missing high-dimensional features due to dimensional compression in orthokeratology lens parameter prediction was solved, achieving higher accuracy in parameter prediction and better adaptation efficiency.

CN122451965APending Publication Date: 2026-07-24THE EYE HOSPITAL OF WENZHOU MEDICAL UNIVERSITY
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
THE EYE HOSPITAL OF WENZHOU MEDICAL UNIVERSITY
Filing Date
2026-05-08
Publication Date
2026-07-24

AI Technical Summary

Technical Problem

Existing technologies for setting parameters in orthokeratology lenses suffer from problems such as high lens misalignment and fitting failure rates due to spatial dimension compression loss and lack of high-dimensional feature resolution.

Method used

A multimodal chain labeling approach is adopted, which obtains a parameter rule base through convolutional neural network training, combines corneal topography and structured clinical parameters for dual-channel fusion, uses a CatBoost regression model for chain processing, and combines static image and dynamic video evaluation to adjust lens parameters to improve prediction accuracy.

Benefits of technology

It improves the accuracy and reliability of orthokeratology lens parameter prediction, reduces trial fitting costs, ensures the comprehensiveness and objectivity of evaluation results, and avoids prediction bias caused by single data.

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Abstract

The application discloses a kind of corneal molding lens parameter prediction methods based on multimodal chain label, it is related to corneal molding lens parameter prediction field, the method includes the original data of corneal topography is reconstructed to obtain standardized corneal data;Standardized corneal data and structured clinical parameters are fused to obtain multimodal data in two channels;Multimodal data is processed in chain, and the prediction result of previous parameter is used as the prediction adjustment of current parameter, and the numerical value of each target parameter is sequentially predicted;The video data of try-on of slit lamp fluorescein sodium dyeing is extracted and compared, to determine the first fitting evaluation data and the second fitting evaluation data of lens;Unqualified first fitting evaluation data and / or second fitting evaluation data are matched with parameter rule base, to determine the target parameter to be adjusted and its adjustment value, and the parameter of corneal molding lens is adjusted.The application has the effect of improving the prediction accuracy of corneal molding lens parameter.
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