A knee osteoarthritis typing model training method and device and a typing method

By constructing a regression model based on age and gender, and combining minimum spanning tree and multi-matrix constraint optimization, the discreteness and instability problems of the knee osteoarthritis classification method are solved, realizing continuous phenotypic classification and individualized treatment basis for knee osteoarthritis.

CN122158152APending Publication Date: 2026-06-05ZHUJIANG HOSPITAL OF SOUTHERN MEDICAL UNIVERSITY
View PDF 0 Cites 0 Cited by

Patent Information

Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
ZHUJIANG HOSPITAL OF SOUTHERN MEDICAL UNIVERSITY
Filing Date
2026-05-08
Publication Date
2026-06-05

AI Technical Summary

Technical Problem

Existing classification methods for knee osteoarthritis suffer from problems such as discrete classification distortion, lack of interpretability, parameter sensitivity, and unstable results, making it difficult to characterize the continuous evolution of disease phenotypes and provide a basis for individualized treatment.

Method used

A regression model with age and gender as independent variables was constructed, confounding factors were eliminated, and a soft-assignment probability matrix was generated through minimum spanning tree and multi-matrix constraint optimization. This model was then used to construct a knee osteoarthritis classification model, achieving accurate classification of continuous phenotypic features.

Benefits of technology

It improves the clinical interpretability and stability of the classification results, can objectively present the disease evolution pattern, and provide a reliable basis for individualized treatment.

✦ Generated by Eureka AI based on patent content.

Smart Images

  • Figure CN122158152A_ABST
    Figure CN122158152A_ABST
Patent Text Reader

Abstract

The application provides a knee osteoarthritis typing model training method and device and a typing method, and the method comprises the following steps: constructing a data set by taking information of a patient in a phenotype characteristic as a data sample; taking two-dimensional coordinates obtained by dimension reduction as initial clustering centers, and constructing a minimum spanning tree according to connection lines between the clustering centers; generating an adjacency matrix and a Laplacian matrix according to the connection relationship; calculating the probability of the data sample being distributed to the clustering center according to the distance between the two-dimensional coordinates and the clustering center, and generating a soft distribution probability matrix and a diagonal matrix; solving a pre-set target function based on the above matrices to obtain an updated matrix set; solving the target function based on the updated matrix set, and when the target function value converges, taking the minimum spanning tree topology as a knee osteoarthritis typing model. The application can make the knee osteoarthritis typing result meet the requirements of clinical interpretability, continuous phenotype characteristics and stable and reliable results at the same time.
Need to check novelty before this filing date? Find Prior Art