Method for constructing lymphedema pathological network based on graph neural network

By constructing a lymphedema pathological network based on graph neural network, the problems of dense computing resources, scarce data and insufficient interpretation in the existing technology are solved, and efficient pathological identification and prediction in the environment of limited medical resources are achieved to adapt to the needs of different patient groups.

CN118919099BActive Publication Date: 2025-07-25AFFILIATED HOSPITAL OF JIANGSU UNIV
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Patent Information

Application Number
CN202410941261.7
Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
Filing Date
2024-07-15
Publication Date
2025-07-25
Estimated Expiration
2044-07-15

AI Technical Summary

Technical Problem

The existing lymphedema pathological network construction methods rely on complex graph neural network models, require a large amount of labeled data and intensive computing resources, making it difficult to apply in an environment with limited medical resources, and there are scarce data, privacy issues, insufficient model interpretation and bias, and it is impossible to effectively process imperfect clinical data.

Method used

By collecting medical images and clinical records of the lymphatic system, using image processing technology to extract key features and define topological structures, combining multimodal data fusion technology, designing multi-layer graph convolution networks and introducing attention mechanisms, conducting deep learning and graph embedding, and developing anomaly detection algorithms to identify pathological changes and predict lymphedema progression.

Benefits of technology

It improves the accuracy and early diagnosis ability of lymphedema pathological identification, adapts to different patient groups, reduces the demand for computing resources, enhances the interpretability and robustness of the model, can process complex and non-Euclidean data, and tracks disease changes.

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Abstract

The present invention relates to a method for constructing a lymphedema pathological network based on graph neural networks. First, collect medical imaging data, clinical records, and biomarker data of the lymphatic system of patients, and use image processing techniques to extract key features and structure them into nodes and edges of a graph; then, based on the anatomical structure and physiological functions of the lymphatic system, define the topological structure of the graph, and introduce multi-modal data fusion technology to combine imaging data and biomarker data; then, design a multi-layer graph convolutional network model for learning the patterns of lymph nodes and their connections, and introduce an attention mechanism; then, use the graph convolutional network for deep learning to extract hidden features of nodes and edges, and apply graph embedding technology to transform high-dimensional graph data into a low-dimensional space; finally, develop a graph-based anomaly detection algorithm for identifying pathological changes that are significantly different from common patterns, and combine time series analysis to predict the pathological progression of lymphedema.
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