Pelvic fracture probability prediction method, system, medium, program product and terminal based on deep learning

By segmenting and analyzing pelvic CT images using a deep learning-based pelvic segmentation network and fracture probability prediction model, the problems of low computational efficiency and low accuracy of deep learning models in medical image analysis are solved, achieving high efficiency and accuracy in fracture detection and supporting automated diagnosis and treatment of pelvic fractures.

CN122243851APending Publication Date: 2026-06-19YINGWEI MEDICAL TECHNOLOGY (SUZHOU) CO LTD
View PDF -1 Cites -1 Cited by

Patent Information

Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
YINGWEI MEDICAL TECHNOLOGY (SUZHOU) CO LTD
Filing Date
2024-12-10
Publication Date
2026-06-19

AI Technical Summary

Technical Problem

In existing medical image analysis tasks, deep learning models suffer from low computational efficiency and low analytical accuracy when processing large-scale medical image data. Furthermore, they exhibit poor generalization ability and robustness, leading to performance degradation and inaccurate diagnosis across different hospitals, equipment, or patient groups.

Method used

A deep learning-based pelvic segmentation network was used to segment pelvic CT images. The Triplet-Attention-Unet algorithm was used for accurate segmentation. A pelvic fracture probability prediction model was constructed by combining a 3D Vnet network. Fracture probability was predicted by bone density analysis and bone quality analysis, and a fracture probability prediction distribution map was generated.

Benefits of technology

It has improved the accuracy and efficiency of fracture detection and analysis, reduced misdiagnosis and missed diagnosis, achieved automated processing, improved processing speed and prediction accuracy, and provided a scientific basis for the diagnosis and treatment of pelvic fractures.

✦ Generated by Eureka AI based on patent content.

Smart Images

  • Figure CN122243851A_ABST
    Figure CN122243851A_ABST
Patent Text Reader

Abstract

This application provides a method, system, medium, program product, and terminal for predicting the probability of pelvic fractures based on deep learning. This application uses a deep learning-based pelvic segmentation network to segment and locate targets in pelvic CT images, reducing the total amount of data processing in the model, thereby reducing computational resource consumption and improving processing speed. Simultaneously, segmentation eliminates other tissue structures, reducing interference factors in the analysis process and helping to reduce misdiagnosis and missed diagnosis, making fracture detection and analysis more direct and accurate, and improving the efficiency of subsequent processing and analysis. This invention uses a pre-constructed pelvic fracture probability prediction model for pelvic fracture probability analysis, which not only enables automatic processing, improving processing efficiency and speed, but also enhances prediction accuracy, providing a new possibility for the diagnosis and treatment of pelvic fractures and helping doctors make more accurate diagnostic analyses based on the prediction results.
Need to check novelty before this filing date? Find Prior Art