Multi-feature fusion diagnosis system and method for L1-L4 lumbar vertebra segments

Through a multi-feature fusion diagnostic system, utilizing a cross-modal attention mechanism and trabecular bone network diagram, the problem of traditional methods being unable to accurately assess the bone status of the L1-L4 lumbar vertebrae segments is solved, achieving high sensitivity and high specificity in the diagnosis of early osteoporosis and providing refined fracture risk assessment.

CN120748692APending Publication Date: 2025-10-03NANJING WANGSHI INTELLIGENT TECHNOLOGY CO LTD
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Patent Information

Application Number
CN202511111657.X
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-08-08
Publication Date
2025-10-03

AI Technical Summary

Technical Problem

Existing technologies make it difficult to accurately diagnose osteoporosis early, especially to effectively assess the bone status and fracture risk of the L1-L4 lumbar vertebrae. Traditional methods ignore the heterogeneity of different regions within the vertebral body and the topological structure of the trabecular network.

Method used

A multi-feature fusion diagnostic system is used to learn imaging and clinical data through a cross-modal attention mechanism, construct a trabecular network graph, and combine imaging features and clinical information to generate refined diagnostic results, including vertebral level, sub-region level and patient level assessments.

Benefits of technology

It significantly improves the sensitivity and specificity of early diagnosis of osteoporosis, can identify local bone loss, provide accurate fracture risk assessment, and support robust diagnosis in a variety of situations with incomplete data.

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Abstract

The invention provides an L1-L4 lumbar vertebra segment-oriented multi-feature fusion diagnosis system and method, and the system comprises an image preprocessing module which is used for receiving a lumbar vertebra CT image sequence of a patient; a centrum anatomy partition module; the multi-dimensional image feature extraction module is used for extracting four types of quantitative features from each sub-region; the clinical multi-modal data coding module is used for independently acquiring and processing three types of clinical data: a multi-modal graph attention fusion network; and the segment-level diagnosis output module outputs diagnosis results of three levels. Through a parallel processing architecture and an optimized feature extraction algorithm, the whole diagnosis process only needs 45 seconds from data input to report generation, time is saved compared with manual film reading, and the consistency of diagnosis results is remarkably improved.
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