Multi-source feature-based automatic assessment method for limb dysfunction of lumbago patient

A dysfunctional, automatic assessment technology, applied in the direction of medical science, diagnosis, diagnostic recording/measurement, etc., can solve the problem of low reliability

Pending Publication Date: 2022-05-27
XI AN JIAOTONG UNIV
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Problems solved by technology

[0009] In view of the defects of strong subjectivity and low reliability in the existing clinical assessment methods of limb dysfunction in patients with low back pai

Method used

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  • Multi-source feature-based automatic assessment method for limb dysfunction of lumbago patient
  • Multi-source feature-based automatic assessment method for limb dysfunction of lumbago patient
  • Multi-source feature-based automatic assessment method for limb dysfunction of lumbago patient

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Embodiment Construction

[0060] see figure 1 , the present embodiment provides an automatic assessment method for limb dysfunction in patients with low back pain, and the specific implementation steps are as follows:

[0061] Step 1: Use the U-Net neural network to automatically segment the multifidus and erector spinae in the lumbar MRI images of patients with low back pain, and use the threshold method to extract imaging features such as muscle cross-sectional area and fat infiltration;

[0062] The method of extracting imaging features using U-Net neural network and thresholding method is as follows:

[0063] ①Select three MRI axial images of the intervertebral disc of the L3-L4 spinal segment of the patient;

[0064] ② Improve the contrast of MRI images based on the contrast-limited adaptive histogram equalization method, and limit the noise amplification and enhance the local contrast by reducing the height of the local histogram;

[0065] ③ Build a U-Net network based on Adam optimizer with a ...

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Abstract

The invention discloses an automatic assessment method for limb dysfunction of a lumbago patient based on multi-source features, and the method comprises the steps: firstly, carrying out the automatic segmentation of multifidus muscle and erector spine muscle in a waist nuclear magnetic resonance (MRI) image through a U-Net neural network, and achieving the extraction of imaging features, such as the muscle cross-sectional area and the fat infiltration degree, through a maximum between-class variance method; secondly, screening clinical common lumbago patient evaluation scales, and extracting scale characteristics; then, screening all features by using a support vector machine recursive feature elimination algorithm (SVM-RFE), reducing feature redundancy, and determining an optimal feature combination; and finally, a support vector machine (SVM) method in machine learning is selected to construct a limb dysfunction assessment model of the lumbago patient, and automatic assessment and discrimination of dysfunction of the lumbago patient are realized. According to the method, the automatic assessment of the limb dysfunction degree of the lumbago patient is realized by combining a support vector machine method based on multi-source feature combination such as scale and iconography for the first time.

Description

technical field [0001] The invention belongs to the technical field of limb dysfunction assessment, and in particular relates to an automatic assessment method for limb dysfunction in patients with low back pain based on multi-source features. Background technique [0002] Low back pain is a common clinical disease with high incidence, which seriously affects the work and life of patients, and brings a heavy burden to patients and families. Accurate assessment of functional impairment is an important prerequisite for the determination of patient rehabilitation treatment and assistive device adaptation plans. [0003] At present, the assessment of limb dysfunction in patients with low back pain is mainly based on behavioral scales. The commonly used clinical scales include the Visual Analogue Scale (VAS), the Oswestry Disability Index (ODI), Japan Japanese Orthopaetic Association Scores (JOA), Roland-Morris Disability Questionnaire (RMDQ), Quebec Back Pain Disability Scale (...

Claims

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Application Information

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IPC IPC(8): A61B5/11A61B5/00
CPCA61B5/11A61B5/0033A61B5/7267A61B5/7246
Inventor 徐进张风镝冯翊航沈冲
Owner XI AN JIAOTONG UNIV
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