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Automatic grading method for ankylosing spondylitis based on multi-task deep learning

Ankylosing spondylitis, deep learning technology, applied in the field of image processing, can solve problems such as low degree of automation

Pending Publication Date: 2021-12-07
AIR FORCE MEDICAL UNIV
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Problems solved by technology

[0005] The embodiment of the present disclosure provides an automatic grading method for ankylosing spondylitis based on multi-task deep learning, which can solve the problem of low automation of hip joint bone gap measurement in the prior art

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  • Automatic grading method for ankylosing spondylitis based on multi-task deep learning
  • Automatic grading method for ankylosing spondylitis based on multi-task deep learning
  • Automatic grading method for ankylosing spondylitis based on multi-task deep learning

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Embodiment Construction

[0086] Reference will now be made in detail to the exemplary embodiments, examples of which are illustrated in the accompanying drawings. When the following description refers to the accompanying drawings, the same numerals in different drawings refer to the same or similar elements unless otherwise indicated. The implementations described in the following exemplary examples do not represent all implementations consistent with the present disclosure. Rather, they are merely examples of apparatuses and methods consistent with aspects of the present disclosure as recited in the appended claims.

[0087] The embodiment of the present disclosure provides an automatic grading method for ankylosing spondylitis based on multi-task deep learning. This method uses the X-ray image data of the hip joint, and proposes clinical features for grading ankylosing spondylitis based on multi-task deep learning. The automatic measurement method of hip joint gap distance, this method is different...

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Abstract

The invention provides an automatic grading method for ankylosing spondylitis based on multi-task deep learning, and relates to the technical field of image processing, and the method comprises the steps: obtaining a target sample data set; constructing a hip joint gap target recognition network and a hip joint gap segmentation network based on global attention according to the target sample data set; according to a hip joint gap target identification network and a hip joint gap segmentation network, carrying out target identification detection and segmentation on the plurality of preprocessed 2D hip orthotopic X-ray film images to obtain a hip joint gap segmentation result; performing edge extraction processing on the hip joint gap segmentation result to obtain a hip joint gap contour curve; performing distance measurement on the hip joint gap profile curve to obtain a measurement result; and performing disease grading on the measurement result according to a preset grading system. According to the invention, the automatic grading of the ankylosing spondylitis disease by the hip joint X-ray film based on multi-task deep learning is realized, and the automation degree and accuracy of hip joint gap measurement are improved.

Description

technical field [0001] The present disclosure relates to the technical field of image processing, and in particular to an automatic grading method for ankylosing spondylitis based on multi-task deep learning. Background technique [0002] Ankylosing spondylitis (AS) comprises a group of interrelated disorders characterized by inflammation of the sacroiliac joints and sites of the spine, peripheral joints, and tendon attachments, manifested primarily by narrowing of the bony spaces. The etiology of AS is complex, the pathogenesis is unclear, the early clinical manifestations of patients are not typical, and the lack of specific laboratory indicators has caused great obstacles for clinicians to accurately judge AS in the early stage. Failure to diagnose and treat in time will often cause serious complications. As a result, it brings irreversible bone destruction to the patient, and even leads to lifelong disability. Hip involvement is the most common manifestation of extraspi...

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Application Information

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IPC IPC(8): G06T7/00G06K9/34G06K9/46G06K9/62G06N3/04G16H50/20
CPCG06T7/0012G16H50/20G06T2207/10116G06T2207/30008G06N3/045G06F18/241
Inventor 韩青韩洁黄陆光朱平郑朝晖张葵丁进马晨超
Owner AIR FORCE MEDICAL UNIV