Artificial intelligence diagnosis and classification system for lumbar disc herniation

A technology of lumbar intervertebral disc herniation and artificial intelligence, which is applied in the fields of diagnosis, diagnostic recording/measurement, medical science, etc., can solve problems such as low efficiency, uneven medical staff, and heavy burden on doctors, so as to reduce the difference in diagnosis and treatment level, The effect of high accuracy of interpretation and improvement of diagnosis and treatment efficiency

Active Publication Date: 2019-11-15
冯世庆 +1
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0004] 1. There are large unavoidable errors, and due to the uneven professional level of medical staff and the level of diagnosis and treatment in different regions, the same image data will give different results;
[0005] 2. The classification of lumbar disc herniation is complex and difficult to interpret in a short period of time, resulting in a heavy burden on doctors and low efficiency

Method used

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  • Artificial intelligence diagnosis and classification system for lumbar disc herniation
  • Artificial intelligence diagnosis and classification system for lumbar disc herniation
  • Artificial intelligence diagnosis and classification system for lumbar disc herniation

Examples

Experimental program
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Effect test

Embodiment 1

[0046] In the present embodiment, a plurality of sagittal lumbar images (such as Figure 1a shown), in preparation for lumbar lesion labeling.

[0047] Preferably, the marking module includes an interdisc image acquisition module and a lesion marking module.

[0048] The disc image acquisition module identifies multiple discs in each of the multiple lumbar images and captures multiple disc images centering on each disc. Such as Figure 1b and 1cAs shown, due to the large inclination angle and special shape of the sacrum, the sacrum is listed as a first-class bone, and other bones with similar shapes from the sacrum are listed as a first-class bone. In each of the images, the sacrum starts from the sacrum and identifies multiple lumbar vertebrae bones in turn, which are marked as bone S, bone L5, bone L4, bone L3, bone L2, bone L1, and bone T12; further identify the adjacent lumbar bones. Discs, namely, disc L5-S, disc L4-L5, disc L3-L4, disc L2-L3, disc L1-L2, disc T12-L1. ...

Embodiment 2

[0054] Pfirrmann lumbar disc herniation MRI grading is an intuitive grade of degeneration, which is often used as a standard to measure the severity of intervertebral disc herniation and as a basis for judging different treatment options. Combine below Figure 1a , 2a and 2b illustrate in detail the implementation of Pfirrmann classification on lumbar images using the lumbar intervertebral disc diagnosis and classification system proposed by the present invention.

[0055] In the present embodiment, a plurality of sagittal lumbar images (such as Figure 1a shown), in preparation for the Pfirrmann classification.

[0056] Preferably, the marking module includes an interdisc image acquisition module and a lesion marking module.

[0057] The disc image acquisition module identifies multiple discs in each of the multiple lumbar images and captures multiple disc images centering on each disc. Such as Figure 2a As shown, due to the large inclination angle and special shape of th...

Embodiment 3

[0064] MSU classification is a method to classify the degree of lesions shown in the cross-sectional images of the lumbar spine, which can guide medical staff to choose a treatment plan and make patient selection objective. Combine below Figure 3a with 3b The specific application of the artificial intelligence lumbar disc herniation diagnosis and classification system proposed in the present invention in MSU classification will be further described.

[0065] The data acquisition module receives a plurality of lumbar spine cross-sectional images (for example, Figure 3a The cross-sectional images of the lumbar spine shown in ) were used as the training basis for the neural network classifier.

[0066] In this embodiment, the marking module includes a bone window image acquisition module and a lesion marking module.

[0067] Since MSU classification is mainly based on the position of intervertebral disc herniation, it is necessary to identify the key bone window to remove th...

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Abstract

The invention proposes an artificial intelligence diagnosis and classification system for lumbar disc herniation. The system includes a data collection module, a marking module, a neural network classifier and a judgment module, wherein the data collection module receives several lumbar images, the marking module is connected with the data collection module to generate a lesion marker for each lumbar image and associate the lesion markers with the corresponding lumbar images for storage, the neural network classifier is trained by using each lumbar image and the corresponding lesion marker, and the judgment module receives a to-be-judged lumbar image and uses the neural network classifier to distinguish the lesion type of the to-be-judged lumbar image. The disclosed diagnosis and classification system can automatically judge whether or not the lumbar intervertebral disc is protruding and the degree of protruding, and meanwhile perform Pfirrmann classification and MSU classification, avaluable reference basis is provided for selection of a disease treatment scheme, and a basis is provided for taking prevention measures to prevent or slow down the occurrence of diseases.

Description

technical field [0001] The invention relates to an artificial intelligence lumbar intervertebral disc herniation diagnosis and classification system. Background technique [0002] Lumbar disc herniation is mostly caused by degeneration of lumbar intervertebral disc. It is a common and frequently-occurring disease in orthopedics, and its incidence rate is increasing year by year. The auxiliary examination for lumbar disc herniation is mainly imaging examination, and imaging reports are currently interpreted manually, and artificial intelligence has not yet realized the interpretation of image data in this field. [0003] At present, the auxiliary examination for lumbar disc herniation is mainly imaging examination, and imaging reports are currently interpreted manually, but manual interpretation will inevitably have the following problems: [0004] 1. There are large unavoidable errors, and due to the uneven professional level of medical staff and the level of diagnosis and ...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): A61B5/00A61B5/055
CPCA61B5/055A61B5/7267
Inventor 冯世庆张逸凌张蒂刘星宇段会全安奕成石家晓潘大宇张云东朱世博吴宇
Owner 冯世庆
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