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Scoliosis progress prediction method based on X-ray film and scoliosis progress prediction device based on X-ray film

A technology for scoliosis and prediction methods, which is applied in the field of deep learning and can solve problems such as low technical efficiency, large error in results, and limitations.

Pending Publication Date: 2021-12-10
NANJING UNIV +1
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0009] The evaluation of scoliosis progress based on artificial visual image reading is mechanical, time-consuming, and highly subjective. The methods and reference standards used by each family are different, which is limited by the experience and level of doctors, and the accuracy of the standard atlas and the universality of the population Doubts lead to low technical efficiency and large error in results. The present invention provides a method and device for predicting the progress of scoliosis based on X-ray films. The automatic and intelligent scoliosis progress prediction can replace manual rapid and accurate processing. , Analyzing medical images can make up for the problems of weak competitiveness of radiologists and shortage of physicians in the multidisciplinary era, and can also reduce the impact of X-ray radiation on children's physical functions

Method used

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  • Scoliosis progress prediction method based on X-ray film and scoliosis progress prediction device based on X-ray film
  • Scoliosis progress prediction method based on X-ray film and scoliosis progress prediction device based on X-ray film
  • Scoliosis progress prediction method based on X-ray film and scoliosis progress prediction device based on X-ray film

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Embodiment 1

[0057] Such as figure 1 As shown, the present invention provides a method for predicting scoliosis progress based on X-ray films, comprising the following steps:

[0058] Step S1, collect data

[0059] Two vertical X-ray films of the whole spine and anteroposterior X-ray films of the left hand were collected from the same patient half a year apart. The inclusion criteria were: (1) Anteroposterior X-ray films including the metacarpal bones, phalanges, carpal bones, and 3-4 cm distal ulna and radius backbones; (2) DICOM format images with correct shooting positions and projection points of the hands and no epiphyseal defects, (3) Age: 0-18 years old, (4) No hand or wrist structural incompleteness. 80% of the total data is used as a training set to establish a training deep learning model; 20% is used as a validation set to adjust hyperparameters, find the best parameters for the model, and confirm the effectiveness of its method.

[0060] Step S2, data preprocessing

[0061]...

Embodiment 2

[0128] Such as image 3 Shown, increase in embodiment 1:

[0129] Step S4, the scoliosis progression prediction regression optimization model that has been trained includes a feature extractor and a scoliosis progression prediction network.

[0130] Target detection algorithms such as the YOLO method are used to automatically calibrate and cut the region of interest (ROI) of 17 bones in each hand bone slice, and perform random rotation, random translation and cropping, and random center cropping on the image to achieve data enhanced;

[0131] After passing the picture through the feature extractor, the feature map is extracted, and then the attention map is obtained through the CAM (Class Activation Mapping) attention mechanism.

[0132] According to the thermal value, two areas with the highest thermal value are detected from the channel's thermal map as the most recognizable ROI areas, and cut.

[0133] In the above method, the cutting rule is:

[0134] Each bone is cut ...

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Abstract

The invention discloses a scoliosis progress prediction method and device based on an X-ray film. The method comprises the following steps: acquiring and preprocessing spine X-ray film data and a hand X-ray film of a patient within a preset time period; and according to the preprocessed spine X-ray film data and the hand X-ray film, constructing a deep learning model to evaluate the scoliosis degree and predict the scoliosis progress. By the adoption of the technical scheme, automatic intelligent scoliosis progress estimation can be achieved, manual rapid and accurate processing and analysis of medical images can be replaced, the problems that doctors in multidisciplinary cross times are weak in competitiveness and lack of doctors can be solved, and meanwhile the influence of X-ray film radiation on body functions of sick children can be reduced.

Description

technical field [0001] The invention belongs to the technical field of deep learning, and in particular relates to a scoliosis progression prediction method and device based on X-ray films. Background technique [0002] Scoliosis is a three-dimensional deformity of the spine that includes sequence abnormalities in the coronal, sagittal, and axial planes. Scoliosis is a common disease among teenagers and children, with an incidence rate of about 2%-3%. Scoliosis may affect the growth and development of children and cause trunk imbalance. In severe cases, it may affect cardiopulmonary function, and even involve the spinal cord, resulting in paralysis. Idiopathic scoliosis (IS) is one of the most common forms of scoliosis. It is clinically recommended to treat immature IS patients with a Cobb angle greater than 20° with braces conservatively. IS patients whose Cobb angle is between 10° and 20° or have matured should go to the hospital for follow-up and reexamination every 6 mo...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06T7/00G06T7/11G06K9/54G06K9/32G06K9/62G06N3/04G06N3/08
CPCG06T7/0016G06T7/11G06N3/04G06N3/08G06N3/084G06T2207/10116G06T2207/30012G06F18/253
Inventor 何克磊何中张峻峰秦晓东朱泽章许悦高阳
Owner NANJING UNIV
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