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A method for predicting prosthesis size in total knee arthroplasty

A technology of total knee replacement and prediction method, applied in the direction of neural learning method, biological neural network model, instrument, etc., to achieve the effect of low cost

Active Publication Date: 2022-06-28
PEKING UNIV +1
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0005] Aiming at the problem that the existing prosthesis selection relies too much on the doctor's personal experience, the purpose of the present invention is to provide a prosthesis model prediction system framework and a prosthesis model classification method based on deep learning

Method used

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  • A method for predicting prosthesis size in total knee arthroplasty
  • A method for predicting prosthesis size in total knee arthroplasty

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Experimental program
Comparison scheme
Effect test

Embodiment 1

[0035] The architecture of the prosthesis size prediction system for total knee arthroplasty is as follows: figure 1 shown:

[0036] 1. Data collection and screening.

[0037] Collect the basic data (gender, height, weight) and preoperative knee X-rays of patients undergoing total knee arthroplasty in a provincial tertiary hospital from 2014 to 2018. The total number of cases is about 300, which is not enough to directly use for training high A convolutional neural network model with strong accuracy and generalization, so the present invention is based on migration learning, firstly training on a similar large data set, and then migrating back to the original data set to fine-tune the model structure and parameters.

[0038] 1) Select the selected data according to the aforementioned criteria for subsequent processing.

[0039] 2) For the data that meets the exclusion criteria, it is not used for the incremental training of the subsequent model, but the model can be used to ...

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Abstract

The invention discloses a method for predicting the prosthesis model of total knee joint replacement, which comprises the following steps: collecting the X-ray films of the knee joints of patients meeting the inclusion criteria before operation and the basic information of the patients; and analyzing the collected X-ray films and the patients The basic information of the patient is preprocessed; the preprocessed picture and the basic information of the patient are used as features, and input into the prosthesis model classifier trained based on deep learning technology, and the probability of using each model of prosthesis in the operation of the patient is obtained. The present invention can efficiently and accurately predict the required prosthesis model, and the detection accuracy rate can reach more than 84%, which has reached the same level as experienced experts using CT and X-ray films for preoperative prediction, and The present invention only needs to use X-ray film and basic patient information, and the cost is lower.

Description

technical field [0001] The invention belongs to the field of artificial intelligence medical image processing, and in particular relates to the design of a prosthesis model prediction system for total knee arthroplasty, and a prosthesis model prediction method based on the system. Background technique [0002] Total Knee Arthroplasty (TKA) is an effective method for the treatment of severe knee osteoinflammation and other diseases. While achieving good clinical efficacy and reliable prosthesis survival rate, it also faces a series of postoperative complications. It affects the clinical efficacy of patients, and even requires revision surgery again, increasing the pain and medical burden of patients. According to various studies, patient satisfaction after total knee arthroplasty is only around 80%. [0003] Accurate prosthesis matching is considered to be one of the important factors to reduce postoperative complications such as knee pain, prosthesis loosening, prosthesis w...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G16H30/40G16H50/20G06V10/764G06V10/82G06K9/62G06N3/04G06N3/08
CPCG16H30/40G16H50/20G06N3/08G06N3/045G06F18/241
Inventor 岳宇王鑫光赵旻暐田华曹志崴高翘楚李斗
Owner PEKING UNIV