基于机器视觉的胸腰椎骨折智能分类方法的建立及测试研究  

Establishment and test of intelligent classification method of thoracolumbar fractures based on machine vision

作　　者：余志斌[1] 刘婧潇 杨毅[2] 张翔[2] 申艺玮 张珂瑞 王型金 马立泰[2] YU Zhibin;LIU Jingxiao;YANG Yi;ZHANG Xiang;SHEN Yiwei;ZHANG Kerui;WANG Xingjin;MA Litai(School of Electrical Engineering,Southwest Jiaotong University,Chengdu,Sichuan 611756,P.R.China;Department of Orthopedics,West China Hospital,Sichuan University,Chengdu,Sichuan 610041,P.R.China)

机构地区：[1]西南交通大学电气工程学院,成都611756 [2]四川大学华西医院骨科,成都610041

出　　处：《华西医学》2021年第10期1337-1343,共7页West China Medical Journal

基　　金：四川省科学技术厅重点研发计划项目(2020YFS0089);中央高校基本科研业务费资助项目(2682021ZTPY032)。

摘　　要：目的提出一种用于辅助诊断的胸腰椎骨折智能分类方法,并分析其临床应用的可行性。方法收集四川大学华西医院2019年1月-2020年3月共1256张胸腰椎骨折CT影像,通过影像LabelImg系统用统一的标准进行标注。所有CT图像按照AO Spine胸腰椎损伤分类。在ABC型的分类中,共使用1039张CT图像进行训练和验证来优化深度学习系统,其中训练集1004张,验证集35张;其余217张CT图像作为测试集,对比深度学习系统和临床医生诊断结果。在A型亚型的分类中,共使用581张CT图像进行训练和验证来优化深度学习系统,其中训练集556张,验证集25张;其余104张CT图像作为测试集,对比深度学习系统和临床医生诊断结果。结果深度学习系统骨折ABC分类的正确率为89.4%,Kappa系数为0.849(P<0.001);A型亚分型的正确率为87.5%,Kappa系数为0.817(P<0.001)。结论基于深度学习的胸腰椎骨折智能分类正确率高。这种方法可以用来辅助智能诊断胸腰椎骨折CT图像,改善目前人工复杂的诊断流程。Objective To develop a deep learning system for CT images to assist in the diagnosis of thoracolumbar fractures and analyze the feasibility of its clinical application.Methods Collected from West China Hospital of Sichuan University from January 2019 to March 2020,a total of 1256 CT images of thoracolumbar fractures were annotated with a unified standard through the Imaging LabelImg system.All CT images were classified according to the AO Spine thoracolumbar spine injury classification.The deep learning system in diagnosing ABC fracture types was optimized using 1039 CT images for training and validation,of which 1004 were used as the training set and 35 as the validation set;the rest 217 CT images were used as the test set to compare the deep learning system with the clinician’s diagnosis.The deep learning system in subtyping A was optimized using 581 CT images for training and validation,of which 556 were used as the training set and 25 as the validation set;the rest 104 CT images were used as the test set to compare the deep learning system with the clinician’s diagnosis.Results The accuracy and Kappa coefficient of the deep learning system in diagnosing ABC fracture types were 89.4%and 0.849(P<0.001),respectively.The accuracy and Kappa coefficient of subtyping A were 87.5%and 0.817(P<0.001),respectively.Conclusions The classification accuracy of the deep learning system for thoracolumbar fractures is high.This approach can be used to assist in the intelligent diagnosis of CT images of thoracolumbar fractures and improve the current manual and complex diagnostic process.

关 键 词：深度学习 胸腰椎骨折 卷积神经网络 骨折分类 

分 类 号：R687.3[医药卫生—骨科学]