Development And Validation Of A Deep Learning Model Using Convolutional Neural Networks To Identify Femoral Internal Fixation Device In Radiographs

Objective:Accurate identification of the brand and model of internal fixation for hip fractures is critical for the second surgery.Failure to correctly recognize the implant design preoperatively can lead to referrals,surgical failure,and increased medical costs,but obtaining a record of the first surgery is not an easy task.The purpose of this study is to develop and validate a deep convolutional neural network(DCNN)model to automatically identify the manufacturer and model of hip internal fixation devices from anteroposterior(AP)radiographs.Materials and Methods:In this retrospective study,1721 hip AP radiographs,including six internal fixation devices from 1012 patients,were collected from orthopedic center between June 2014 and June 2022 to establish a classification network.The images were divided into training set(1106 images),validation(272 images)set and test set(343images).We randomly divide the dataset into a training set used to fit the parameters of the model,a validation set to provide an evaluation of the fitted model and to optimize the model’s hyperparameters,and a test set to provide an evaluation of the final model.The overall Top-1 accuracy,and the precision,sensitivity,specificity and F1 score of each model are calculated and receiver operating characteristic(ROC)curves are plotted to evaluate the model performance.Gradient-weighted class activation mapping(Grad-CAM)images are used to determine the image features that are most important for DCNN decisions.Results:1378(80%)images were used for model development,and model efficacy was validated on a test set with 343(20%)images.The overall TOP-1 accuracy was98.5%.The area under the receiver operating characteristic curve(AUC)values for each internal fixation model were 1.000,1.000,0.980,1.000,0.999 and 1.000,respectively.Gradient-weighted class activation mapping showed the unique design of the internal fixation device.Conclusion:We developed a deep convolutional neural network model that can identify the manufacturer and model of hip internal fixation devices from the hip AP radiographs.