基于DR的深度学习模型预测非血管化腓骨移植术的保髋疗效
A Deep Learning Model Based on Digital Radiography for the Prediction of the Efficacy of Non-Vascularized Fibular Grafting
陈浩 1薛鹏 1席洪钟 1何帅 1孙光权 1刘锌 1杜斌1
作者信息
- 1. 210029 南京中医药大学附属医院骨伤科
- 折叠
摘要
目的 探讨基于数字X线摄影(DR)的深度学习(DL)模型在预测非血管化腓骨移植术(NVFG)疗效中的价值.方法 纳入2009年6月至2021年6月接受NVFG进行保髋的339例(432髋)股骨头坏死(ONFH)患者,以3:1的比例划分为训练集(n=324)和测试集(n=108).每侧髋术前均拍摄标准正位、蛙位DR且分布于同一个数据集中.根据术后2年的随访结果定义保髋成败.以ResNet-50作为骨干网络构建DL模型,在训练集中训练、优化模型,并在测试集中采用受试者工作特征曲线(ROC)曲线下面积(AUC)、准确率(Acc)、精确率(Pre)、召回率(Rec)和F1-score进行模型的预测性能评估.结果 截至2023年6月,309髋随访结果优良,保髋成功率达71.52％.联合正位、蛙位DR图像构建的模型,对NVFG疗效的预测性能最佳(P＜0.05),其AUC为0.780,Acc为0.789,Pre 为 0.787,Rec 为 0.960,F1-score 为 0.865.基于正位 DR 的模型 AUC 为 0.660,Acc 为 0.662,Pre 为0.703、Rec 为 0.900、F1-score 为 0.790;基于蛙位 DR 的模型 AUC 为 0.710,Acc 为 0.761,Pre 为 0.762,Rec 为0.960,F1-score为0.850.结论 基于DR的DL模型能准确预测NVFG的保髋疗效,具有一定的临床应用价值.
Abstract
Objective To explore the value of deep learning(DL)models based on digital radiography(DR)in pre-dicting the efficacy of non-vascularized fibular grafting(NVFG).Methods A total of 339 patients(432 hips)with os-teonecrosis of the femoral head(ONFH)who underwent NVFG between June 2009 and June 2021 were included.They were divided into a training set(n=324)and a test set(n=108)in a ratio of 3:1.Standard anteroposterior and frog-lat-eral DR images were taken before surgery for both hips and distributed within the same dataset.The success or failure of preservation was defined based on the follow-up results at 2 years postoperatively.A DL model was constructed using Res-Net-50 as the backbone network.The model was trained and optimized in the training set,and its predictive performance was evaluated in the test set using metrics such as the area under the curve(AUC),accuracy(Acc),precision(Pre),re-call(Rec),and F1-score.Results As of June 2023,a total of 309 hips had excellent follow-up results,with a success rate of 71.52％for preservation.The model constructed using combined anteroposterior and frog-lateral DR images showed the best predictive performance for NVFG efficacy(P＜0.05),with an AUC of 0.780,Ace of 0.789,Pre of 0.787,Rec of 0.960,and F1-score of 0.865.The model based on anteroposterior DR had an AUC of 0.660,Acc of 0.662,Pre of 0.703,Rec of 0.900,and F1-score of 0.790.The model based on frog-lateral DR had an AUC of 0.710,Acc of 0.761,Pre of 0.762,Rec of 0.960,and F1-score of 0.850.Conclusion The DL model based on DR can accurately predict the efficacy of NVFG,which is worthy of application to clinical practice.
关键词
深度学习/股骨头坏死/非血管化腓骨移植/精准医学/数字X线摄影Key words
Deep learning/Osteonecrosis of the femoral head/Non-vascularized fibular grafting/Precision medicine/Digital radiography引用本文复制引用
基金项目
国家自然科学基金面上项目(82074471)
江苏省研究生科研与实践创新计划项目(SJCX24_0961)
出版年
2024
NETL
NSTL
万方数据