Deep learning risk assessment models for predicting progression of radiographic medial joint space loss over a 48-MONTH follow-up period
Deep learning risk assessment models for predicting progression of radiographic medial joint space loss over a 48-MONTH follow-up period
复制标题
DOI：
10.1016/j.joca.2020.01.010
复制
发表时间：
2020-04-01
影响因子：
7
通讯作者：
Kijowski, R.
中科院分区：
文献类型：
--
作者：
Guan, B.;Liu, F.;Kijowski, R.
目的：建立和评估深度学习(DL)风险评估模型，用于使用基线膝关节X线片预测放射学内侧关节间隙丢失的进展。方法：将骨关节炎计划中无和有进展的膝关节间隙丢失(定义为基线和48个月随访X线片内侧关节间隙宽度减少0.7 mm)随机分成训练(1400膝)和坚持测试(400膝)数据集。使用基线膝关节X线片对DL网络进行训练，以预测放射学关节间隙丢失的进展。人工神经网络被用来开发利用人口统计学和放射学危险因素预测进展的传统模型。利用DL网络从膝关节X线片中提取信息作为特征向量，并与危险因素数据向量相连接，建立了联合关节训练模型。结果：传统模型预测疾病进展的曲线下面积为0.660，敏感性为61.5%，特异性为64.0%。DL模型的AUC值为0.799(敏感度78.0%，特异度75.5%)，显著高于传统模型(P<；0.001)。联合模型的AUC值为0.863(敏感度和特异度为80.5%)，显著高于DL模型(P=0.015)和传统模型(P<；0.001)。结论：与使用人口学和放射学危险因素的传统模型相比，使用基线膝关节X线片的DL模型对预测关节间隙丢失的进展具有更高的诊断性能。(C)2020年国际骨性关节炎研究会。爱思唯尔有限公司出版。保留所有权利。
Objective: To develop and evaluate deep learning (DL) risk assessment models for predicting the progression of radiographic medial joint space loss using baseline knee X-rays.Methods: Knees from the Osteoarthritis Initiative without and with progression of radiographic joint space loss (defined as >= 0.7 mm decrease in medial joint space width measurement between baseline and 48-month follow-up X-rays) were randomly stratified into training (1400 knees) and hold-out testing (400 knees) datasets. A DL network was trained to predict the progression of radiographic joint space loss using the baseline knee X-rays. An artificial neural network was used to develop a traditional model for predicting progression utilizing demographic and radiographic risk factors. A combined joint training model was developed using a DL network to extract information from baseline knee X-rays as a feature vector, which was further concatenated with the risk factor data vector. Area under the curve (AUC) analysis was performed using the hold-out test dataset to evaluate model performance.Results: The traditional model had an AUC of 0.660 (61.5% sensitivity and 64.0% specificity) for predicting progression. The DL model had an AUC of 0.799 (78.0% sensitivity and 75.5% specificity), which was significantly higher (P < 0.001) than the traditional model. The combined model had an AUC of 0.863 (80.5% sensitivity and specificity), which was significantly higher than the DL (P = 0.015) and traditional (P < 0.001) models.Conclusion: DL models using baseline knee X-rays had higher diagnostic performance for predicting the progression of radiographic joint space loss than the traditional model using demographic and radiographic risk factors. (C) 2020 Osteoarthritis Research Society International. Published by Elsevier Ltd. All rights reserved.