Performance of a Deep-Learning Neural Network Model in Assessing Skeletal Maturity on Pediatric Hand Radiographs
Performance of a Deep-Learning Neural Network Model in Assessing Skeletal Maturity on Pediatric Hand Radiographs
复制标题
DOI：
10.1148/radiol.2017170236
复制
发表时间：
2018-04-01
期刊：
影响因子：
19.7
通讯作者：
Langlotz, Curtis P.
中科院分区：
文献类型：
--
作者：
Larson, David B.;Chen, Matthew C.;Langlotz, Curtis P.
目的：比较基于手部X光片的深度学习骨龄评估模型与放射科专家和现有自动模型的性能。材料和方法：机构审查委员会批准了这项研究。从两家儿童医院获得了14036张临床手部X光照片和相应的报告来训练和验证模型。对于由200次检查组成的第一个测试集，使用临床报告和三名额外的人类审查员的骨龄估计值的平均值作为参考标准。通过比较模型估计值和参考标准骨龄之间的均方根（RMS）和平均绝对差（MAD），评估了总体模型性能。以成对方式计算所有评审员和模型的95%一致性限值。RMS的第二个测试集组成的913个考试从公开的数字手图谱进行了比较与已发表的报告，现有的自动model.Results：骨龄估计的模型和审查员之间的平均差异为0年，平均RMS和MAD分别为0.63和0.50年。模型、临床报告和三位评审员的估计值在95%的一致性限度内。Digital Hand Atlas数据集的RMS为0.73年，而之前报道的模型为0.61年。结论：深度学习卷积神经网络模型可以估计骨骼成熟度，其准确性与放射科专家和现有自动模型相似。
Purpose: To compare the performance of a deep-learning bone age assessment model based on hand radiographs with that of expert radiologists and that of existing automated models.Materials and Methods: The institutional review board approved the study. A total of 14 036 clinical hand radiographs and corresponding reports were obtained from two children's hospitals to train and validate the model. For the first test set, composed of 200 examinations, the mean of bone age estimates from the clinical report and three additional human reviewers was used as the reference standard. Overall model performance was assessed by comparing the root mean square (RMS) and mean absolute difference (MAD) between the model estimates and the reference standard bone ages. Ninety-five percent limits of agreement were calculated in a pairwise fashion for all reviewers and the model. The RMS of a second test set composed of 913 examinations from the publicly available Digital Hand Atlas was compared with published reports of an existing automated model.Results: The mean difference between bone age estimates of the model and of the reviewers was 0 years, with a mean RMS and MAD of 0.63 and 0.50 years, respectively. The estimates of the model, the clinical report, and the three reviewers were within the 95% limits of agreement. RMS for the Digital Hand Atlas data set was 0.73 years, compared with 0.61 years of a previously reported model.Conclusion: A deep-learning convolutional neural network model can estimate skeletal maturity with accuracy similar to that of an expert radiologist and to that of existing automated models.