摘要
目的基于深度学习法构建适合平原和高原儿童的骨龄预测模型,并进行临床验证。方法本研究共纳入三个数据集[北美放射学会(Radiology Society of North America,RSNA)数据集,包括训练集12611例、验证集1425例、测试集200例;放射学手部姿势评估(Radiological Hand Pose Estimation,RHPE)数据集,包括训练集5491例、验证集713例和测试集79例;自建数据集,包括训练集825例和测试集351例],用于模型的训练和内部验证。自建数据集回顾性纳入北京协和医院(745例,均为汉族)和西藏自治区人民医院(431例,其中汉族114例、藏族317例)共1176例儿童的左手腕部X线影像。此外,研究还纳入了来自尼玛县人民医院的外部测试集(256例,均为藏族),用于模型的外部验证。应用深度学习法构建骨龄预测模型(ethnicity vision gender⁃bone age net,EVG⁃BANet),并采用平均绝对差异(mean absolute difference,MAD)和1岁以内准确率作为模型的评价指标。结果EVG⁃BANet模型在RSNA和RHPE测试集中的MAD分别为0.34岁和0.52岁。在自建数据集中,该模型的MAD为0.47(95%CI:0.43~0.50)岁,1岁以内准确率为97.72%(95%CI:95.56%~99.01%);在外部测试集中,该模型的MAD为0.53(95%CI:0.48~0.58)岁,1岁以内准确率为89.45%(95%CI:85.03%~92.93%)。结论EVG⁃BANet模型在平原和高原儿童中均表现出较高的准确性,具有一定的推广应用价值。
Objective To construct and validate a deep learning-based bone age prediction model for children living in both plain and highland regions.Methods A model named“ethnicity vision gender-bone age net(EVG-BANet)”was trained using three datasets,including the Radiology Society of North America(RSNA)dataset[training set(n=12611),validation set(n=1425),test set(n=200)],the Radiolog-ical Hand Pose Estimation(RHPE)dataset[training set(n=5491),validation set(n=713),test set(n=79)],and a self-established dataset[training set(n=825),test set(n=351)],and it was validated using an external test set.Self-established dataset retrospectively recruited 1176 left-hand DR images of children from Peking Union Medical College Hospital(n=745,all were Han)and Tibet Autonomous Region Peoples Hospital(n=431,114 were Han,317 were Tibetan).External test set included images from Peoples Hospital of Nagqu(n=256,all were Tibetan).Mean absolute difference(MAD)and accuracy within 1 year were used as indicators.Results EVG-BANet exhibited MAD of 034 and 052 years in RSNA and RHPE test sets,respectively.In the self-established test set,the model achieved MAD of 047 years(95%CI:043-050)with accuracy within 1 year of 9772%(95%CI:9556-9901%).For the external test set,MAD was 053 years(95%CI:048-058),with accuracy within 1 year of 8945%(95%CI:8503-9293).Conclusion EVG-BANet demonstrated high accuracy in bone age prediction,and therefore can be applied in children living in both plain and highland.
作者
刘琪星
汪火根
次旦旺久
土旦阿旺
杨美杰
普琼穷达
杨筱
潘慧
王凤丹
LIU Qixing;WANG Huogen;CIDAN Wangjiu;TUDAN Awang;YANG Meijie;PUQIONG Qiongda;YANG Xiao;PAN Hui;WANG Fengdan(Department of Radiology,Chinese Academy of Medical Sciences&Peking Union Medical College,Beijing 100730,China;College of Computer Science and Technology,Zhejiang University,Hangzhou 310000,China;Department of Radiology,Tibet Autonomous Region People's Hospital,Lhasa 850000,China;Department of Radiology,People's Hospital of Nyima County,Nagqu,Tibet 852600,China;Department of Radiology,People's Hospital of Nagqu,Nagqu,Tibet 852000,China;Department of Ultrasound,Peking Union Medical College Hospital,Chinese Academy of Medical Sciences&Peking Union Medical College,Beijing 100730,China;Department of Endocrinology,Peking Union Medical College Hospital,Chinese Academy of Medical Sciences&Peking Union Medical College,Beijing 100730,China)
出处
《协和医学杂志》
CSCD
北大核心
2024年第6期1439-1446,共8页
Medical Journal of Peking Union Medical College Hospital
基金
国家自然科学基金青年科学基金(82001900)
中央高水平医院临床科研专项(2022⁃PUMCH⁃A⁃003)
中国医学科学院医学与健康科技创新工程(2021⁃I2M⁃1⁃051)。
关键词
骨龄
深度学习
人工智能
高原
藏族
bone age
deep learning
artificial intelligence
plateau
Tibetan
