PDF(Pubmed)
Abstract：
UNASSIGNED: Bone age assessment (BAA) is a crucial research topic in pediatric radiology. Interest in the development of automated methods for BAA is increasing. The current BAA algorithms based on deep learning have displayed the following deficiencies: (I) most methods involve end-to-end prediction, lacking integration with clinically interpretable methods; (II) BAA methods exhibit racial and geographical differences.
UNASSIGNED: A novel, automatic skeletal maturity assessment (SMA) method with clinically interpretable methods was proposed based on a multi-region ensemble of convolutional neural networks (CNNs). This method predicted skeletal maturity scores and thus assessed bone age by utilizing left-hand radiographs and key regional patches of clinical concern.
UNASSIGNED: Experiments included 4,861 left-hand radiographs from the database of Beijing Jishuitan Hospital and revealed that the mean absolute error (MAE) was 31.4±0.19 points (skeletal maturity scores) and 0.45±0.13 years (bone age) for the carpal bones-series and 29.9±0.21 points and 0.43±0.17 years, respectively, for the radius, ulna, and short (RUS) bones series based on the Tanner-Whitehouse 3 (TW3) method.
UNASSIGNED: The proposed automatic SMA method, which was without racial and geographical influence, is a novel, automatic method for assessing childhood bone development by utilizing skeletal maturity. Furthermore, it provides a comparable performance to endocrinologists, with greater stability and efficiency.
UNASSIGNED: A novel, automatic skeletal maturity assessment (SMA) method with clinically interpretable methods was proposed based on a multi-region ensemble of convolutional neural networks (CNNs). This method predicted skeletal maturity scores and thus assessed bone age by utilizing left-hand radiographs and key regional patches of clinical concern.
UNASSIGNED: Experiments included 4,861 left-hand radiographs from the database of Beijing Jishuitan Hospital and revealed that the mean absolute error (MAE) was 31.4±0.19 points (skeletal maturity scores) and 0.45±0.13 years (bone age) for the carpal bones-series and 29.9±0.21 points and 0.43±0.17 years, respectively, for the radius, ulna, and short (RUS) bones series based on the Tanner-Whitehouse 3 (TW3) method.
UNASSIGNED: The proposed automatic SMA method, which was without racial and geographical influence, is a novel, automatic method for assessing childhood bone development by utilizing skeletal maturity. Furthermore, it provides a comparable performance to endocrinologists, with greater stability and efficiency.
摘要：
■骨龄评估（BAA）是儿科放射学的重要研究课题。对开发BAA自动化方法的兴趣正在增加。当前基于深度学习的BAA算法存在以下不足:(I)大多数方法涉及端到端预测,缺乏与临床可解释方法的整合；（II）BAA方法表现出种族和地理差异。
■小说,基于卷积神经网络（CNN）的多区域集成，提出了具有临床可解释方法的自动骨骼成熟度评估（SMA）方法。该方法预测骨骼成熟度评分，从而通过利用左侧X射线照片和临床关注的关键区域斑块来评估骨龄。
■实验包括来自北京积水潭医院数据库的4,861张左手X射线照片，显示平均绝对误差（MAE）为31.4±0.19分（骨骼成熟度评分）和0.45±0.13岁（骨龄）腕骨系列和29.9±0.21分和0.43±0.17岁，分别,对于半径，尺骨,和基于Tanner-Whitehouse3（TW3）方法的短（RUS）骨骼系列。
■提出的自动SMA方法，没有种族和地理影响，是一部小说,利用骨骼成熟度自动评估儿童骨骼发育的方法。此外，它提供了与内分泌学家相当的表现，具有更大的稳定性和效率。
■小说,基于卷积神经网络（CNN）的多区域集成，提出了具有临床可解释方法的自动骨骼成熟度评估（SMA）方法。该方法预测骨骼成熟度评分，从而通过利用左侧X射线照片和临床关注的关键区域斑块来评估骨龄。
■实验包括来自北京积水潭医院数据库的4,861张左手X射线照片，显示平均绝对误差（MAE）为31.4±0.19分（骨骼成熟度评分）和0.45±0.13岁（骨龄）腕骨系列和29.9±0.21分和0.43±0.17岁，分别,对于半径，尺骨,和基于Tanner-Whitehouse3（TW3）方法的短（RUS）骨骼系列。
■提出的自动SMA方法，没有种族和地理影响，是一部小说,利用骨骼成熟度自动评估儿童骨骼发育的方法。此外，它提供了与内分泌学家相当的表现，具有更大的稳定性和效率。
