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.
■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。