{Reference Type}: Journal Article
{Title}: SMANet: multi-region ensemble of convolutional neural network model for skeletal maturity assessment.
{Author}: Zhang Y;Zhu W;Li K;Yan D;Liu H;Bai J;Liu F;Cheng X;Wu T;
{Journal}: Quant Imaging Med Surg
{Volume}: 12
{Issue}: 7
{Year}: Jul 2022
{Factor}: 4.63
{DOI}: 10.21037/qims-21-1158
{Abstract}: <B>UNASSIGNED: </B>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.<BR><B>UNASSIGNED: </B>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.<BR><B>UNASSIGNED: </B>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.<BR><B>UNASSIGNED: </B>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.