SMANet: multi-region ensemble of convolutional neural network model for skeletal maturity assessment

Quantitative Imaging in Medicine and Surgery

article

Yi Zhang¹  Wenwen Zhu¹  Kai Li²  Dong Yan²  Hua Liu³  Jie Bai³  Fan Liu³  Xiaoguang Cheng²  Tongning Wu¹ 

[1] China Academy of Information and Communications Technology;Department of Radioligy , Beijing Jishuitan Hospital;Forensic Science Service of Beijing Public Security Bureau

关键词: Skeletal maturity;    bone age assessment (BAA);    deep learning;    Tanner-Whitehouse 3 (TW3);   

DOI  :  10.21037/qims-21-1158

学科分类：外科医学

来源: AME Publications

【 摘 要 】

Background: 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. Methods: 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. Results: 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. Conclusions: 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.

【 授权许可】

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