Cover Image

Cao, Shaomeng; Chen, Zhiye; Li, Congsheng; Lv, Chuanfeng; Wu, Tongning; Lv, Bin

doi:10.1002/ima.22382

Cited by 1 publication

(1 citation statement)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The other category is the automatic BAA algorithm that combines local and global features and exhibits better performance. Cao et al (16) (18) proposed an attention-guided approach to obtain ROIs from whole-hand images and then aggregated these ROIs for BAA.…”

Section: Related Workmentioning

confidence: 99%

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

Zhang

Zhu

et al. 2022

Quant Imaging Med Surg

Self Cite

View full text Add to dashboard Cite

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.

show abstract

Section: Related Workmentioning

confidence: 99%