Bone age assessment from articular surface and epiphysis using deep neural networks

Yamei Deng; Yonglu Chen; Qian He; Xu Wang; Yong Liao; Jue Liu; Zhaoran Liu; Jianwei Huang; Ting Song; Yamei Deng; Yonglu Chen; Qian He; Xu Wang; Yong Liao; Jue Liu; Zhaoran Liu; Jianwei Huang; Ting Song

doi:10.3934/mbe.2023585

Mathematical Biosciences and Engineering

2023, Volume 20, Issue 7: 13133-13148. doi: 10.3934/mbe.2023585

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Research article

Bone age assessment from articular surface and epiphysis using deep neural networks

1.
Department of Radiology, Guangdong Provincial Key Laboratory of Major Obstetric Diseases, Guangdong Provincial Clinical Research Center for Obstetrics and Gynecology, The Third Affiliated Hospital of Guangzhou Medical University, Guangzhou 510150, China
2.
School of Automation, Guangdong University of Technology, Guangzhou 510006, China
3.
School of physics, electronics and electrical engineering, Xiangnan University, Chenzhou 423000, China

Academic Editor: Yang Kuang

Received: 28 February 2023 Revised: 18 May 2023 Accepted: 21 May 2023 Published: 06 June 2023

Bone age assessment is of great significance to genetic diagnosis and endocrine diseases. Traditional bone age diagnosis mainly relies on experienced radiologists to examine the regions of interest in hand radiography, but it is time-consuming and may even lead to a vast error between the diagnosis result and the reference. The existing computer-aided methods predict bone age based on general regions of interest but do not explore specific regions of interest in hand radiography. This paper aims to solve such problems by performing bone age prediction on the articular surface and epiphysis from hand radiography using deep convolutional neural networks. The articular surface and epiphysis datasets are established from the Radiological Society of North America (RSNA) pediatric bone age challenge, where the specific feature regions of the articular surface and epiphysis are manually segmented from hand radiography. Five convolutional neural networks, i.e., ResNet50, SENet, DenseNet-121, EfficientNet-b4, and CSPNet, are employed to improve the accuracy and efficiency of bone age diagnosis in clinical applications. Experiments show that the best-performing model can yield a mean absolute error (MAE) of 7.34 months on the proposed articular surface and epiphysis datasets, which is more accurate and fast than the radiologists. The project is available at https://github.com/YameiDeng/BAANet/, and the annotated dataset is also published at https://doi.org/10.5281/zenodo.7947923.
- bone age assessment,
- articular surface,
- epiphysis,
- hand radiography,
- deep neural networks
Citation: Yamei Deng, Yonglu Chen, Qian He, Xu Wang, Yong Liao, Jue Liu, Zhaoran Liu, Jianwei Huang, Ting Song. Bone age assessment from articular surface and epiphysis using deep neural networks[J]. Mathematical Biosciences and Engineering, 2023, 20(7): 13133-13148. doi: 10.3934/mbe.2023585

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Abstract

Bone age assessment is of great significance to genetic diagnosis and endocrine diseases. Traditional bone age diagnosis mainly relies on experienced radiologists to examine the regions of interest in hand radiography, but it is time-consuming and may even lead to a vast error between the diagnosis result and the reference. The existing computer-aided methods predict bone age based on general regions of interest but do not explore specific regions of interest in hand radiography. This paper aims to solve such problems by performing bone age prediction on the articular surface and epiphysis from hand radiography using deep convolutional neural networks. The articular surface and epiphysis datasets are established from the Radiological Society of North America (RSNA) pediatric bone age challenge, where the specific feature regions of the articular surface and epiphysis are manually segmented from hand radiography. Five convolutional neural networks, i.e., ResNet50, SENet, DenseNet-121, EfficientNet-b4, and CSPNet, are employed to improve the accuracy and efficiency of bone age diagnosis in clinical applications. Experiments show that the best-performing model can yield a mean absolute error (MAE) of 7.34 months on the proposed articular surface and epiphysis datasets, which is more accurate and fast than the radiologists. The project is available at https://github.com/YameiDeng/BAANet/, and the annotated dataset is also published at https://doi.org/10.5281/zenodo.7947923.

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