Skeletal bone age assessments for young children based on regression convolutional neural networks
Pediatricians and pediatric endocrinologists utilize Bone Age Assessment (BAA) for in-vestigations pertaining to genetic disorders, hormonal complications and abnormalities in the skeletal system maturity of children. Conventional methods dating back to 1950 were often tedious and suscep-tible to in...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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AIMS Press
2019-07-01
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| Series: | Mathematical Biosciences and Engineering |
| Subjects: | |
| Online Access: | https://www.aimspress.com/article/10.3934/mbe.2019323?viewType=HTML |
| _version_ | 1811234559454347264 |
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| author | Pengyi Hao Sharon Chokuwa Xuhang Xie Fuli Wu Jian Wu Cong Bai |
| author_facet | Pengyi Hao Sharon Chokuwa Xuhang Xie Fuli Wu Jian Wu Cong Bai |
| author_sort | Pengyi Hao |
| collection | DOAJ |
| description | Pediatricians and pediatric endocrinologists utilize Bone Age Assessment (BAA) for in-vestigations pertaining to genetic disorders, hormonal complications and abnormalities in the skeletal system maturity of children. Conventional methods dating back to 1950 were often tedious and suscep-tible to inter-observer variability, and preceding attempts to improve these traditional techniques have inadequately addressed the human expert inter-observer variability so as to significantly refine bone age evaluations. In this paper, an automated and efficient approach with regression convolutional neu-ral network is proposed. This approach automatically exploits the carpal bones as the region of interest (ROI) and performs boundary extraction of carpal bones, then based on the regression convolutional neural network it evaluates the skeletal age from the left hand wrist radiograph of young children. Experiments show that the proposed method achieves an average discrepancy of 2.75 months between clinical and automatic bone age evaluations, and achieves 90.15% accuracy within 6 months from the ground truth for male. Further experimental results with test radiographs assigned an accuracy within 1 year achieved 99.43% accuracy. |
| first_indexed | 2024-04-12T11:39:38Z |
| format | Article |
| id | doaj.art-41b54085022148069a5404f14922efd3 |
| institution | Directory Open Access Journal |
| issn | 1551-0018 |
| language | English |
| last_indexed | 2024-04-12T11:39:38Z |
| publishDate | 2019-07-01 |
| publisher | AIMS Press |
| record_format | Article |
| series | Mathematical Biosciences and Engineering |
| spelling | doaj.art-41b54085022148069a5404f14922efd32022-12-22T03:34:43ZengAIMS PressMathematical Biosciences and Engineering1551-00182019-07-011666454646610.3934/mbe.2019323Skeletal bone age assessments for young children based on regression convolutional neural networksPengyi Hao 0Sharon Chokuwa1Xuhang Xie2Fuli Wu 3Jian Wu 4Cong Bai 51. College of Computer Science & Technology, Zhejiang University of Technology, Hangzhou, China1. College of Computer Science & Technology, Zhejiang University of Technology, Hangzhou, China1. College of Computer Science & Technology, Zhejiang University of Technology, Hangzhou, China1. College of Computer Science & Technology, Zhejiang University of Technology, Hangzhou, China3. Real Doctor AI Research Center, Zhejiang University, Hangzhou, China2. College of Computer Science and Technology, Zhejiang University, Hangzhou, China 3. Real Doctor AI Research Center, Zhejiang University, Hangzhou, China1. College of Computer Science & Technology, Zhejiang University of Technology, Hangzhou, ChinaPediatricians and pediatric endocrinologists utilize Bone Age Assessment (BAA) for in-vestigations pertaining to genetic disorders, hormonal complications and abnormalities in the skeletal system maturity of children. Conventional methods dating back to 1950 were often tedious and suscep-tible to inter-observer variability, and preceding attempts to improve these traditional techniques have inadequately addressed the human expert inter-observer variability so as to significantly refine bone age evaluations. In this paper, an automated and efficient approach with regression convolutional neu-ral network is proposed. This approach automatically exploits the carpal bones as the region of interest (ROI) and performs boundary extraction of carpal bones, then based on the regression convolutional neural network it evaluates the skeletal age from the left hand wrist radiograph of young children. Experiments show that the proposed method achieves an average discrepancy of 2.75 months between clinical and automatic bone age evaluations, and achieves 90.15% accuracy within 6 months from the ground truth for male. Further experimental results with test radiographs assigned an accuracy within 1 year achieved 99.43% accuracy.https://www.aimspress.com/article/10.3934/mbe.2019323?viewType=HTMLbone age assessmentcarpal bones extractionregression convolutional neural network |
| spellingShingle | Pengyi Hao Sharon Chokuwa Xuhang Xie Fuli Wu Jian Wu Cong Bai Skeletal bone age assessments for young children based on regression convolutional neural networks Mathematical Biosciences and Engineering bone age assessment carpal bones extraction regression convolutional neural network |
| title | Skeletal bone age assessments for young children based on regression convolutional neural networks |
| title_full | Skeletal bone age assessments for young children based on regression convolutional neural networks |
| title_fullStr | Skeletal bone age assessments for young children based on regression convolutional neural networks |
| title_full_unstemmed | Skeletal bone age assessments for young children based on regression convolutional neural networks |
| title_short | Skeletal bone age assessments for young children based on regression convolutional neural networks |
| title_sort | skeletal bone age assessments for young children based on regression convolutional neural networks |
| topic | bone age assessment carpal bones extraction regression convolutional neural network |
| url | https://www.aimspress.com/article/10.3934/mbe.2019323?viewType=HTML |
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