Construction and Validation of A Deep Learning-based Bone Age Prediction Model for Children Living in Both Plain and Highland Regions
ObjectiveTo construct and validate a deep learning-based bone age prediction model for children living in both plain and highland regions.MethodsA model named "ethnicity vision gender-bone age net (EVG-BANet)" was trained using three datasets, including the Radiology Society of North Ameri...
Saved in:
| Main Authors: | , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | zho |
| Published: |
Editorial Office of Medical Journal of Peking Union Medical College Hospital
2024-10-01
|
| Series: | Xiehe Yixue Zazhi |
| Subjects: | |
| Online Access: | https://xhyxzz.pumch.cn/article/doi/10.12290/xhyxzz.2023-0651 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850175194951319552 |
|---|---|
| author | LIU Qixing WANG Huogen CIDAN Wangjiu TUDAN Awang YANG Meijie PUQIONG Qiongda YANG Xiao PAN Hui WANG Fengdan |
| author_facet | LIU Qixing WANG Huogen CIDAN Wangjiu TUDAN Awang YANG Meijie PUQIONG Qiongda YANG Xiao PAN Hui WANG Fengdan |
| author_sort | LIU Qixing |
| collection | DOAJ |
| description | ObjectiveTo construct and validate a deep learning-based bone age prediction model for children living in both plain and highland regions.MethodsA model named "ethnicity vision gender-bone age net (EVG-BANet)" was trained using three datasets, including the Radiology Society of North America (RSNA) dataset [training set(n=12 611), validation set (n=1425), test set (n=200)], the Radiological Hand Pose Estimation (RHPE) dataset[training set (n=5491), validation set (n=713), test set (n=79)], and a self-established dataset[training set (n=825), test set (n=351)], and it was validated using an external test set. Self-established dataset retrospectively recruited 1176 left-hand DR images of children from Peking Union Medical College Hospital (n=745, all were Han) and Tibet Autonomous Region People's Hospital (n=431, 114 were Han, 317 were Tibetan). External test set included images from People's Hospital of Nagqu (n=256, all were Tibetan). Mean absolute difference (MAD) and accuracy within 1 year were used as indicators.ResultsEVG-BANet exhibited MAD of 0.34 and 0.52 years in RSNA and RHPE test sets, respectively. In the self-established test set, the model achieved MAD of 0.47 years (95% CI: 0.43-0.50) with accuracy within 1 year of 97.72% (95% CI: 95.56-99.01%). For the external test set, MAD was 0.53 years(95% CI: 0.48-0.58), with accuracy within 1 year of 89.45% (95% CI: 85.03-92.93).ConclusionEVG-BANet demonstrated high accuracy in bone age prediction, and therefore can be applied in children living in both plain and highland. |
| format | Article |
| id | doaj-art-db0f20c90c2e46a7b777f8ffa3931deb |
| institution | OA Journals |
| issn | 1674-9081 |
| language | zho |
| publishDate | 2024-10-01 |
| publisher | Editorial Office of Medical Journal of Peking Union Medical College Hospital |
| record_format | Article |
| series | Xiehe Yixue Zazhi |
| spelling | doaj-art-db0f20c90c2e46a7b777f8ffa3931deb2025-08-20T02:19:30ZzhoEditorial Office of Medical Journal of Peking Union Medical College HospitalXiehe Yixue Zazhi1674-90812024-10-011561439144610.12290/xhyxzz.2023-0651Construction and Validation of A Deep Learning-based Bone Age Prediction Model for Children Living in Both Plain and Highland RegionsLIU Qixing0WANG Huogen1CIDAN Wangjiu2TUDAN Awang3YANG Meijie4PUQIONG Qiongda5YANG Xiao6PAN Hui7WANG Fengdan8Department of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing 100730, ChinaCollege of Computer Science and Technology, Zhejiang University, Hangzhou 310000, ChinaDepartment of Radiology, Tibet Autonomous Region People's Hospital, Lhasa 850000, ChinaDepartment of Radiology, People's Hospital of Nyima County, Nagqu, Tibet 852600, ChinaDepartment of Radiology, People's Hospital of Nyima County, Nagqu, Tibet 852600, ChinaDepartment of Radiology, People's Hospital of Nagqu, Nagqu, Tibet 852000, ChinaDepartment of Ultrasound, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing 100730, ChinaDepartment of Endocrinology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing 100730, ChinaDepartment of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing 100730, ChinaObjectiveTo construct and validate a deep learning-based bone age prediction model for children living in both plain and highland regions.MethodsA model named "ethnicity vision gender-bone age net (EVG-BANet)" was trained using three datasets, including the Radiology Society of North America (RSNA) dataset [training set(n=12 611), validation set (n=1425), test set (n=200)], the Radiological Hand Pose Estimation (RHPE) dataset[training set (n=5491), validation set (n=713), test set (n=79)], and a self-established dataset[training set (n=825), test set (n=351)], and it was validated using an external test set. Self-established dataset retrospectively recruited 1176 left-hand DR images of children from Peking Union Medical College Hospital (n=745, all were Han) and Tibet Autonomous Region People's Hospital (n=431, 114 were Han, 317 were Tibetan). External test set included images from People's Hospital of Nagqu (n=256, all were Tibetan). Mean absolute difference (MAD) and accuracy within 1 year were used as indicators.ResultsEVG-BANet exhibited MAD of 0.34 and 0.52 years in RSNA and RHPE test sets, respectively. In the self-established test set, the model achieved MAD of 0.47 years (95% CI: 0.43-0.50) with accuracy within 1 year of 97.72% (95% CI: 95.56-99.01%). For the external test set, MAD was 0.53 years(95% CI: 0.48-0.58), with accuracy within 1 year of 89.45% (95% CI: 85.03-92.93).ConclusionEVG-BANet demonstrated high accuracy in bone age prediction, and therefore can be applied in children living in both plain and highland.https://xhyxzz.pumch.cn/article/doi/10.12290/xhyxzz.2023-0651bone agedeep learningartificial intelligenceplateautibetan |
| spellingShingle | LIU Qixing WANG Huogen CIDAN Wangjiu TUDAN Awang YANG Meijie PUQIONG Qiongda YANG Xiao PAN Hui WANG Fengdan Construction and Validation of A Deep Learning-based Bone Age Prediction Model for Children Living in Both Plain and Highland Regions Xiehe Yixue Zazhi bone age deep learning artificial intelligence plateau tibetan |
| title | Construction and Validation of A Deep Learning-based Bone Age Prediction Model for Children Living in Both Plain and Highland Regions |
| title_full | Construction and Validation of A Deep Learning-based Bone Age Prediction Model for Children Living in Both Plain and Highland Regions |
| title_fullStr | Construction and Validation of A Deep Learning-based Bone Age Prediction Model for Children Living in Both Plain and Highland Regions |
| title_full_unstemmed | Construction and Validation of A Deep Learning-based Bone Age Prediction Model for Children Living in Both Plain and Highland Regions |
| title_short | Construction and Validation of A Deep Learning-based Bone Age Prediction Model for Children Living in Both Plain and Highland Regions |
| title_sort | construction and validation of a deep learning based bone age prediction model for children living in both plain and highland regions |
| topic | bone age deep learning artificial intelligence plateau tibetan |
| url | https://xhyxzz.pumch.cn/article/doi/10.12290/xhyxzz.2023-0651 |
| work_keys_str_mv | AT liuqixing constructionandvalidationofadeeplearningbasedboneagepredictionmodelforchildrenlivinginbothplainandhighlandregions AT wanghuogen constructionandvalidationofadeeplearningbasedboneagepredictionmodelforchildrenlivinginbothplainandhighlandregions AT cidanwangjiu constructionandvalidationofadeeplearningbasedboneagepredictionmodelforchildrenlivinginbothplainandhighlandregions AT tudanawang constructionandvalidationofadeeplearningbasedboneagepredictionmodelforchildrenlivinginbothplainandhighlandregions AT yangmeijie constructionandvalidationofadeeplearningbasedboneagepredictionmodelforchildrenlivinginbothplainandhighlandregions AT puqiongqiongda constructionandvalidationofadeeplearningbasedboneagepredictionmodelforchildrenlivinginbothplainandhighlandregions AT yangxiao constructionandvalidationofadeeplearningbasedboneagepredictionmodelforchildrenlivinginbothplainandhighlandregions AT panhui constructionandvalidationofadeeplearningbasedboneagepredictionmodelforchildrenlivinginbothplainandhighlandregions AT wangfengdan constructionandvalidationofadeeplearningbasedboneagepredictionmodelforchildrenlivinginbothplainandhighlandregions |