Automated Distal Radius and Ulna Skeletal Maturity Grading from Hand Radiographs with an Attention Multi-Task Learning Method
<b>Background:</b> Assessment of skeletal maturity is a common clinical practice to investigate adolescent growth and endocrine disorders. The distal radius and ulna (DRU) maturity classification is a practical and easy-to-use scheme that was designed for adolescent idiopathic scoliosis...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-11-01
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| Series: | Tomography |
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| Online Access: | https://www.mdpi.com/2379-139X/10/12/139 |
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| author | Xiaowei Liu Rulan Wang Wenting Jiang Zhaohua Lu Ningning Chen Hongfei Wang |
| author_facet | Xiaowei Liu Rulan Wang Wenting Jiang Zhaohua Lu Ningning Chen Hongfei Wang |
| author_sort | Xiaowei Liu |
| collection | DOAJ |
| description | <b>Background:</b> Assessment of skeletal maturity is a common clinical practice to investigate adolescent growth and endocrine disorders. The distal radius and ulna (DRU) maturity classification is a practical and easy-to-use scheme that was designed for adolescent idiopathic scoliosis clinical management and presents high sensitivity in predicting the growth peak and cessation among adolescents. However, time-consuming and error-prone manual assessment limits DRU in clinical application. <b>Methods</b>: In this study, we propose a multi-task learning framework with an attention mechanism for the joint segmentation and classification of the distal radius and ulna in hand X-ray images. The proposed framework consists of two sub-networks: an encoder–decoder structure with attention gates for segmentation and a slight convolutional network for classification. <b>Results:</b> With a transfer learning strategy, the proposed framework improved DRU segmentation and classification over the single task learning counterparts and previously reported methods, achieving an accuracy of 94.3% and 90.8% for radius and ulna maturity grading. <b>Findings:</b> Our automatic DRU assessment platform covers the whole process of growth acceleration and cessation during puberty. Upon incorporation into advanced scoliosis progression prognostic tools, clinical decision making will be potentially improved in the conservative and operative management of scoliosis patients. |
| format | Article |
| id | doaj-art-35d9a2b6b0ca4d75b503e3de110fc0b6 |
| institution | DOAJ |
| issn | 2379-1381 2379-139X |
| language | English |
| publishDate | 2024-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Tomography |
| spelling | doaj-art-35d9a2b6b0ca4d75b503e3de110fc0b62025-08-20T02:57:04ZengMDPI AGTomography2379-13812379-139X2024-11-0110121915192910.3390/tomography10120139Automated Distal Radius and Ulna Skeletal Maturity Grading from Hand Radiographs with an Attention Multi-Task Learning MethodXiaowei Liu0Rulan Wang1Wenting Jiang2Zhaohua Lu3Ningning Chen4Hongfei Wang5School of Computing and Artificial Intelligence, Shandong University of Finance and Economics, Jinan 250000, ChinaShenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518000, ChinaDepartment of Diagnostic Radiology, The University of Hong Kong, Hong Kong 999077School of Computing and Artificial Intelligence, Shandong University of Finance and Economics, Jinan 250000, ChinaDepartment of Orthopedic Surgery, The Seventh Affiliated Hospital, Sun Yat-Sen University, Shenzhen 518000, ChinaDepartment of Orthopaedics and Traumatology, The University of Hong Kong, Hong Kong 999077<b>Background:</b> Assessment of skeletal maturity is a common clinical practice to investigate adolescent growth and endocrine disorders. The distal radius and ulna (DRU) maturity classification is a practical and easy-to-use scheme that was designed for adolescent idiopathic scoliosis clinical management and presents high sensitivity in predicting the growth peak and cessation among adolescents. However, time-consuming and error-prone manual assessment limits DRU in clinical application. <b>Methods</b>: In this study, we propose a multi-task learning framework with an attention mechanism for the joint segmentation and classification of the distal radius and ulna in hand X-ray images. The proposed framework consists of two sub-networks: an encoder–decoder structure with attention gates for segmentation and a slight convolutional network for classification. <b>Results:</b> With a transfer learning strategy, the proposed framework improved DRU segmentation and classification over the single task learning counterparts and previously reported methods, achieving an accuracy of 94.3% and 90.8% for radius and ulna maturity grading. <b>Findings:</b> Our automatic DRU assessment platform covers the whole process of growth acceleration and cessation during puberty. Upon incorporation into advanced scoliosis progression prognostic tools, clinical decision making will be potentially improved in the conservative and operative management of scoliosis patients.https://www.mdpi.com/2379-139X/10/12/139bone agehand-wrist X-rayscoliosisdeep learningclassificationsegmentation |
| spellingShingle | Xiaowei Liu Rulan Wang Wenting Jiang Zhaohua Lu Ningning Chen Hongfei Wang Automated Distal Radius and Ulna Skeletal Maturity Grading from Hand Radiographs with an Attention Multi-Task Learning Method Tomography bone age hand-wrist X-ray scoliosis deep learning classification segmentation |
| title | Automated Distal Radius and Ulna Skeletal Maturity Grading from Hand Radiographs with an Attention Multi-Task Learning Method |
| title_full | Automated Distal Radius and Ulna Skeletal Maturity Grading from Hand Radiographs with an Attention Multi-Task Learning Method |
| title_fullStr | Automated Distal Radius and Ulna Skeletal Maturity Grading from Hand Radiographs with an Attention Multi-Task Learning Method |
| title_full_unstemmed | Automated Distal Radius and Ulna Skeletal Maturity Grading from Hand Radiographs with an Attention Multi-Task Learning Method |
| title_short | Automated Distal Radius and Ulna Skeletal Maturity Grading from Hand Radiographs with an Attention Multi-Task Learning Method |
| title_sort | automated distal radius and ulna skeletal maturity grading from hand radiographs with an attention multi task learning method |
| topic | bone age hand-wrist X-ray scoliosis deep learning classification segmentation |
| url | https://www.mdpi.com/2379-139X/10/12/139 |
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