Development of a model for measuring sagittal plane parameters in 10–18-year old adolescents with idiopathic scoliosis based on RTMpose deep learning technology
Abstract Purpose The study aimed to develop a deep learning model for rapid, automated measurement of full-spine X-rays in adolescents with Adolescent Idiopathic Scoliosis (AIS). A significant challenge in this field is the time-consuming nature of manual measurements and the inter-individual variab...
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| Format: | Article |
| Language: | English |
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BMC
2025-01-01
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| Series: | Journal of Orthopaedic Surgery and Research |
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| Online Access: | https://doi.org/10.1186/s13018-024-05334-2 |
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| author | Zhijie Kang Guopeng Shi Yong Zhu Feng Li Xiaohe Li Haiyan Wang |
| author_facet | Zhijie Kang Guopeng Shi Yong Zhu Feng Li Xiaohe Li Haiyan Wang |
| author_sort | Zhijie Kang |
| collection | DOAJ |
| description | Abstract Purpose The study aimed to develop a deep learning model for rapid, automated measurement of full-spine X-rays in adolescents with Adolescent Idiopathic Scoliosis (AIS). A significant challenge in this field is the time-consuming nature of manual measurements and the inter-individual variability in these measurements. To address these challenges, we utilized RTMpose deep learning technology to automate the process. Methods We conducted a retrospective multicenter diagnostic study using 560 full-spine sagittal plane X-ray images from five hospitals in Inner Mongolia. The model was trained and validated using 500 images, with an additional 60 images for independent external validation. We evaluated the consistency of keypoint annotations among different physicians, the accuracy of model-predicted keypoints, and the accuracy of model measurement results compared to manual measurements. Results The consistency percentages of keypoint annotations among different physicians and the model were 90–97% within the 4-mm range. The model's prediction accuracies for key points were 91–100% within the 4-mm range compared to the reference standards. The model's predictions for 15 anatomical parameters showed high consistency with experienced physicians, with intraclass correlation coefficients ranging from 0.892 to 0.991. The mean absolute error for SVA was 1.16 mm, and for other parameters, it ranged from 0.22° to 3.32°. A significant challenge we faced was the variability in data formats and specifications across different hospitals, which we addressed through data augmentation techniques. The model took an average of 9.27 s to automatically measure the 15 anatomical parameters per X-ray image. Conclusion The deep learning model based on RTMpose can effectively enhance clinical efficiency by automatically measuring the sagittal plane parameters of the spine in X-rays of patients with AIS. The model's performance was found to be highly consistent with manual measurements by experienced physicians, offering a valuable tool for clinical diagnostics. |
| format | Article |
| id | doaj-art-b22f494faae84dce89ba8b98b98fca0f |
| institution | Kabale University |
| issn | 1749-799X |
| language | English |
| publishDate | 2025-01-01 |
| publisher | BMC |
| record_format | Article |
| series | Journal of Orthopaedic Surgery and Research |
| spelling | doaj-art-b22f494faae84dce89ba8b98b98fca0f2025-01-12T12:32:39ZengBMCJournal of Orthopaedic Surgery and Research1749-799X2025-01-0120111310.1186/s13018-024-05334-2Development of a model for measuring sagittal plane parameters in 10–18-year old adolescents with idiopathic scoliosis based on RTMpose deep learning technologyZhijie Kang0Guopeng Shi1Yong Zhu2Feng Li3Xiaohe Li4Haiyan Wang5Department of Human Anatomy, Graduate School, Inner Mongolia Medical UniversityDepartment of Human Anatomy, Graduate School, Inner Mongolia Medical UniversityTumor Hospital, Affiliated to Inner Mongolia Medical University, Inner Mongolia Medical UniversityDepartment of Spinal Surgery, The Second Affiliated Hospital of Inner Mongolia Medical UniversityDepartment of Human Anatomy, Graduate School, Inner Mongolia Medical UniversityDepartment of Human Anatomy, Graduate School, Inner Mongolia Medical UniversityAbstract Purpose The study aimed to develop a deep learning model for rapid, automated measurement of full-spine X-rays in adolescents with Adolescent Idiopathic Scoliosis (AIS). A significant challenge in this field is the time-consuming nature of manual measurements and the inter-individual variability in these measurements. To address these challenges, we utilized RTMpose deep learning technology to automate the process. Methods We conducted a retrospective multicenter diagnostic study using 560 full-spine sagittal plane X-ray images from five hospitals in Inner Mongolia. The model was trained and validated using 500 images, with an additional 60 images for independent external validation. We evaluated the consistency of keypoint annotations among different physicians, the accuracy of model-predicted keypoints, and the accuracy of model measurement results compared to manual measurements. Results The consistency percentages of keypoint annotations among different physicians and the model were 90–97% within the 4-mm range. The model's prediction accuracies for key points were 91–100% within the 4-mm range compared to the reference standards. The model's predictions for 15 anatomical parameters showed high consistency with experienced physicians, with intraclass correlation coefficients ranging from 0.892 to 0.991. The mean absolute error for SVA was 1.16 mm, and for other parameters, it ranged from 0.22° to 3.32°. A significant challenge we faced was the variability in data formats and specifications across different hospitals, which we addressed through data augmentation techniques. The model took an average of 9.27 s to automatically measure the 15 anatomical parameters per X-ray image. Conclusion The deep learning model based on RTMpose can effectively enhance clinical efficiency by automatically measuring the sagittal plane parameters of the spine in X-rays of patients with AIS. The model's performance was found to be highly consistent with manual measurements by experienced physicians, offering a valuable tool for clinical diagnostics.https://doi.org/10.1186/s13018-024-05334-2Adolescent idiopathic scoliosisRTMposeDeep learningSagittal plane |
| spellingShingle | Zhijie Kang Guopeng Shi Yong Zhu Feng Li Xiaohe Li Haiyan Wang Development of a model for measuring sagittal plane parameters in 10–18-year old adolescents with idiopathic scoliosis based on RTMpose deep learning technology Journal of Orthopaedic Surgery and Research Adolescent idiopathic scoliosis RTMpose Deep learning Sagittal plane |
| title | Development of a model for measuring sagittal plane parameters in 10–18-year old adolescents with idiopathic scoliosis based on RTMpose deep learning technology |
| title_full | Development of a model for measuring sagittal plane parameters in 10–18-year old adolescents with idiopathic scoliosis based on RTMpose deep learning technology |
| title_fullStr | Development of a model for measuring sagittal plane parameters in 10–18-year old adolescents with idiopathic scoliosis based on RTMpose deep learning technology |
| title_full_unstemmed | Development of a model for measuring sagittal plane parameters in 10–18-year old adolescents with idiopathic scoliosis based on RTMpose deep learning technology |
| title_short | Development of a model for measuring sagittal plane parameters in 10–18-year old adolescents with idiopathic scoliosis based on RTMpose deep learning technology |
| title_sort | development of a model for measuring sagittal plane parameters in 10 18 year old adolescents with idiopathic scoliosis based on rtmpose deep learning technology |
| topic | Adolescent idiopathic scoliosis RTMpose Deep learning Sagittal plane |
| url | https://doi.org/10.1186/s13018-024-05334-2 |
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