Machine learning analysis of cervical balance in early-onset scoliosis post-growing rod surgery: a case-control study
Abstract We aimed to analyze the cervical sagittal alignment change following the growing rod treatment in early-onset scoliosis (EOS) and identify the risk factors of sagittal cervical imbalance after growing-rod surgery of machine learning. EOS patients from our centre between 2007 and 2019 were r...
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Nature Portfolio
2025-01-01
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| Online Access: | https://doi.org/10.1038/s41598-025-86330-2 |
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| author | Bo Han Junrui Jonathan Hai Aixing Pan Yingjie Wang Yong Hai |
| author_facet | Bo Han Junrui Jonathan Hai Aixing Pan Yingjie Wang Yong Hai |
| author_sort | Bo Han |
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| description | Abstract We aimed to analyze the cervical sagittal alignment change following the growing rod treatment in early-onset scoliosis (EOS) and identify the risk factors of sagittal cervical imbalance after growing-rod surgery of machine learning. EOS patients from our centre between 2007 and 2019 were retrospectively reviewed. Radiographic parameters include the cervical lordosis (CL), T1 slope, C2-C7 sagittal vertical axis (C2-7 SVA), primary curve Cobb angle, thoracic kyphosis (TK), C7-S1 sagittal vertical axis (C7-S1 SVA) and proximal junctional angle (PJA) were evaluated preoperatively, postoperatively and at the final follow-up. The parameters were analyzed using a t-test and χ2 test. The machine learning methodology of a sparse additive machine (SAM) was applied to identify the risk factors that caused the cervical imbalance. 138 patients were enrolled in this study (96 male and 42 female). The mean thoracic curve Cobb angle was 67.00 ± 22.74°. The mean age at the first operation was 8.5 ± 2.6yrs. The mean follow-up was 38.48 ± 10.87 months. CL, T1 slope, and C2-7 SVA increased significantly in the final follow-up compared with the pre-operative data. (P < 0.05). The CL and T1 slope increased more significantly in the group of patients who had proximal junctional kyphosis (PJK) compared with the patients without PJK (P < 0.05). The location of the upper instrumented vertebrae (UIV) and single/dual growing rod had no significant influence on the sagittal cervical parameters (P > 0.05). According to the SAM analysis of machine learning algorithms, Postoperative PJK, more improvement of kyphosis, and T1 slope angle were identified as the risk factors of cervical sagittal imbalance during the treatment of growing rod surgery. The growing rod surgery in EOS significantly affected the cervical sagittal alignment. Postoperative PJK and more improvement of kyphosis and T1 slope angle would lead to a higher incidence of cervical sagittal imbalance. |
| format | Article |
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| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Nature Portfolio |
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| series | Scientific Reports |
| spelling | doaj-art-daf8d114c9c34ed0adef34e582b360a22025-01-19T12:18:08ZengNature PortfolioScientific Reports2045-23222025-01-0115111110.1038/s41598-025-86330-2Machine learning analysis of cervical balance in early-onset scoliosis post-growing rod surgery: a case-control studyBo Han0Junrui Jonathan Hai1Aixing Pan2Yingjie Wang3Yong Hai4Beijing Jishuitan Hospital, Affiliated with Capital Medical UniversityPrinceton International School of Mathematics and ScienceDepartment of Orthopedic Surgery, Beijing Chao-Yang Hospital, Capital Medical UniversityCollege of Control Science and Engineering, China University of Petroleum (East China)Department of Orthopedic Surgery, Beijing Chao-Yang Hospital, Capital Medical UniversityAbstract We aimed to analyze the cervical sagittal alignment change following the growing rod treatment in early-onset scoliosis (EOS) and identify the risk factors of sagittal cervical imbalance after growing-rod surgery of machine learning. EOS patients from our centre between 2007 and 2019 were retrospectively reviewed. Radiographic parameters include the cervical lordosis (CL), T1 slope, C2-C7 sagittal vertical axis (C2-7 SVA), primary curve Cobb angle, thoracic kyphosis (TK), C7-S1 sagittal vertical axis (C7-S1 SVA) and proximal junctional angle (PJA) were evaluated preoperatively, postoperatively and at the final follow-up. The parameters were analyzed using a t-test and χ2 test. The machine learning methodology of a sparse additive machine (SAM) was applied to identify the risk factors that caused the cervical imbalance. 138 patients were enrolled in this study (96 male and 42 female). The mean thoracic curve Cobb angle was 67.00 ± 22.74°. The mean age at the first operation was 8.5 ± 2.6yrs. The mean follow-up was 38.48 ± 10.87 months. CL, T1 slope, and C2-7 SVA increased significantly in the final follow-up compared with the pre-operative data. (P < 0.05). The CL and T1 slope increased more significantly in the group of patients who had proximal junctional kyphosis (PJK) compared with the patients without PJK (P < 0.05). The location of the upper instrumented vertebrae (UIV) and single/dual growing rod had no significant influence on the sagittal cervical parameters (P > 0.05). According to the SAM analysis of machine learning algorithms, Postoperative PJK, more improvement of kyphosis, and T1 slope angle were identified as the risk factors of cervical sagittal imbalance during the treatment of growing rod surgery. The growing rod surgery in EOS significantly affected the cervical sagittal alignment. Postoperative PJK and more improvement of kyphosis and T1 slope angle would lead to a higher incidence of cervical sagittal imbalance.https://doi.org/10.1038/s41598-025-86330-2Cervical alignmentEarly-Onset ScoliosisGrowing rodProximal junctional kyphosisMachine learning algorithm |
| spellingShingle | Bo Han Junrui Jonathan Hai Aixing Pan Yingjie Wang Yong Hai Machine learning analysis of cervical balance in early-onset scoliosis post-growing rod surgery: a case-control study Scientific Reports Cervical alignment Early-Onset Scoliosis Growing rod Proximal junctional kyphosis Machine learning algorithm |
| title | Machine learning analysis of cervical balance in early-onset scoliosis post-growing rod surgery: a case-control study |
| title_full | Machine learning analysis of cervical balance in early-onset scoliosis post-growing rod surgery: a case-control study |
| title_fullStr | Machine learning analysis of cervical balance in early-onset scoliosis post-growing rod surgery: a case-control study |
| title_full_unstemmed | Machine learning analysis of cervical balance in early-onset scoliosis post-growing rod surgery: a case-control study |
| title_short | Machine learning analysis of cervical balance in early-onset scoliosis post-growing rod surgery: a case-control study |
| title_sort | machine learning analysis of cervical balance in early onset scoliosis post growing rod surgery a case control study |
| topic | Cervical alignment Early-Onset Scoliosis Growing rod Proximal junctional kyphosis Machine learning algorithm |
| url | https://doi.org/10.1038/s41598-025-86330-2 |
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