| Autor: |
Bin-Fei Zhang, Jun Wang, Yu-Min Zhang, Hui-Guang Cheng, Qian-Yue Cheng, Wen-Wen Cao |
| Jazyk: |
angličtina |
| Rok vydání: |
2021 |
| Předmět: |
|
| Zdroj: |
Journal of Orthopaedic Surgery and Research, Vol 16, Iss 1, Pp 1-10 (2021) |
| Druh dokumentu: |
article |
| ISSN: |
1749-799X |
| DOI: |
10.1186/s13018-021-02818-3 |
| Popis: |
Abstract Purpose This finite element analysis assessed lateral compression (LC-1) fracture stability using machine learning for morphological mapping and classification of pelvic ring stability. Methods Computed tomography (CT) files of LC-1 pelvic fractures were collected. After morphological mapping and producing matrix data, we used K-means clustering in unsupervised machine learning to classify the fractures. Based on these subtypes, we manually added fracture lines in ANSYS software. Finally, we performed a finite element analysis of a normal pelvis and eight fracture subtypes based on von Mises stress and total deformation changes. Results A total of 218 consecutive cases were analyzed. According to the three main factors—zone of sacral injury and completion, pubic ramus injury side, and the sagittal rotation of the injured hemipelvis—the LC-1 injuries were classified into eight subtypes (I–VIII). No significant differences in stress or deformation were observed between unilateral and bilateral public ramus fractures. Subtypes VI and VIII showed the maximum stress while subtypes V–VIII showed the maximum deformation in the total pelvis and sacrum. The subtypes did not differ in superior public ramus deformation. Conclusions Complete fracture of sacrum zones 2/3 may be a feature of unstable LC-1 fractures. Surgeons should give surgical strategies for subtypes V–VIII. |
| Databáze: |
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| Externí odkaz: |
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