Schmaranzer, Florian; Movahhedi, Mohammadreza; Singh, Mallika; Kallini, Jennifer R; Nanavati, Andreas K; Steppacher, Simon D; Heimann, Alexander F; Kiapour, Ata M; Novais, Eduardo N (2024). Computed tomography-based automated 3D measurement of femoral version: Validation against standard 2D measurements in symptomatic patients. (In Press). Journal of orthopaedic research Wiley 10.1002/jor.25865
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Journal_Orthopaedic_Research_-_2024_-_Schmaranzer_-_Computed_tomography_based_automated_3D_measurement_of_femoral_version_.pdf - Published Version Available under License Creative Commons: Attribution-Noncommercial-No Derivative Works (CC-BY-NC-ND). Download (2MB) | Preview |
To validate 3D methods for femoral version measurement, we asked: (1) Can a fully automated segmentation of the entire femur and 3D measurement of femoral version using a neck based method and a head-shaft based method be performed? (2) How do automatic 3D-based computed tomography (CT) measurements of femoral version compare to the most commonly used 2D-based measurements utilizing four different landmarks? Retrospective study (May 2017 to June 2018) evaluating 45 symptomatic patients (57 hips, mean age 18.7 ± 5.1 years) undergoing pelvic and femoral CT. Femoral version was assessed using four previously described methods (Lee, Reikeras, Tomczak, and Murphy). Fully-automated segmentation yielded 3D femur models used to measure femoral version via femoral neck- and head-shaft approaches. Mean femoral version with 95% confidence intervals, and intraclass correlation coefficients were calculated, and Bland-Altman analysis was performed. Automatic 3D segmentation was highly accurate, with mean dice coefficients of 0.98 ± 0.03 and 0.97 ± 0.02 for femur/pelvis, respectively. Mean difference between 3D head-shaft- (27.4 ± 16.6°) and 3D neck methods (12.9 ± 13.7°) was 14.5 ± 10.7° (p < 0.001). The 3D neck method was closer to the proximal Lee (-2.4 ± 5.9°, -4.4 to 0.5°, p = 0.009) and Reikeras (2 ± 5.6°, 95% CI: 0.2 to 3.8°, p = 0.03) methods. The 3D head-shaft method was closer to the distal Tomczak (-1.3 ± 7.5°, 95% CI: -3.8 to 1.1°, p = 0.57) and Murphy (1.5 ± 5.4°, -0.3 to 3.3°, p = 0.12) methods. Automatic 3D neck-based-/head-shaft methods yielded femoral version angles comparable to the proximal/distal 2D-based methods, when applying fully-automated segmentations.
Item Type: |
Journal Article (Original Article) |
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Division/Institute: |
04 Faculty of Medicine > Department of Orthopaedic, Plastic and Hand Surgery (DOPH) > Clinic of Orthopaedic Surgery 04 Faculty of Medicine > Department of Radiology, Neuroradiology and Nuclear Medicine (DRNN) > Institute of Diagnostic, Interventional and Paediatric Radiology |
UniBE Contributor: |
Schmaranzer, Florian, Nanavati, Andreas Kavin, Steppacher, Simon Damian |
Subjects: |
600 Technology > 610 Medicine & health |
ISSN: |
0736-0266 |
Publisher: |
Wiley |
Language: |
English |
Submitter: |
Pubmed Import |
Date Deposited: |
29 Apr 2024 13:48 |
Last Modified: |
30 Apr 2024 09:22 |
Publisher DOI: |
10.1002/jor.25865 |
PubMed ID: |
38678375 |
Uncontrolled Keywords: |
deep learning femoral osteotomy femoral version hip arthroscopy |
BORIS DOI: |
10.48350/196318 |
URI: |
https://boris.unibe.ch/id/eprint/196318 |