Effects of axial malrotation on posterior tibial slope measurement: a digitally reconstructed radiograph study enabling automated quality assessment.
Authors
Affiliations (12)
Affiliations (12)
- Seoul National University College of Medicine, Seoul, Republic of Korea.
- Oslo Sports Trauma Research Center, Norwegian School of Sports Science, Oslo, Norway.
- Department of Arthroscopy and Sports Medicine, Oslo University Hospital Aker, Oslo, Norway.
- Department of Orthopedic Surgery, University of Minnesota, Minneapolis, MN, USA.
- Department of Orthopedic Surgery, CentraCare, Saint Cloud, MN, USA.
- Department of Clinical Medicine, University of Bergen, Bergen, Norway.
- Haukeland University Hospital, Bergen, Norway.
- Department of Orthopedic Surgery, Seoul National University College of Medicine, Seoul National University Hospital, 101 Daehakro, Jongno-Gu, Seoul, 110-744, Republic of Korea.
- National Strategic Technology Research Institute, Biomedical Research Institute, Seoul National University Hospital, Seoul, Republic of Korea.
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA.
- Department of Orthopedic Surgery, Seoul National University College of Medicine, Seoul National University Hospital, 101 Daehakro, Jongno-Gu, Seoul, 110-744, Republic of Korea. [email protected].
- CONNECTEVE Co., Ltd, Seoul, Republic of Korea. [email protected].
Abstract
Accurate measurement of posterior tibial slope (PTS) is highly sensitive to axial malrotation of the knee during acquisition, but its impact has not been systematically quantified across different anatomical variations. This simulation study aimed to quantify the effect of axial malrotation on PTS using digitally reconstructed radiographs (DRRs) and suggest a practical marker for filtering out low-quality images with excessive malrotation. A total of 55 preoperative computed tomography (CT) scans from January 2021 to December 2024 in a single, tertiary hospital were retrospectively reviewed. DRRs were generated from those scans to simulate lateral knee radiographs with malrotation ranging from -12° to +12° relative to an anatomically aligned baseline. An artificial-intelligence (AI)-based tool automatically measured PTS on each DRR, with agreement evaluated using intraclass correlation coefficient (ICC). PCDR was calculated from femoral contours and analyzed for correlation with malrotation angles and resulting PTS measurement error. AI-based PTS measurements on DRRs showed good agreement with expert annotations (ICC = 0.78, 95% CI 0.73-0.82). PTS increased linearly with internal rotation, with each 1° of rotation resulting in approximately 0.2° change in PTS (R<sup>2</sup> = 0.43, p < 0.01). Errors exceeded 1° when malrotation surpassed ±6°. PCDR was strongly correlated with malrotation angle (R<sup>2</sup> > 0.98, p < 0.001) and achieved fair discriminative performance as a binary classifier for > 1° PTS error [area under the receiver operating curve (AUROC) = 0.77]. CT-derived DRRs combined with AI analysis showed that PTS measurement error proportionately increased with axial malrotation. Identifying and excluding radiographs with excessive rotation improves the reliability of slope-based assessments and supports more accurate surgical planning. III, retrospective cohort study.