# Effects of axial malrotation on posterior tibial slope measurement: a digitally reconstructed radiograph study enabling automated quality assessment

## TL;DR

This study shows how knee rotation during imaging affects the accuracy of measuring the posterior tibial slope and suggests a way to filter out poor-quality images.

## Contribution

The study introduces a practical marker for identifying and filtering low-quality images due to axial malrotation in PTS measurements.

## Key findings

- PTS measurement error increases linearly with axial malrotation, exceeding 1° at ±6° rotation.
- PCDR is strongly correlated with malrotation and shows fair performance in detecting PTS errors.
- AI-based measurements on DRRs showed good agreement with expert annotations (ICC = 0.78).

## 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 (R2 = 0.43, p < 0.01). Errors exceeded 1° when malrotation surpassed ±6°. PCDR was strongly correlated with malrotation angle (R2 > 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.

The online version contains supplementary material available at 10.1186/s43019-026-00303-x.