Development and validation of a fully automated tool to quantify 3D foot and ankle alignment using weight-bearing CT

Abstract

Introduction: Foot and ankle alignment plays a pivotal role in human gait and posture. Traditional assessment methods, relying on 2D standing radiographs, present limitations in capturing the dynamic 3D nature of foot alignment during weight-bearing and are prone to observer error. This study aims to integrate weight-bearing CT (WBCT) imaging and advanced deep learning (DL) techniques to automate and enhance quantification of the 3D foot and ankle alignment.

Methods: Thirty-two patients who underwent a WBCT of the foot and ankle were retrospectively included. After training and validation of a 3D nnU-Net model on 45 cases to automate the segmentation into bony models, 35 clinically relevant 3D measurements were automatically computed using a custom-made tool. Automated measurements were assessed for accuracy against manual measurements, while the latter were analyzed for inter-observer reliability.

Results: DL-segmentation results showed a mean dice coefficient of 0.95 and mean Hausdorff distance of 1.41 mm. A good to excellent reliability and mean prediction error of under 2 degrees was found for all angles except the talonavicular coverage angle and distal metatarsal articular angle.

Conclusion: In summary, this study introduces a fully automated framework for quantifying foot and ankle alignment, showcasing reliability comparable to current clinical practice measurements. This operator-friendly and time-efficient tool holds promise for implementation in clinical settings, benefiting both radiologists and surgeons. Future studies are encouraged to assess the tool's impact on streamlining image assessment workflows in a clinical environment.

Keywords: 3D analysis; Deep Learning; Foot Alignment; Segmentation; Weight-bearing CT.