Deep Learning-Based Segmentation for Automated Detection of Tibial Implant Loosening

Overview

This study developed a fully automatic deep learning (DL) segmentation model to replace the semi-automatic approach for quantifying tibial implant displacement in total knee arthroplasty (TKA). The DL model demonstrated comparable performance to the current workflow, enabling automated and efficient analysis of implant loosening using paired CT scans under varus and valgus loading.

Background

Revision surgery after TKA occurs in approximately 13% of cases within 10 years, with implant loosening being a major cause in about 30% of revisions. Current diagnostic imaging methods provide indirect signs of loosening and may lead to misdiagnosis. The gold standard for measuring implant migration, model-based Roentgen stereo photogrammetric analysis (RSA), is invasive and limited to research. A recent non-invasive workflow uses paired CT scans under load to quantify implant displacement, but relies on semi-automatic segmentation that is time-consuming and affected by metal artifacts. Deep learning offers potential for fully automated segmentation to improve this workflow.

Data Highlights

Key Findings

• The DL segmentation model (nnUNet) successfully replaced semi-automatic segmentation in the implant displacement workflow.
• Three annotation protocols and both 2D and 3D models were evaluated to optimize segmentation and downstream displacement quantification.
• The automated segmentation showed robust performance despite metal artifacts in CT scans.
• Displacement measurements using DL segmentation were not significantly different from those using semi-automatic segmentation.
• The approach was validated on cadaveric and patient datasets, demonstrating applicability in real-world clinical data.

Clinical Implications

The introduction of a fully automated DL segmentation method streamlines the quantification of tibial implant displacement, reducing manual effort and potential user variability. This advancement facilitates more widespread and efficient assessment of implant loosening in clinical practice, potentially improving diagnostic accuracy and patient management without the need for invasive procedures.

Conclusion

This study demonstrates that deep learning-based automatic segmentation can effectively replace semi-automatic methods in the CT-based workflow for detecting tibial implant loosening, enabling accurate and efficient quantification of implant displacement under load.

References

1. Kievit et al. 2021 -- Non-invasive quantification of tibial implant displacement using CT
2. Buijs et al. 2023 -- Evaluation of CT-based implant displacement analysis
3. Roentgen Stereo Photogrammetric Analysis (RSA) -- Standard for implant migration measurement
4. Deep Learning in Knee Arthroplasty Imaging -- Recent advances in implant loosening detection