Automation in tibial implant loosening detection using deep-learning segmentation.

ABSTRACT:

Purpose

Patients with recurrent complaints after total knee arthroplasty may suffer from aseptic implant loosening. Current imaging modalities do not quantify looseness of knee arthroplasty components. A recently developed and validated workflow quantifies the tibial component displacement relative to the bone from CT scans acquired under valgus and varus load. The 3D analysis approach includes segmentation and registration of the tibial component and bone. In the current approach, the semi-automatic segmentation requires user interaction, adding complexity to the analysis. The research question is whether the segmentation step can be fully automated while keeping outcomes indifferent.

Methods

In this study, different deep-learning (DL) models for fully automatic segmentatio

SUBMITTER: Magg C 

PROVIDER: S-EPMC12518389 | biostudies-literature | 2025 Oct

REPOSITORIES: biostudies-literature