@article{13823ada-37e5-4e06-bbe4-680e4340892e,
  abstract     = {{<p>Background/Objectives: Radiostereometric analysis (RSA) is the gold standard for measuring implant migration, with CT-RSA increasingly used as an alternative. To evaluate CT-RSA, it is important to assess data that include the surrounding soft tissues, rather than data from simplified phantoms, while also avoiding unnecessary radiation from multiple scans. This study proposes a method for generating multiple follow-up CTs from a single post-operative CT (baseline CT) by simulating stem migration and uses it to assess an AI-based CT-RSA tool. Methods: The method involves extracting the stem implant voxels from the baseline CT, digitally translating them along the x-, y-, and z-axes, and storing the result as new follow-up CTs. The voxel spacing of the baseline CT is used to define the ground-truth translations, which are then compared with the AI-based CT-RSA results using descriptive statistics and Bland–Altman plots. Results: Using 10 patients’ baseline CTs, 780 follow-up CTs were generated. Bland–Altman analysis showed a mean difference of 0.00 mm, largest LoA −0.10 to 0.09 mm, and translational precision for zero-migration of 0.026 to 0.049 mm. Conclusions: The proposed method offers a practical alternative to phantom-based models, and the AI-based CT-RSA showed high accuracy and precision for stem translation. The study addresses translational migration only.</p>}},
  author       = {{M. Nemati, Hassan and Christensson, Albin and Pettersson, Andreas and Flivik, Gunnar}},
  issn         = {{2075-4418}},
  keywords     = {{AI-based CT-RSA; computer assisted; hip implant migration; medical image processing; Ortoma treatment solution; synthetic implant migration}},
  language     = {{eng}},
  number       = {{10}},
  publisher    = {{MDPI AG}},
  series       = {{Diagnostics}},
  title        = {{Synthetic Implant Migration Generation for Accuracy and Precision Evaluation of AI-Based CT-RSA in Total Hip Arthroplasty}},
  url          = {{http://dx.doi.org/10.3390/diagnostics16101484}},
  doi          = {{10.3390/diagnostics16101484}},
  volume       = {{16}},
  year         = {{2026}},
}