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Purpose Accurate acetabular cup placement is critical for the success of total hip arthroplasty (THA). The aim of this study was to evaluate the accuracy and precision of a CT-based, AI-driven navigation tool for acetabular cup placement in supine and lateral patient positions, using a patient-specific 3D-printed pelvic model with an embedded acetabular cup rim as a physical measurement reference. Methods Twenty-six patient-specific pelvic models were generated from preoperative CT data. A virtual acetabular cup was added to each pelvic mesh, cropped to the rim, and 3D-printed together with the bone, yielding a physical cup reference embedded in the printed pelvis. The ground-truth cup orientation and position were defined within the planning application by overlaying a cup component template on the embedded rim. Each model was tested once in supine and once in lateral position in a paired design. Ten cases were repeated three times per position to assess intra-session precision. Outcomes were surface matching root-mean-square error (RMSE), cup anteversion, inclination, and depth errors. Analyses included two one-sided tests (TOST) for equivalence (±0.5 mm), Bland–Altman agreement, Levene's test, and intraclass correlation coefficients (ICC). Results Mean alignment-point RMSE was 0.26 mm (supine) and 0.28 mm (lateral); mean independent test-point RMSE was 0.30 mm in both positions, with TOST confirming equivalence within ±0.5 mm. Mean absolute anteversion error was 0.67° (supine) and 0.44° (lateral), with comparable inclination errors. Mean absolute cup depth error remained well below ±3 mm. Bland–Altman analyses showed narrow 95% limits of agreement between positions. All ICC(3,1) values for cup orientation exceeded 0.99 in both positions. Conclusions The evaluated navigation tool achieved sub-millimeter surface matching, small cup placement errors, and excellent intra-session precision in both supine and lateral positions. The embedded-rim phantom enabled direct measurement of cup orientation and seating depth on the printed model and is applicable to other CT-based navigation systems.
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Liang et al. (2026) studied this question.
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