Evaluation of Fluoroscopic and Imageless Navigation Systems for Acetabular Component Positioning in Direct Anterior Approach Total Hip Arthroplasty
Bibliographic record
Abstract
BACKGROUND: Malpositioning of the acetabular component in total hip arthroplasty (THA) is a critical factor contributing to complications such as instability, impingement, and the need for revision surgery. This study aimed to compare the accuracy of acetabular component positioning in the direct anterior approach (DAA) using three techniques: conventional fluoroscopy, fluoroscopic image-dependent navigation, and imageless navigation. METHODS: A retrospective cohort study was conducted with 150 patients undergoing primary THA using the DAA. Patients were grouped based on the technique used (50 patients per group). Intraoperative cup inclination and anteversion were recorded, and postoperative measurements were obtained using 6-week antero-posterior radiographs. The primary outcomes included deviations in anteversion and inclination between intraoperative and postoperative measurements. The secondary outcomes included operative time and 60-day postoperative complications. RESULTS: A total of 150 patients undergoing DAA-THA were included, with 50 patients in each group: conventional fluoroscopy, fluoroscopic image-dependent navigation, and imageless navigation. The imageless group had significantly longer operative time (75.5 ± 10.8 minutes) than conventional fluoroscopy (65.8 ± 8.5 minutes) and image-dependent (68.9 ± 10.7 minutes), P < 0.0001. Radiographic analysis indicated that image-dependent navigation provided the highest accuracy, with 83 and 69% of cups placed within the target zones for the two surgeons, outperforming conventional and imageless methods. The imageless system showed improved accuracy over conventional fluoroscopy and image-dependent navigation when comparing intraoperative and postoperative inclination and anteversion. Postoperative anteversion (P = 0.08) and inclination (P = 0.94) showed no significant differences among groups. Complication rates, including dislocations and infections, were similar, though one periprosthetic joint infection was noted in both the conventional and image-dependent navigation groups, with no PJIs reported in the imageless group. CONCLUSIONS: Navigation systems, particularly fluoroscopic image-dependent navigation, enhance acetabular component positioning accuracy over conventional methods in DAA-THA. However, imageless navigation requires optimization to reduce operative time and improve anteversion accuracy.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".