Using standard X-ray images to create 3D digital bone models and patient-matched guides for aiding implant positioning and sizing in total knee arthroplasty
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Bibliographic record
Abstract
X-Atlas™ is a new imaging technology intended to advance the state of the art in patient-specific instrumentation. It uses standard AP and lateral radiographs instead of CT or MRI scans to create 3D bone models, which can be used to perform pre-operative surgical planning and fabricate TKA personalized guides. The aim of this study was to validate X-Atlas™ and evaluate the accuracy of personalized guides created with this imaging technology. Its ability to predict implant size was also determined. The accuracy of the X-Atlas™ imaging technology was evaluated by comparing the landmarks of X-Atlas™ 3D Bone models to MRI-reconstructed bone models. The accuracy of PSI guides created with X-Atlas™ (X-PSI™ guides) was evaluated during a validation study (16 specimen knees) and a clinical study (50 patients; Health Canada #CSU2015-12K). Optical navigation was used to measure positioning accuracy. In addition, the ability of X-Atlas™ to predict implant size was assessed. The accuracy of the X-Atlas™ imaging technology was below 0.87 and 1.28 mm for the femoral and tibial landmarks, respectively. The accuracy of X-PSI™ guides to reproduce the pre-operative planned HKA was within ± 3° in 100% and 86.1% of cases, for the laboratory and clinical study respectively, which was significantly better than historical data for conventional instrumentation. X-Atlas™ was able to predict implant size to ± 1 size in 95.6% and 100%, for the femur and tibia component respectively. The X-Atlas™ imaging technology demonstrated excellent accuracy for reconstructing a 3D bone model. The results show that PSI guides created with X-Atlas™ (X-PSI™ guides) provide greater implant positioning accuracy than conventional instrumentation, without the requirement of advanced imaging. Furthermore, the X-Atlas™ imaging technology could effectively predict implant sizing, potentially reducing the number of instrument trays and improving surgical efficiency.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 it