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
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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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".