A Model-Free Feature-Based Bi-Planar RSA Method for Kinematic Analysis of Total Knee Arthroplasty
Bibliographic record
Abstract
Fluoroscopic imaging is commonly used for assessing relative motions of orthopaedic implants. One limiting factor to in vivo model-based roentgen stereophotogrammetric analysis of total knee arthroplasty is the need for 3D models of the implants.The 3D models of the implant components must be reverse-engineered, if not provided by the company, which makes this method impractical for a clinical study involving many types or sizes of implants. This study introduces a novel feature-based methodology that registers the features at the implant-bone or implant-cement interface of the components that have elementary shapes. These features include pegs with hemispherical heads, and straight, circular or curved edges located on flat faces of the box of the femoral component or the stem geometry of the tibial component. Software was developed to allow easy registration of these features through a graphical user interface. The accuracy and precision of registration for multiple flexion angles from 0 to 120 deg was determined with reference to registered poses of the implants through experiments on bone replica models and also on a cadaver specimen implanted with total knee prostheses. When compared to an equivalent bi-planar model-based registration, the results were comparable: The mean accuracy of this feature-based method was 1.45 deg and 1.03 mm (in comparison to 0.95 deg and 1.32 mm for the model-based approach), and the mean precision was 0.57 deg and 0.26 mm (in comparison to 0.42 deg and 0.44 mm for the model-based approach).The methodology and the developed software can easily accommodate different design of implants with various fixation features. This method can facilitate in vivo kinematic analysis of total knee arthroplasty by eliminating the need for 3D models of the implant components.
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How this classification was reachedexpand
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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 teacher head, 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".