Automated Identification of Make and Model of Total Wrist Replacement Implants using Deep Learning
Bibliographic record
Abstract
Accurately identifying orthopaedic implants is a crucial step in executing revision surgeries, as any misidentification can result in surgical delays and adverse outcomes.With the rising number of primary and revision surgeries for wrist replacement, there is a growing need for a reliable method to recognize the make and model of wrist implants depicted in X-ray images.This paper proposes an innovative approach that employs deep learning techniques to accurately identify wrist implants, potentially enhancing the precision and efficiency of revision surgeries.The study demonstrated that the utilisation of deep learning techniques was extremely effective in identifying the exact make and model of wrist implants from X-ray images, with a remarkable accuracy rate of 95.12% a superior Area Under Curve (AUC) of 0.9959 in identifying 3 models of total wrist prosthesis.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".