Automatic assessment of skeletal maturity in adolescent idiopathic scoliosis patients using support vector regression on deep features
Bibliographic record
Abstract
Skeletal maturity assessment is an important step in the treatment of adolescent idiopathic scoliosis (AIS). Traditional methods rely on the bone age assessment using 2D X-ray radiography. Assessment is generally performed using the Risser stages that is routinely used to assess the skeletal maturity through the observation of the level of ossification of iliac crests. This bone maturity assessment method is preferred in AIS but in practice, shows a rather high-level interobserver variability. This study aims to use an automatic Risser stage classification for a longitudinal study of follow-up visits to observe growth indicators of AIS patients. A regression model will then be used to evaluate the maturity changes of patients from à Risser stage to another. For the classification task, the pre-trained model of VGG16 was implemented with Python 3.10. The network parameters were changed since the task we were training it for contained a smaller dataset. The first experiments of this work were for the classification of the patient’s Risser signs. After several tests of optimization of the SVR classifiers hyperparameter, a mean square error of 0.38, a mean absolute error of 0.31 and an R2 of 0.33. An optimization of the network and a pre-processing of the images will be done in the next phases of this project.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".