Validity and reliability of active shape models for the estimation of Cobb angles in adolescent idiopathic scoliosis.
Bibliographic record
Abstract
Choosing the most suitable treatment for scoliosis relies heavily on accurate and reproducible Cobb angle measurement from successive radiographs. The objective is to reduce variability of Cobb angle measurement by reducing user intervention and bias. Custom software to automate Cobb angle measurement from posteroanterior radiographs was developed using active shape models. Validity and reliability of the automated system against a manual and semi-automated measurement method was conducted by two examiners each performing measurements on 3 occasions from a test set (N=22). A training set (N=47) of radiographs representative of curves seen in a scoliosis clinic was used to train the software to recognize vertebrae from T4 to L4. Images with a maximum Cobb angle between 20 degrees and 50 degrees, excluding surgical cases, were selected for training and test sets. Automated Cobb angles were calculated using best-fit slopes of the detected vertebrae endplates. Intra-class correlation coefficient (ICC) and standard error of measurement (SEM) showed high intra-examiner (ICC > 0.90, SEM 2-3 degrees) and inter-examiner (ICC > 0.82, SEM 2-4 degrees), but poor inter-method reliability (ICC=0.30, SEM 8-9 degrees). The automated method underestimated large curves. The reliability improved (ICC = 0.70, SEM 4-5 degrees) with exclusion of the 4 largest curves (>40 degrees) in the test set. The automated method was reliable for moderate sized curves, but did not properly detect vertebrae in larger curves. Optimization of constraints on scaling, rotation, translation, and iteration may improve reliability with larger curves.
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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 | 0.019 | 0.077 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 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 | 0.001 |
| 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".