Automating Cobb Angle Measurement for Adolescent Idiopathic Scoliosis using Instance Segmentation
Bibliographic record
Abstract
Scoliosis is a three-dimensional deformity of the spine, often diagnosed in childhood. It affects 2-3% of the population, representing seven million people in North America. Currently, the gold standard for assessing scoliosis is done manually by measuring Cobb angles. This manual process is time-consuming and unreliable as it is affected by inter- and intra-observer variance. To eliminate these inaccuracies, machine learning (ML) methods can be used to automate the Cobb angle measurement process. This paper proposes to address the Cobb angle measurement task using an instance segmentation model. The proposed method first segments the vertebrae in an X-ray image using YOLACT model, then tracks the decisive landmarks using minimum bounding boxes. Lastly, the extracted landmarks are used to calculate the corresponding Cobb angles. The proposed method achieved a Symmetric Mean Absolute Percentage Error (SMAPE) score of 10.76%, outperforming the results presented in previous research. Additionally, more than 94% of the estimated Cobb angles had an error of less than ten degrees. The proposed method demonstrates reliability in both vertebra localization and Cobb angle measurement.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".