An accuracy assessment of SlicerAutoscoperM − software for tracking skeletal structures in multi-plane videoradiography datasets
Bibliographic record
Abstract
(SAM), an upgrade of the markerless tracking software (Autoscoper) and its migration into the established computing environment of 3DSlicer and addition of a comprehensive pre-processing module to provide a standardized workflow. We present the accuracy and agreement in tracking four skeletal joints by four research groups. Accuracy was assessed by comparing marker-generated and SAM kinematics for bones of the foot, knee, shoulder, and wrist. Bland-Altman analyses quantified bias (mean error) and limits of agreement (LOA). Tracking accuracy was robust for all joints. In the foot, mean error (bias) was less than 0.5° (1.8°) and 0.8 mm (3.1 mm). In the knee, mean error was less than 1.0° (1.5°) and 0.4 mm (0.8 mm). In the shoulder, mean translational error for both the humerus and scapula was less than 0.2 mm (0.7 mm). Rotational error was highest in Roll and Pitch for the humerus, 1.9° (4.8°) and 1.7° (4.6°), respectively, and Yaw was 0.3° (2.1°). The scapula rotational bias was less than 0.2° (0.7°). In the wrist, the error was less than 0.05° (1.2°) and 0.5 mm (1.2 mm). Our data demonstrate that SAM is an accurate image-based skeletal motion tracking tool. With broad adoption, SAM will promote collaboration, simplify the harmonization of methods between study sites for large multi-center research studies, lower the entry bar for early-stage investigators, and facilitate translations toward clinical use.
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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 | 0.008 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".