Feasibility of video-based skills assessment: a study on ultrasound-guided needle insertions using simulated projections
Bibliographic record
Abstract
Automated skills assessment of ultrasound-guided needle insertions has previously been explored through 3D motion tracking data. The purpose of this study was to determine the viability of 2D motion tracking data in distinguishing between novice and expert subjects. METHODS: Perspective projection was applied to needle and ultrasound probe time series data. The resulting time series data of 2D points were used to calculate various performance metrics. Using these metrics, classifications between novice and expert were performed by random forest. This procedure was repeated with different camera positions all pointing at the reference point to examine systematically the effect of camera position on assessment. RESULTS: For in-plane needle insertions, mean AUC obtained through 3D data and mean AUC obtained through 2D data were well-matched (0.68 vs. 0.69). For out-of-plane insertions, mean AUC values from 3D and 2D data were more distant (0.86 vs. 0.77), but AUC from the optimal camera angle matched up well (0.85). CONCLUSION: 2D data is comparable to 3D data when used to perform skills assessment of ultrasound-guided needle insertions, and camera placement level with the instruments is optimal. We conclude that videos of needle insertions may be feasible for skills assessment.
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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 | 0.005 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".