Development of a synchronous motion-tracking and video capture tool for flexible ureteroscopy
Bibliographic record
Abstract
INTRODUCTION: Hand/instrument motion-tracking in surgical simulation provides valuable data to improve psychomotor skills and can serve as a formative evaluation tool. Motion analysis has been well-studied in laparoscopic surgery; however, there are essentially no studies looking at motion-tracking for flexible ureteroscopy (fURS ), a common surgical procedure requiring hand dexterity and 3D spatial awareness. We aimed to design a synchronized motion-tracking and video capture system for fURS capable of collecting objective metrics for use in surgical skills training. METHODS: Motion tracking of the ureteroscope was performed using a motion-tracking platform, inertial measurement units (IMUs), and an optical sensor. Position (x, y, z) and orientation (roll, pitch, yaw) of the ureteroscope handle, lever deflection, and translation of the scope insertion point were collected. Video capture of the operator's hands was collected with a Raspberry Pi camera. All peripherals were controlled on a Raspberry Pi 4 and synchronized to its system clock. RESULTS: Our system demonstrated good accuracy in detecting translation of the ureteroscope in the x- and y-axes, and yaw, pitch and roll of the ureteroscope at discrete orientations of 0, ±30, ±60, and ±90 degrees. Unique to fURS, deflection of the lever was captured by the difference in IMU static accelerations with good accuracy. The optical sensor detected translation of the ureteroscope at the insertion point with good precision and an average error of 5.51%. CONCLUSIONS: We successfully developed a motion-tracking and video-capture system capable of collecting motion-analysis parameters unique to fURS . Future studies will focus on establishing the construct validity of this tool.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| 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 | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".