Feasibility of a touch-free user interface for ultrasound snapshot-guided nephrostomy
Bibliographic record
Abstract
PURPOSE: Clinicians are often required to interact with visualization software during image-guided medical interventions, but sterility requirements forbid the use of traditional keyboard and mouse devices. In this study we attempt to determine the feasibility of using a touch-free interface in a real time procedure by creating a full gesture-based guidance module for ultrasound snapshot-guided percutaneous nephrostomy. METHODS: The workflow for this procedure required a gesture to select between two options, a “back” and “next” gesture, a “reset” gesture, and a way to mark a point on an image. Using an orientation sensor mounted on the hand as input device, gesture recognition software was developed based on hand orientation changes. Five operators were recruited to train the developed gesture recognition software. The participants performed each gesture ten times and placed three points on predefined target positions. They also performed tasks unrelated to the sought-after gestures to evaluate the specificity of the gesture recognition. The orientation sensor measurements and the position of the marked points were recorded. The recorded data sets were used to establish threshold values and optimize the gesture recognition algorithm. RESULTS: For the “back”, “reset” and “select option” gesture, a 100% recognition accuracy was achieved. For the “next” gesture, a 92% recognition accuracy was obtained. With the optimized gesture recognition software no misclassified gestures were observed when testing the individual gestures or when performing actions unrelated to the sought-after gestures. The mean point placement error was 0.55 mm with a standard deviation of 0.30 mm. The mean placement time was 4.8 seconds. CONCLUSION: The system that was developed is promising and demonstrates potential for touch-free interfaces in the operating room.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".