Using a depth-sensing infrared camera system to access and manipulate medical imaging from within the sterile operating field
Bibliographic record
Abstract
BACKGROUND: As surgical procedures become increasingly dependent on equipment and imaging, the need for sterile members of the surgical team to have unimpeded access to the nonsterile technology in their operating room (OR) is of growing importance. To our knowledge, our team is the first to use an inexpensive infrared depthsensing camera (a component of the Microsoft Kinect) and software developed inhouse to give surgeons a touchless, gestural interface with which to navigate their picture archiving and communication systems intraoperatively. METHODS: The system was designed and developed with feedback from surgeons and OR personnel and with consideration of the principles of aseptic technique and gestural controls in mind. Simulation was used for basic validation before trialing in a pilot series of 6 hepatobiliary-pancreatic surgeries. RESULTS: The interface was used extensively in 2 laparoscopic and 4 open procedures. Surgeons primarily used the system for anatomic correlation, real-time comparison of intraoperative ultrasound with preoperative computed tomography and magnetic resonance imaging scans and for teaching residents and fellows. CONCLUSION: The system worked well in a wide range of lighting conditions and procedures. It led to a perceived increase in the use of intraoperative image consultation. Further research should be focused on investigating the usefulness of touchless gestural interfaces in different types of surgical procedures and its effects on operative time.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".