Real-Time Streaming of Surgery Performance and Intraoperative Imaging Data in the Hybrid Operating Room: Development and Usability Study
Bibliographic record
Abstract
BACKGROUND: The trend of quick evolution and increased digital data in today's operating rooms (ORs) has led to the construction of hybrid ORs. There is often a main control room with monitors for integrating intraoperative data from multiple devices in the hybrid OR. However, there is no adequate solution for communicating the data with people outside the OR. OBJECTIVE: The objective of this study was to design an intelligent operating room (iOR) system, augmented onto the existing information technology (IT) infrastructure of hybrid ORs, to stream surgery performance and intraoperative imaging data. METHODS: In this study, an all-in-one device with synergetic encoder and decoder was used. The device was able to stream multiple sources to one display. The lossless video and images from specific surgical workflows were streamed outside the hybrid OR through network protocols and were further managed by a streaming server and wireless control system. The steps of this study included the following: (1) defining the requirements and feasibility of an iOR system in the hybrid OR, (2) connecting multiple sources, (3) setting up equipment across the hybrid OR and a conference room, (4) designing a video management system, and (5) real-time streaming under specific surgical workflows. RESULTS: The wired streamed video was shown simultaneously on the display in the hybrid OR and the display in the conference room with near-zero latency. Additionally, an interactive video between the hybrid OR and the conference room was achieved through the bidirectional wireless control system. The functions of recording, archiving, and playback were successfully provided by the streaming server. The readily available hardware components and open-access programming reduced the cost required to construct this streaming system. CONCLUSIONS: This flexible and cost-effective iOR system not only provided educational benefits, but also contributed to surgical telementoring.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".