An Internet of Things operating room platform for neurosurgery
Bibliographic record
Abstract
OBJECTIVE: The operating room (OR) is a data-rich environment and largely follows closed-door policies for health data security and privacy. To overcome this, the authors have developed a unique sensor-driven, secure, cloud-based scalable data framework enabling real-time acquisition, streaming, and analytics of OR data, accessible to surgeons as feedback and performance reporting. For system validation, this dynamic digital platform was deployed across neurosurgical centers for precise, accurate, and fast analytics of surgical data, establishing an Internet of Things-OR (IoT-OR). METHODS: Through recent deployment of a novel sensorized surgical device called the SmartForceps System, the authors established and validated a data-driven interconnected platform for neurosurgery, the IoT-OR. The system includes sensorized surgical bipolar forceps, allowing quantification of tool-tissue force in real time. Surgical microscope video live-streamed into the software allows a videographic data display time-stamped to tool-tissue interaction, enabling both quantification of surgery and real-time interrogation for feedback and guidance. This IoT platform, with secure data containers by each surgical center and hosted in the cloud, allows data flow and automated analytics through its custom artificial intelligence (AI) model, enriching the model with each new case in perpetuity. The output is a surgeon performance report unique to each procedure and accessible by the surgeon via secure personalized devices and authentication. RESULTS: In more than 250 neurosurgical procedures, spanning 3 neurosurgical units across western Canada (University Alberta Hospital, Edmonton, Alberta; Vancouver General Hospital, Vancouver, British Columbia; and Foothills Medical Centre, Calgary, Alberta, Canada), the system successfully demonstrated that a cloud-driven end-to-end secure platform for surgical procedures can be enabled and operated in real time. Linked to a smart surgical device, built-in intelligent software interface with cloud connectivity, a unique IoT-OR platform has thus been established, with built-in security and scalability to include other data sources (e.g., OR equipment, electronic medical records), multiple centers, and surgeons globally. CONCLUSIONS: The study thus demonstrates the utility of sensors, AI, and cloud interconnectivity in real-time monitoring, analytics, and feedback as a digital footprint of surgery. Using and quantifying closed-door OR data and weaving them into a secure and innovative data-rich pipeline, the system offers a glimpse toward standardization of surgery at the level where the tool meets the tissue.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".