Operating Room Door Status Monitoring via Machine Learning
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Solution Overview
Problem
Current surgical environments lack real-time monitoring and alert systems to maintain aseptic conditions, leading to potential surgical site infections (SSIs) due to uncontrolled door openings in operating rooms, which disrupt temperature, pressure, and air quality.
Innovation Solution
A system utilizing cameras and machine-learning models to monitor the status of operating room doors and sensors to detect environmental deviations, generating alerts for door closure and storing data for post-surgery analysis to prevent SSIs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If door(s) of the operating room are left open for a long-time during surgery, then accessibility and ventilation are improved, but the integrity of the aseptic environment is compromised leading to surgical site infection risk
Solution Approach 1:
The system continuously monitors door status using camera images processed by machine learning models, and provides real-time feedback through alerts when the door is opened during surgery. This closed-loop feedback mechanism enables timely corrective action to maintain aseptic conditions while allowing necessary door access.
Solution Approach 2:
The patent replaces traditional mechanical door position indicators with optical sensing systems (cameras) and computational analysis (machine learning models) to detect door status. This substitution enables more reliable and continuous monitoring without physical contact with the door mechanism.
2Reliability
If real-time monitoring systems are implemented to track door status and environmental parameters, then surgical safety is improved, but system complexity and cost increase
Solution Approach 1:
The system uses a multi-functional integrated approach where camera images serve multiple purposes: door status detection, surgical milestone identification, and environmental monitoring coordination. This consolidation reduces the need for separate specialized sensors and systems, thereby lowering overall complexity while maintaining comprehensive surgical safety monitoring.
Solution Approach 2:
The machine learning model automatically processes camera images to determine door status and surgical milestones without requiring manual input or configuration. The system self-calibrates and adapts to different surgical scenarios, reducing the operational complexity and training requirements for staff.
3Measurement precision
If multiple sensors are deployed to monitor temperature, pressure, humidity, and air quality, then environmental control precision is improved, but device complexity and energy consumption increase
Solution Approach 1:
The patent combines multiple environmental sensors (temperature, pressure, humidity, air quality) into an integrated monitoring system that processes all parameters through a unified alert generation mechanism. This merging approach consolidates data processing and alert management, reducing operational complexity while maintaining comprehensive environmental monitoring precision.
4Reliability
If continuous monitoring and alerting systems are used throughout surgery, then surgical site infection prevention is improved, but energy consumption and operational overhead increase
Solution Approach 1:
The system employs periodic monitoring through continuous image capture and processing at optimized intervals, rather than truly continuous high-frequency analysis. The machine learning model processes images periodically to determine door status and surgical milestones, reducing computational energy consumption while maintaining effective surveillance for SSI prevention.
Data Source
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AI summary
The present disclosure relates generally to improving surgery safety, and more specifically to monitoring various aspects of an operating room. An exemplary method for generating an alert to close a door of an operating room comprises: determining a status of the door of the operating room by: receiving one or more images of the door captured by one or more cameras; inputting the one or more images into a trained machine-learning model to obtain the status of the door, wherein the machine-learning model is trained using training images depicting open or closed doors; receiving one or more signals from one or more sensors in the operating room; determining, based on the one or more signals, whether an alert threshold is reached; and if the alert threshold is reached and the status of the door is open, generating the alert to close the door of the operating room.