Operating Room Phase Detection via Video Analysis
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Solution Overview
Problem
Conventional medical facilities face challenges in efficiently scheduling surgical procedures due to limited information about operating room availability and status, leading to increased time between procedures and delays in personnel arrival.
Innovation Solution
A system of multiple image capture devices and sensors within the operating room, communicating with a surgical tracking server, which captures video and audio, and uses computer vision and machine learning models to determine the phase and status of the operating room, including the presence and state of objects and personnel, to provide real-time data on room availability and procedure progress.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If manual updating of surgical procedure progress by personnel in the operating room is used, then personnel can provide information about procedure status, but this causes distraction to personnel during surgery and delays in notification of additional personnel
Solution Approach 1:
The system enables self-service by automatically detecting and tracking surgical procedure status through video analysis and machine learning models. The operating room environment itself generates the data through captured video feeds, eliminating the need for personnel to manually report status. The system autonomously identifies procedure phases, objects, and events, then communicates this information to relevant personnel, resolving both the distraction issue and the delay in notification.
2Loss of information
If conventional operating room monitoring is used, then basic occupancy status can be tracked, but no information is available about when the operating room will be available for use
Solution Approach 1:
The system performs preliminary action by proactively detecting when an operating room is approaching completion of a surgical procedure through real-time video analysis. By identifying procedure phases and predicting imminent completion, the system notifies scheduling personnel in advance, allowing them to prepare for the next procedure. This anticipatory approach provides lead time for resource allocation and reduces idle time between procedures.
Solution Approach 2:
The system implements continuous feedback by monitoring operating room status in real-time through multiple video feeds and machine learning analysis. The system tracks procedure progress, identifies phase transitions, and provides ongoing information about room availability status. This feedback loop enables dynamic scheduling adjustments and optimizes utilization of operating room resources.
3Area of stationary object
If multiple image capture devices are deployed to capture entire operating room, then comprehensive video coverage is achieved, but system complexity increases
Solution Approach 1:
The system merges multiple video feeds from different image capture devices into a unified analysis stream. By combining the data from multiple cameras and processing them through a centralized machine learning system, the solution achieves comprehensive coverage while managing complexity through integration. The unified approach allows the system to detect objects and events across the entire operating room environment without requiring each individual device to be overly complex.
Data Source
AI summary
Multiple image capture devices are located in an operating room to capture video of the entirety of the operating room. A surgical tracking server obtains video of an operating from the multiple image capture devices and identifies objects within frames of the video using one or more computer vision models. The surgical tracking server determines a state of each identified object by applying one or more models to characteristics of the video including the identified objects. The surgical tracking server determines a phase of the operating room from a set of predefined phases using one or more phase classification models and transmits a notification to a client device if the determined phase of the operating room matches the specific phase associated with the user.