Surgical Delay Detection via Video-Based Time-to-Complete Analysis
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Solution Overview
Problem
Existing surgical scheduling systems struggle to accurately detect delays during surgical procedures, leading to inefficiencies and disruptions in operating room schedules.
Innovation Solution
A system and method that utilize video analysis of robotic surgical procedures to estimate the time-to-complete (TTC) and detect deviations from expected durations, providing real-time notifications of delays.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If surgical procedures are scheduled based on historical average durations, then scheduling efficiency is improved, but accuracy of delay detection deteriorates because delays are not made apparent immediately
Solution Approach 1:
The system continuously monitors surgical procedure progress and provides real-time feedback by comparing actual elapsed time against expected time-to-complete predictions. This feedback loop enables dynamic delay detection and notification, allowing scheduling systems to respond to actual procedure progress rather than relying solely on historical averages.
Solution Approach 2:
The patent replaces manual delay detection methods (periodic checks by administrators) with an automated computer vision system that analyzes surgical video feeds. This substitution enables continuous, real-time monitoring and immediate delay detection without requiring human intervention.
2Measurement precision
If real-time video analysis is implemented to detect delays, then delay detection accuracy is improved, but system complexity increases
Solution Approach 1:
The system introduces an intermediary AI model that processes complex video analysis tasks. This intermediary layer translates raw surgical video data into meaningful progress indicators and time-to-complete predictions, simplifying the overall system architecture while maintaining high detection accuracy.
Solution Approach 2:
The system uses computer vision to create a digital representation or 'copy' of the surgical procedure progress by analyzing video frames. This virtual model of procedure progression allows for accurate time-to-complete predictions without requiring direct physical measurement or disruption of the surgical workflow.
3Device complexity
If manual monitoring of surgery status is performed periodically, then system complexity is kept low, but delay detection timeliness deteriorates as delays are only noticed after significant time has passed
Solution Approach 1:
The system implements continuous monitoring of surgical procedures through uninterrupted video analysis, replacing periodic manual checks. This continuous observation enables immediate detection of delays as they occur, minimizing the time loss between delay onset and detection while maintaining relatively simple system architecture.
Data Source
AI summary
Examples of systems and methods for detecting delays during a surgical procedure are disclosed. For example, one disclosed method includes receiving, a computing device, video of a robotic surgical procedure; determining, by the computing device, an estimated time-to-complete (“TTC”) the robotic surgical procedure based on the video; and in response to determining a deviation between the estimated TTC and an expected duration of the robotic surgical procedure exceeds a threshold, outputting an indication that the robotic surgical procedure is deviating from the expected duration.