Surgical Step Recognition System for Reducing Surgeon Distraction
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Solution Overview
Problem
Surgeons face distractions during robotic surgery due to the need to access multiple sources of information, leading to potential errors and inefficiencies in the surgical process.
Innovation Solution
A system that uses real-time machine learning to recognize the current surgical step and provide intraoperative cues, such as visual and audio guidance, to the surgeon, reducing the need for manual information retrieval and minimizing distractions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If multiple sources of information are provided to the surgeon during surgery, then the surgeon has access to comprehensive surgical data, but the surgeon becomes distracted and experiences inattention blindness
Solution Approach 1:
The system segments surgical information delivery by identifying specific surgical steps through machine learning analysis of video feeds and providing targeted cues only at relevant moments. Instead of presenting all information simultaneously, the system divides information delivery into discrete, context-appropriate segments that match the current surgical step, thereby maintaining surgical judgment while ensuring access to necessary information.
Solution Approach 2:
The system introduces an intermediary layer (machine learning model and processing system) between the raw surgical data and the surgeon. This intermediary automatically processes video feeds, identifies surgical steps, and filters information to provide only relevant cues, eliminating the need for the surgeon to manually access multiple information sources while preventing information overload.
2Productivity
If traditional surgical procedures are performed without automation, then the surgeon maintains full control, but the surgical process is time-consuming and prone to human error
Solution Approach 1:
The system implements feedback by continuously analyzing video feeds from the surgical procedure, comparing observed actions against known surgical step patterns, and providing real-time identification of current surgical steps. This feedback loop enables automated assistance that improves efficiency and precision without removing surgeon control, as the system learns and adapts to the specific surgical workflow.
Solution Approach 2:
The system enables self-service by automatically performing the function of surgical step identification and information retrieval without requiring surgeon intervention. The machine learning model autonomously analyzes video data, determines current surgical steps, and provides appropriate cues, freeing the surgeon to focus on the surgical task while maintaining accuracy and efficiency.
3Loss of information
If overlays are used to provide information to the surgeon, then all relevant information is displayed at once, but the surgeon experiences distraction and inattention blindness
Solution Approach 1:
The system applies dynamics by making information display adaptive rather than static. Instead of showing all information through constant overlays, the system dynamically adjusts information presentation based on the identified surgical step, displaying only relevant cues at appropriate times. This dynamic approach maintains surgical focus while ensuring information availability when needed.
Solution Approach 2:
The system uses periodic action by providing information cues at specific intervals corresponding to surgical step transitions rather than continuously. The machine learning model periodically identifies surgical steps and triggers appropriate information delivery only at these discrete moments, preventing constant distraction while maintaining information accessibility.
Data Source
AI summary
A system for robot-assisted surgery includes an image sensor and a display. The system further includes a controller coupled to the image sensor and the display, where the controller includes logic that when executed by the controller causes the system to perform operations. The operations may include acquiring first images of a surgical procedure with the image sensor, and analyzing the first images with the controller to identify a surgical step in the surgical procedure. The operations may further include displaying second images on the display in response to identifying the surgical step; the second images may include at least one of a diagram of human anatomy, a preoperative image, an intraoperative image, or an annotated image of one of the first images.