Surgical Guiding System for Tool Selection Accuracy
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Solution Overview
Problem
During laparoscopic surgical procedures, surgeons face challenges in selecting and positioning appropriate surgical tools due to limited visibility, leading to sub-optimal treatment outcomes, as existing systems lack effective guidance for tool selection and positioning.
Innovation Solution
A surgical guiding system utilizing a machine learning system that accesses information from similar procedures, identifies tools in real-time endoscope images, determines if tools need to be changed, re-oriented, or re-positioned, and provides alerts to the surgeon, leveraging stereo-endoscope images with depth information and training data from previous surgeries.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a surgeon relies on personal judgment and experience for tool selection, then the surgical procedure can proceed without additional systems, but the tool selection may be sub-optimal due to limited scope of view
Solution Approach 1:
A machine learning system is introduced as an intermediary between the surgeon and the tool selection process. The system receives images from the endoscope, identifies tools and tissue, determines appropriate tools based on surgical procedure information, and provides recommendations to the surgeon. This intermediary enhances tool selection accuracy by leveraging AI analysis without requiring the surgeon to directly observe all surgical details.
Solution Approach 2:
The system provides feedback to the surgeon by analyzing endoscope images and surgical procedure information, then recommending appropriate tools and positioning. The feedback loop continues as the system monitors tool usage and adjusts recommendations based on real-time images and procedural context, improving tool selection accuracy through continuous data-driven guidance.
2Manufacturing precision
If the surgeon has a limited scope of view through the endoscope display, then the surgical site visualization is constrained, but this limitation leads to sub-optimal treatment outcomes
Solution Approach 1:
The mechanical limitation of direct visual observation is replaced by an AI-based information processing system. Instead of relying solely on the surgeon's direct view through the endoscope, the system uses machine learning models to analyze images, identify tools and tissue structures, and provide comprehensive surgical guidance. This substitution of mechanical visualization with AI analysis expands the effective scope of view and preserves critical surgical site information.
Solution Approach 2:
The system adds a computational dimension to surgical visualization by processing endoscope images through machine learning algorithms. This dimensional transformation converts raw visual data into structured information about tool identification, tissue characterization, and procedural guidance, effectively expanding the information space beyond what is directly visible on the display.
3Productivity
If inappropriate tools are used or tools are not appropriately positioned, then the surgical procedure can continue without interruption, but the treatment results become sub-optimal
Solution Approach 1:
The system continuously monitors tool usage and positioning through real-time image analysis and provides feedback recommendations. When inappropriate tools are detected or positioning is sub-optimal, the system alerts the surgeon with data-driven guidance, enabling corrective action that maintains surgical efficiency while improving treatment outcomes through continuous quality improvement.
Solution Approach 2:
The machine learning system autonomously analyzes surgical images, identifies tools and tissue structures, determines appropriate interventions, and generates recommendations without requiring surgeon intervention. This self-service capability allows the system to independently monitor and improve surgical quality, maintaining productivity while enhancing reliability through automated data-driven oversight.
Data Source
AI summary
Methods for guiding a surgical procedure include accessing information relating to a surgical procedure, accessing at least one image of a surgical site captured by an endoscope during the surgical procedure, identifying a tool captured in the at least one image by a machine learning system, determining whether the tool should be changed based on comparing the information relating to the surgical procedure and the tool identified by the machine learning system, and providing an indication when the determining indicates that the tool should be changed.