Surgical Feedback System Using Cue Sheet Segmentation
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Solution Overview
Problem
Current surgical processes lack efficient methods to optimize procedures by minimizing unnecessary steps and providing personalized feedback to surgeons, relying on pre-captured medical images or expert advice that is not tailored to specific patients.
Innovation Solution
A method that divides actual surgical data into detailed operations, compares them with reference data, and provides feedback on unnecessary, missing, or incorrect steps using standardized names and codes, incorporating reinforcement learning to optimize surgical processes and detect errors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If medical images or advice from skilled surgeons are used to optimize surgical processes, then surgical guidance is provided, but the guidance is not customized to specific patients and cannot determine unnecessary processes
Solution Approach 1:
The surgical process is segmented into multiple granular steps, each with standardized names and codes. This segmentation enables detailed analysis of each surgical step against reference data, allowing identification of patient-specific variations and unnecessary processes while maintaining adaptability to individual patient needs.
Solution Approach 2:
The system provides feedback by comparing actual surgical data with reference cue sheet data, identifying deviations, and generating personalized optimization suggestions. This feedback mechanism enables continuous improvement of surgical processes tailored to each patient's specific conditions and requirements.
2Productivity
If detailed analysis of surgical processes is performed to identify unnecessary steps, then surgical optimization is achieved, but processing time and computational resources increase
Solution Approach 1:
Reference cue sheet data is prepared in advance containing optimized surgical processes for various conditions. This preliminary preparation allows rapid comparison during actual surgery analysis, reducing real-time processing requirements while maintaining high productivity through pre-computed reference standards.
Solution Approach 2:
The system transforms complex surgical process data into standardized parameters (names and codes) that enable efficient comparison and analysis. This parameter standardization reduces computational complexity and processing time while maintaining the ability to perform detailed surgical optimization.
3Measurement precision
If surgical data is divided into multiple detailed operations for analysis, then surgical feedback precision is improved, but data complexity and processing difficulty increase
Solution Approach 1:
Each surgical operation is assigned localized standardized names and codes that capture its specific characteristics. This local quality approach enables precise analysis of individual operations while maintaining a simple overall data structure through standardization, avoiding the complexity of custom analysis frameworks.
Data Source
AI summary
A method for providing a feedback on a surgical outcome by a computer includes dividing, by the computer, actual surgical data obtained in an actual surgical process into a plurality of detailed surgical operations to obtain actual surgical cue sheet data composed of the plurality of detailed surgical operations, obtaining, by the computer, reference cue sheet data about the actual surgery, and comparing, by the computer, the actual surgical cue sheet data with the reference cue sheet data, and providing, by the computer, the feedback based on the comparison result.