Intraoperative Surgical Guidance for Patient-Specific Flow Deviations
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Computer-assisted surgery systems often follow standardized surgical flows that do not fully adapt to the unique anatomical variations of individual patients, leading to potential deviations from the optimal surgical procedure.
Innovation Solution
A surgical assistance system that utilizes a machine learning module to analyze video feeds of surgical procedures, detect deviations from a standard surgical flow, and provide real-time recommendations to surgeons, incorporating patient-specific data such as age, gender, and medical history to adjust the surgical plan.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Stability of the object's composition
If standardized surgical flows are used, then procedural consistency is improved, but adaptability to individual patient anatomy deteriorates
Solution Approach 1:
The surgical flow system transitions from static standardized protocols to dynamic adaptive workflows. The machine learning module continuously monitors surgical progress and automatically adjusts the surgical flow in real-time based on detected anatomical variations and surgical conditions, allowing the system to maintain procedural consistency while adapting to individual patient needs.
Solution Approach 2:
The system changes key parameters of the surgical flow based on patient-specific data and intraoperative conditions. The machine learning module analyzes patient anatomy, surgical progress, and outcome data to dynamically modify surgical parameters such as approach angles, incision locations, and tool selection, thereby resolving the contradiction between standardized procedures and individualized care.
2Measurement precision
If machine learning analysis is added to detect deviations, then surgical precision is improved, but system complexity increases
Solution Approach 1:
The machine learning module is trained on historical surgical data and automatically performs deviation detection without requiring manual configuration or intervention. The system self-learns from past procedures, automatically adapting to new surgical patterns and patient variations, thereby achieving high precision while minimizing the operational complexity for surgeons.
Solution Approach 2:
The system implements continuous feedback loops where the machine learning module monitors surgical progress, compares it against standardized flows and historical data, and provides real-time recommendations. This feedback mechanism enables precise deviation detection while maintaining manageable system complexity through automated decision-support rather than requiring complex manual control systems.
3Reliability
If real-time recommendations are provided, then surgical outcomes are improved, but processing time increases
Solution Approach 1:
The machine learning module performs preliminary analysis of patient anatomy and surgical plan before the procedure begins, pre-calculating potential deviations and optimal adjustments. During surgery, the system only needs to compare real-time data against pre-computed recommendations, significantly reducing processing time while maintaining high outcome reliability.
Solution Approach 2:
The system processes surgical data continuously throughout the procedure without interrupting the surgical flow. The machine learning module operates in parallel with the surgical procedure, continuously analyzing video feeds and sensor data to generate real-time recommendations, thereby improving outcomes without adding significant processing delays to the surgical timeline.
Data Source
AI summary
A surgical assistance system includes a processing unit; and a non-transitory computer-readable memory communicatively coupled to the processing unit and comprising computer-readable program instructions executable by the processing unit for: obtaining a video feed of a surgical procedure and monitoring the surgical procedure from the video feed; detecting, from an image processing of the video feed, a condition requiring a deviation from the surgical procedure, the deviation being defined as being outside of a standard surgical flow; and outputting a recommendation of deviation by intra-operatively providing the recommendation to an operator of the surgical procedure.