Robotic Surgical Feedback System Using ML Segmentation
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Solution Overview
Problem
Current surgical procedures lack effective methods to determine and improve patient outcomes in real-time during robotic surgeries, as existing technologies fail to provide actionable feedback to surgeons based on historical data and patient-specific information.
Innovation Solution
A system utilizing a robotic surgical device that records and analyzes data from surgical procedures, employing machine learning algorithms to provide real-time recommendations or alerts to surgeons, correlating pre-operative, intra-operative, and post-operative data to optimize surgical techniques and patient outcomes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If real-time data analysis and machine learning algorithms are implemented to provide surgical feedback, then surgical precision and patient outcomes are improved, but device complexity and computational requirements increase
Solution Approach 1:
The system segments surgical data into distinct categories (pre-operative, intra-operative, post-operative) and processes them through separate computational modules. The machine learning model is divided into training phase and inference phase, allowing complex analysis to be broken into manageable segments that reduce overall system complexity while maintaining surgical precision.
Solution Approach 2:
The system performs preliminary data collection and model training before actual surgical procedures. Historical surgical data is pre-processed and used to train machine learning models in advance, so that during surgery, only inference operations are needed, reducing real-time computational complexity while maintaining high surgical precision.
2Measurement precision
If comprehensive historical surgical data is collected and analyzed, then feedback accuracy and surgical outcomes are improved, but data processing time and computational resources increase
Solution Approach 1:
The system performs data preprocessing, feature extraction, and model training in advance, before actual surgical procedures. Historical surgical data is pre-processed and stored in optimized formats, allowing rapid inference during surgery without compromising feedback accuracy.
Solution Approach 2:
The system replaces traditional mechanical data processing methods with machine learning-based predictive models. Once trained, these models can provide accurate surgical feedback through simple inference operations, significantly reducing real-time computational requirements while maintaining high feedback accuracy.
3Reliability
If machine learning models are trained on extensive surgical datasets, then recommendation quality and surgical outcomes are improved, but training time and computational resources increase
Solution Approach 1:
The system performs model training as a preliminary step before clinical deployment. Extensive surgical datasets are used to train machine learning models in advance, allowing the models to achieve high recommendation quality. Once trained, the models can be deployed for real-time surgical assistance without requiring continuous retraining.
Solution Approach 2:
The system implements continuous model improvement through ongoing data collection and periodic retraining. As more surgical data becomes available, the model is periodically retrained to maintain and improve recommendation quality, ensuring continuous enhancement of surgical outcomes.
Data Source
AI summary
A system and method for pre-operative or intra-operative surgical procedure feedback provide a recommendation to a surgeon. A system may include a robotic surgical device to perform a portion of a surgical procedure on a patient, and a processor to determine a recommendation, based on past surgical information, for the portion of the surgical procedure performed by the robotic surgical device or for a next action to be taken, but the robotic surgical device or a surgeon. The system may include outputting the recommendation by intra-operatively providing the recommendation to a surgeon operating the robotic surgical device.