Self-learning surgical engine for phacoemulsification settings
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Solution Overview
Problem
Current surgical systems for phacoemulsification, such as those used in cataract surgery, often require time-consuming and expensive customization by skilled technical specialists to optimize settings for individual surgeons, leading to inefficiencies and inconsistencies due to the reliance on default settings and limited availability of specialists.
Innovation Solution
A self-learning system that monitors and analyzes surgical performance in real-time, recommending personalized settings and adjustments based on surgeon preferences and surgical circumstances, using filters and algorithms to tailor settings for specific procedures and surgeons.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If default settings are used for surgical systems, then the system is easy to operate without specialized knowledge, but the surgical efficiency and effectiveness are not optimized
Solution Approach 1:
The system automatically monitors surgical performance data and adjusts settings without requiring external technical specialists. The surgical system serves itself by analyzing its own performance metrics and autonomously optimizing parameters for individual surgeons, eliminating the need for manual customization while maintaining high surgical efficiency.
Solution Approach 2:
The system implements a closed-loop feedback mechanism where surgical performance data is continuously collected, analyzed, and used to adjust settings. Performance metrics are fed back into the system to automatically refine operational parameters, enabling continuous optimization without external intervention and maintaining both ease of operation and surgical efficiency.
2Productivity
If technical specialists customize settings for surgeons, then surgical performance is optimized, but the process is time-consuming and expensive
Solution Approach 1:
The surgical system performs self-customization by automatically analyzing surgical performance data and adjusting settings for individual surgeons. This eliminates the time-consuming process of manual customization by technical specialists while maintaining optimized surgical performance, as the system autonomously adapts to each surgeon's techniques and preferences.
Solution Approach 2:
The system pre-configures optimized settings by analyzing performance data from multiple surgeries and proactively adjusting parameters before new surgical procedures begin. This preliminary optimization occurs automatically in the background, eliminating the need for time-consuming pre-surgery customization sessions with technical specialists.
3Productivity
If technical specialists customize settings, then surgical performance is optimized, but consistency is reduced due to different specialist approaches
Solution Approach 1:
The system eliminates human variability by having the surgical system itself perform the customization rather than different technical specialists. This automated self-service approach ensures consistent optimization algorithms are applied uniformly across all surgeons and procedures, eliminating the inconsistency that arises from different specialists having different approaches to setting optimization.
Solution Approach 2:
The system uses standardized parameter adjustment algorithms that consistently modify surgical settings based on objective performance data. By relying on fixed mathematical relationships and data-driven parameter changes rather than subjective specialist judgment, the system ensures reproducible and consistent optimization across different surgeons and time periods.
4Ease of operation
If default settings are used, then the system is simple to operate, but inappropriate parameter settings lead to suboptimal performance
Solution Approach 1:
The surgical system automatically monitors performance metrics and adjusts parameters to maintain optimal settings without requiring surgeon expertise. This self-service capability ensures reliable performance by continuously adapting to actual surgical conditions while keeping the interface simple and easy to operate, as no specialized knowledge is needed to manage the automated optimization.
Solution Approach 2:
The system implements real-time feedback loops where performance data is continuously monitored and used to adjust settings. This feedback mechanism ensures reliable performance by detecting deviations from optimal parameters and automatically correcting them, maintaining high performance reliability while keeping the system easy to operate through automated control.
Data Source
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AI summary
The present invention pertains to a system (or engine) that monitors a system's performance during a surgery, analyzes that performance, and makes recommendations to the user/surgeon for changes in his settings and/or programs that will result in more effective and time-efficient surgeries. Further, the system may comprise one or more components, including, but not limited to, a user preference filter, a surgical circumstances filter, a surgical instrument, a real time data collection module, and an analysis module.