Adaptive Learning for Robotic Arthroplasty Systems
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Solution Overview
Problem
Current robotic arthroplasty systems require significant user input to adjust default views and implant positions, leading to increased procedure time and error opportunities due to fixed default settings.
Innovation Solution
An adaptive system that utilizes machine learning models to learn user preferences and adjust default settings based on historical usage data, including implant position, view selection, and procedure type, to personalize the robotic arthroplasty system configuration.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If fixed default settings are used for views and implant position, then the system configuration is simple and consistent, but the procedure time increases and error opportunities increase due to required user adjustments
Solution Approach 1:
The system performs preliminary actions by automatically configuring default views and implant positions based on historical usage data before the surgeon needs to use them. The machine learning model pre-processes information about the specific arthroplasty procedure and patient anatomy to generate optimized default settings, eliminating the need for surgeons to manually adjust these parameters during the procedure.
Solution Approach 2:
The system serves itself by using the machine learning model to automatically generate and update default settings based on historical data from multiple surgeons. The system learns from its own operational history and continuously improves its default configurations without requiring manual reprogramming or intervention, adapting to individual surgeon preferences automatically.
2Ease of operation
If default settings are customized for each user, then ease of operation improves, but system complexity increases due to multiple user profiles and preferences
Solution Approach 1:
The machine learning model serves multiple functions: it analyzes historical usage data, identifies surgeon preferences, generates optimized default settings, and continuously learns from new data. This single multi-functional component handles all aspects of user adaptation, avoiding the need for separate complex configuration systems for each surgeon while still providing personalized settings.
3Reliability
If manual adjustment of default views and implant position is required, then configuration precision can be optimized for each case, but the quantity of user inputs increases leading to more errors
Solution Approach 1:
The system implements feedback by continuously collecting data on surgeon adjustments and manual inputs, then using this feedback to refine and update default settings for future procedures. The machine learning model learns from each interaction, identifying patterns in how surgeons modify default settings and automatically adjusting those defaults to better match surgeon preferences, thereby reducing the need for manual changes and potential errors.
Data Source
AI summary
The present disclosure describes techniques and systems to adapt an arthroplasty system to particular users based on historical arthroplasty procedures associated with the user. Furthermore, the present disclosure provides that settings for an arthroplasty system associated with a user during multiple arthroplasty procedures can be captures. A ML model can be trained to infer settings for subsequent arthroplasty procedures for the user and the arthroplasty system adapted based on the inferred settings.