Machine-Learned Models for Surgical Implant Selection
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Solution Overview
Problem
Current surgical joint repair procedures face challenges in selecting appropriately sized and shaped prosthetics and positioning them optimally, leading to suboptimal surgical outcomes, especially for less experienced surgeons and designers.
Innovation Solution
The use of machine learning technologies to determine the operational duration of orthopedic implants and their dimensions, enabling preoperative planning and intraoperative guidance through mixed reality visualization, allowing for more informed implant selection and positioning.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If machine learning models are used to predict implant operational duration, then implant selection accuracy is improved, but system complexity increases
Solution Approach 1:
A machine learning model serves as an intermediary between implant characteristics and surgical outcomes. The model processes input features (implant properties, patient characteristics) and outputs predicted operational duration, enabling data-driven implant selection without requiring direct complex analysis of all possible outcome factors.
Solution Approach 2:
Traditional mechanical decision-making by surgeons based on experience and intuition is replaced with an automated machine learning system. The computational model performs the analysis and prediction, substituting human expertise with an algorithmic approach that can process complex data relationships more efficiently.
2Manufacturing precision
If mixed reality visualization is used for intraoperative guidance, then surgical planning quality is improved, but device complexity increases
Solution Approach 1:
The system transitions from traditional two-dimensional surgical planning documents to three-dimensional mixed reality visualization. Virtual implant models are rendered in 3D space and overlaid with real-world anatomical structures, providing spatial context and depth information that enhances surgical decision-making.
Solution Approach 2:
Virtual copies of implant models are created and displayed in the mixed reality environment. These digital replicas allow surgeons to interact with and manipulate 3D representations of the actual implants before surgery, enabling better visualization of fit and positioning without physical handling of the real implants.
Data Source
AI summary
The disclosure describes examples of machine-learned model based techniques. A computing system may obtain patient characteristics of a patient and implant characteristics of an implant. The computing system may determine information indicative of an operational duration of the implant based on the patient characteristics and the implant characteristics and output the information indicative of the operational duration of the implant. In some examples, one or more processors may be configured to receive, with a machine-learned model of the computing system, implant characteristics of an implant to be manufactured, apply model parameters of the machine-learned model to the implant characteristics, determine information indicative of dimensions of the implant to be manufactured based on the applying of the model parameters of the machine-learned model, and output the information indicative of the dimensions of the implant to be manufactured.