Cascade Machine Learning for Orthopedic Implant Component Selection
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Solution Overview
Problem
Selecting appropriately sized and shaped implant components for orthopedic joint repair surgeries is challenging, particularly for less experienced surgeons, leading to potential surgical failures and the need for revision surgeries.
Innovation Solution
A computing system implements a cascade of machine learning models, including artificial neural networks, to recommend implant components by determining patient pathology, surgery type, and specific implant components based on anatomic parameters, improving accuracy and training efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional manual methods are used for implant component selection, then surgeon experience and judgment are required, but accuracy and consistency of implant selection deteriorate, especially for less experienced surgeons
Solution Approach 1:
The patent replaces the mechanical system of manual implant selection (relying on surgeon experience and judgment) with an automated machine learning system that processes patient data and recommends implant components. This substitution eliminates the variability associated with human expertise levels and provides consistent, data-driven recommendations across all surgeons.
Solution Approach 2:
The machine learning system enables the implant selection process to serve itself by automatically analyzing patient-specific anatomical data, pathology, and surgical requirements to generate implant recommendations without requiring external expert judgment. The system learns from training data and independently makes selections based on learned patterns.
2Device complexity
If a single machine learning model is used for implant recommendation, then the system is simpler, but accuracy and training efficiency deteriorate due to the complexity of multiple decision factors
Solution Approach 1:
The patent divides the complex implant recommendation task into multiple specialized machine learning models, each trained to handle specific aspects of the decision-making process. This segmentation allows each model to focus on particular features or decision criteria, improving overall accuracy while maintaining manageable system complexity through modular architecture.
Data Source
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AI summary
A computing system applies a first machine learning model to determine a suggested pathology. An input vector of the first machine learning model includes a set of input data, the set of input data including anatomic parameters of the patient. Additionally, the computing system applies a second machine learning model to determine a suggested surgery. An input vector of the second machine learning model includes an element that indicates the suggested pathology. The computing system also applies a third machine learning model to determine the suggested implant component to implant into the patient during the orthopedic surgery. An input vector of the third machine learning model includes an element that indicates the suggested pathology and an element that indicates the suggested surgery. The computing system may also output an indication of the suggested implant component.