Bone Graft Recommendation Using Osteogenic Capacity Prediction
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Solution Overview
Problem
Surgeons lack awareness of the biochemical properties and efficacy of bone graft materials and implants, leading to suboptimal selection based on personal experience rather than patient-specific characteristics.
Innovation Solution
A machine learning model, such as an artificial neural network, is used to determine optimal bone grafting materials and procedures by analyzing patient data, implant characteristics, and surgical site features to provide personalized recommendations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If surgeons select bone graft materials based on personal experience and general factors, then the selection process is simple and quick, but the accuracy and personalization of material selection deteriorates
Solution Approach 1:
The patent introduces an intermediary system comprising a processor and database that acts as a mediator between the surgeon and bone graft materials. The system processes patient data, implant characteristics, and material properties to generate personalized recommendations, thereby improving selection accuracy without requiring the surgeon to directly analyze complex biochemical properties of multiple materials.
Solution Approach 2:
The patent replaces the mechanical/cognitive process of surgeon-based material selection with an automated computational system. The processor executes algorithms that compare patient-specific data against database information about bone graft materials, substituting human judgment with machine-based analysis to achieve more precise and consistent recommendations.
2Adaptability or versatility
If surgeons use comprehensive patient-specific data and implant characteristics for material selection, then the personalization and accuracy improve, but the time and computational resources required increase
Solution Approach 1:
The patent implements preliminary action by pre-processing and storing extensive bone graft material characteristics, biochemical properties, and clinical data in a database before the actual selection process. When a patient requires bone grafting, the system quickly retrieves and compares relevant pre-organized data, enabling personalized recommendations without time-consuming analysis during the surgical planning phase.
Solution Approach 2:
The patent creates a digital copy or representation of the complex selection process through computational algorithms. The processor generates a recommended bone graft material selection that replicates what would result from comprehensive manual analysis of all patient-specific factors and material properties, but executes much faster through automated data processing and comparison.
Data Source
AI summary
Technology is disclosed for osteogenic capacity prediction and bone graft recommendation systems. In one implementation, a computer-implemented method comprises accessing patient data for a patient and a representation of a location for a spinal implant or bone graft procedure, accessing a value indicative of an osteogenic capacity of the location for the patient comprising an osteogenic capacity score SOG or an equivalent indicator stored in association with the patient data, accessing implant- or biologic-characterization data such as osteoinduction or osteoconduction attributes for a plurality of candidate implants and/or biologics, determining one or more applicable implants and/or biologics for the patient based at least in part on the value indicative of osteogenic capacity and the implant- or biologic-characterization data, and causing presentation via a user interface of recommendation data identifying the one or more applicable implants and/or biologics.