Spinal Implant Suitability Assessment With Virtual Loading
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Solution Overview
Problem
Conventional methods for assessing spinal column integrity and suitability of spinal implants are subjective, time-consuming, and lack objective data for determining the risk of iatrogenic instability and implant effectiveness.
Innovation Solution
A system using image data to virtually simulate the insertion of spinal implants, performing loading simulations to assess suitability based on patient-specific bony anatomy properties, and adjusting parameters for optimal implant placement.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional methods relying on surgeon's experience and judgment are used to assess spinal column integrity, then the assessment can be performed with existing knowledge, but the method is time-consuming, subjective, and lacks objective data
Solution Approach 1:
The patent creates a virtual copy of the patient's spinal anatomy using image data (CT or MRI scans) to build a computational model. This digital twin allows objective simulation and analysis of spinal column integrity without requiring physical manipulation or prolonged clinical assessment, thereby providing precise, objective measurements while reducing time consumption.
Solution Approach 2:
The patent replaces subjective human judgment and manual assessment methods with computational mechanics simulations. By using finite element analysis and other mechanical modeling techniques on the virtual spinal model, the system objectively quantifies spinal column integrity, implant suitability, and iatrogenic instability risk, eliminating the time-consuming and subjective nature of conventional surgeon-based assessments.
2Object-affected harmful factors
If spinal decompression procedures are performed to relieve spinal compression, then neural element compression is reduced, but the integrity and stability of the spinal column may be compromised
Solution Approach 1:
The patent performs preliminary computational simulations before the actual decompression surgery. The system evaluates multiple surgical scenarios in silico, predicting how different decompression approaches will affect both neural element relief and spinal column stability. This allows surgeons to plan procedures that maximize decompression benefits while minimizing stability compromise through informed decision-making based on simulation results.
Solution Approach 2:
The patent implements a feedback mechanism where the computational model provides quantitative predictions about spinal stability following decompression. These predictions feed back into the surgical planning process, allowing adjustment of the decompression strategy to maintain spinal column integrity while achieving neural element relief, thus balancing the conflicting objectives.
3Stability of the object's composition
If spinal fusion or implant procedures are used to improve spinal column integrity, then spinal stability is enhanced, but the risk of iatrogenic instability and implant failure remains
Solution Approach 1:
The patent utilizes parameter changes in the computational model to evaluate how different implant characteristics (size, material, placement trajectory, fixation method) affect both spinal stability and implant reliability. By systematically varying these parameters in simulations, the system identifies optimal implant configurations that maximize stability enhancement while minimizing the risk of iatrogenic instability and implant failure for each patient's specific anatomy.
Data Source
AI summary
A system for assessing spinal implants includes at least one processor, and a memory storing instructions for execution by the at least one processor that, when executed, cause the at least one processor to receive first image data of a spinal column of a patient, determine, based on the first image data, a property of first bony anatomy in at least a first portion of the spinal column, determine, based on the property of the first bony anatomy, an initial screw trajectory for inserting a screw into the spinal column, virtually insert the screw along the initial screw trajectory using the first image data to generate modified first image data, perform an initial loading simulation for the virtually inserted screw using the modified first image data, and determine, based on the initial loading simulation, a suitability of the initial screw trajectory for implanting the screw into the spinal column.