Spinal Surgery Outcome Prediction Using Image Analysis
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current methods for predicting outcomes in spinal surgery are subjective and dependent on the experience of medical doctors, leading to inaccuracies in choosing the best surgical techniques and parameters for patients with spinal curvature.
Innovation Solution
A system and method that utilize imaging devices and processors to capture and analyze 2D images of spines, generating curves based on select vertebrae locations, grouping spines by similarity, and assigning surgical methods and probabilities to post-operative groups for predicting spinal surgery outcomes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If doctors use subjective observation and experience to predict surgical outcomes, then the process is simple and quick, but prediction accuracy deteriorates due to variability in doctor expertise
Solution Approach 1:
The patent replaces the mechanical system of doctor-based subjective assessment with an automated image processing system. The system captures spinal images, extracts vertebral locations, generates spinal curves, and predicts surgical outcomes objectively, eliminating the variability inherent in human expertise while maintaining operational simplicity through automation.
Solution Approach 2:
The system creates a digital representation (copy) of the patient's spine by extracting vertebral locations from images and generating a mathematical curve model. This digital copy is then analyzed to predict outcomes, allowing repeated, consistent measurements without the variability of human observers.
2Measurement precision
If doctors rely on their experience and judgment, then the process requires minimal equipment, but measurement precision deteriorates due to inter-observer variability
Solution Approach 1:
The system performs self-service by automatically extracting vertebral locations, generating spinal curves, and predicting outcomes without requiring doctor intervention. The automated process handles all data processing and analysis, eliminating the need for manual measurement and subjective judgment while maintaining high precision through consistent algorithmic application.
3Measurement precision
If automated image processing is used to analyze spinal curves, then prediction accuracy improves through objective analysis, but device complexity increases due to imaging equipment and processing systems
Solution Approach 1:
The system segments the complex task of spinal analysis into distinct steps: capturing spinal images, extracting vertebral locations, generating spinal curves, and predicting outcomes. This segmentation allows each component to be optimized independently and simplifies the overall system architecture while maintaining high measurement precision through specialized processing at each stage.
Data Source
AI summary
A spinal surgery training process includes the steps of capturing a plurality of 2D images for each of a plurality of spines, generating a curve of each spine from the respective 2D images based on locations of select vertebrae in each of the spines, grouping the spines into one of a number of groups based on similarity to produce groups of spines having similarities, performing the capturing, generating, determining and grouping steps at least once prior to surgery and at least once after surgery to produce pre-operative groups and their resultant post-operative groups, and assigning surgical methods and a probability to each of the post-operative groups indicating the probability that a spinal shape of the post-operative group can be achieved using the surgical methods. An outcome prediction process for determining surgical methods can be implemented once the training process is complete.