Automated Spinal Stenosis Analysis via Machine Learning
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current imaging technologies for spinal analysis, such as X-ray and MRI, lack autonomous and objective analysis methods, leading to subjective interpretations and difficulties in reliably detecting spinal conditions and deformities, especially in tracking patient progress and predicting outcomes.
Innovation Solution
A system and method for analyzing anatomical image data using machine learning models, specifically selecting regions of interest in 3D image data to identify spinal cord, thecal sac, nerve roots, and intervertebral discs, determining parameters and severity of spinal stenosis and disc degeneration, and providing consistent and accurate interpretations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If traditional imaging methods (X-ray, MRI) are used for spinal analysis, then imaging capability is provided, but autonomous and objective analysis is not achieved
Solution Approach 1:
The patent replaces manual physician analysis with an automated machine learning-based system. The machine learning model processes imaging data (X-ray, MRI, CT) to automatically detect spinal conditions, measure parameters, and generate reports, eliminating subjective human interpretation while maintaining high reliability through trained algorithms.
Solution Approach 2:
The system performs self-service by automatically analyzing imaging data without requiring physician intervention for each measurement. The machine learning model independently processes images, identifies spinal structures, measures parameters such as disc height and canal dimensions, and generates diagnostic reports autonomously.
2Productivity
If manual analysis by physicians is performed, then expert interpretation is achieved, but time consumption and subjectivity increase
Solution Approach 1:
The machine learning system operates continuously and rapidly processes imaging data without interruption. The automated pipeline continuously extracts features, measures parameters, and generates reports in real-time, eliminating the variable time consumption associated with manual physician analysis and enabling rapid sequential processing of multiple cases.
3Manufacturing precision
If subjective analysis methods are used, then flexibility in interpretation is maintained, but consistency and reproducibility decrease
Solution Approach 1:
The system transforms subjective visual interpretation into objective quantitative parameters. The machine learning model measures specific parameters such as disc height, canal dimensions, and spinal curvature with precise numerical values, ensuring consistent and reproducible results across different analyses while maintaining the ability to interpret complex spinal anatomy through standardized metric evaluation.
Data Source
AI summary
Embodiments include example systems, methods, and computer-accessible mediums for analysis and visualization of data associated with anatomical images. In some embodiments the anatomical images can be spinal images used for assessment of stenosis and disc degeneration. In some embodiments, systems, devices, and methods described herein include selecting a region of interest (ROI) in a three-dimensional (3D) volume of image data of a plurality of vertebra of a spine, identifying one or more anatomical parts in the ROI, determining one or more parameters of the one or more anatomical parts, and assessing a severity of a spinal deformity based on the one or more parameters.