Spine Analysis System Using CNN for Objective Stenosis Assessment
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Solution Overview
Problem
Current imaging technologies for spinal assessment, such as X-ray and MRI, lack autonomous and reliable methods for objective analysis, leading to subjective interpretations and inconsistent comparisons, which hinder accurate detection and tracking of spinal conditions and deformities.
Innovation Solution
Implementing machine learning models, particularly convolutional neural networks (CNNs), to analyze anatomical image data, identify spinal structures, and determine parameters for conditions like spinal stenosis and disc degeneration, providing objective and consistent interpretations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional X-ray and CT imaging methods are used for spinal assessment, then anatomical information can be acquired, but the analysis is subjective and inconsistent due to reliance on physician training and experience
Solution Approach 1:
The patent replaces the mechanical system of manual physician analysis with an automated image processing system that uses algorithms to objectively measure spinal parameters. The system automatically detects vertebral bodies, calculates Cobb angles, and assesses spinal deformities without human intervention, thereby eliminating subjectivity and improving measurement consistency.
Solution Approach 2:
The imaging system performs self-analysis by automatically processing the acquired images to generate diagnostic measurements. The system independently identifies anatomical structures, computes spinal parameters, and produces assessment results without requiring external physician interpretation, enabling autonomous and reproducible analysis.
2Productivity
If manual measurement and analysis of X-rays is performed, then spinal parameters can be obtained, but the process is time-consuming and subject to user error
Solution Approach 1:
The system performs preliminary automated processing of spinal images immediately after acquisition, pre-calculating key parameters such as vertebral alignment, Cobb angles, and deformity measurements. This preliminary automated analysis eliminates the need for time-consuming manual measurements and reduces the risk of user error in subsequent diagnostic steps.
3Reliability
If physician expertise is relied upon for X-ray analysis, then interpretation can be performed, but objective comparison of patient progress over time is difficult
Solution Approach 1:
The patent replaces subjective physician interpretation with an automated measurement system that consistently applies the same analytical algorithms to all patient images. This substitution ensures that spinal parameters are measured objectively and reproducibly across different time points, enabling reliable comparison of patient progress without variation introduced by different physicians or changing clinical judgment.
Data Source
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AI summary
Embodiments include example systems, methods, and computer-accessible mediums for analysis of anatomical images 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.