CNN-Based Three-Dimensional Anatomical Image Analysis for Spinal Levels
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Solution Overview
Problem
Current image analysis methods for spinal anatomy, such as X-ray and CT scans, lack autonomy and reliability, leading to subjective measurements, increased radiation exposure, and difficulty in comparing patient images over time, which complicates surgical planning and navigation.
Innovation Solution
Implementing machine learning models, particularly convolutional neural networks (CNNs), to process and analyze anatomical images, enabling objective identification of spinal levels and anatomical components, and generating visual representations for surgical guidance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional X-ray and CT imaging methods are used to acquire spinal anatomy information, then anatomical image data can be obtained, but patients are subjected to high levels of radiation exposure
Solution Approach 1:
The patent uses machine learning models to create a digital copy or representation of spinal anatomy from limited imaging data, allowing comprehensive analysis without requiring multiple high-radiation imaging sessions. The model learns from training data to generate accurate anatomical representations that reduce the need for repeated CT scans.
Solution Approach 2:
The system changes the parameters of image analysis by using deep learning algorithms that can extract meaningful information from lower-quality or lower-dose images. The model transforms the relationship between radiation dose and image quality by enabling reliable analysis even when radiation exposure is minimized.
2Reliability
If traditional X-ray analysis methods are used, then image interpretation can be performed, but the analysis is subjective and time-consuming
Solution Approach 1:
The system implements autonomous analysis where the machine learning model automatically performs spinal level identification and anatomical structure analysis without requiring manual measurement or interpretation. The model serves itself by learning from training data and applying that knowledge to new cases, eliminating the need for time-consuming manual analysis while ensuring consistent, objective results.
Solution Approach 2:
The patent replaces the mechanical process of manual measurement and visual inspection with an automated computational system. Deep learning algorithms substitute for the human expert's manual analysis, providing rapid, consistent, and reproducible measurements without the subjectivity and time requirements of traditional methods.
3Extent of automation
If manual measurement and analysis of X-rays are performed, then necessary measurements can be obtained, but the process is subject to user error and lacks autonomy
Solution Approach 1:
The machine learning model performs autonomous analysis by automatically identifying spinal levels, vertebrae, and anatomical structures without human intervention. The system learns from labeled training data and independently applies this knowledge to new images, eliminating user error while maintaining high accuracy through continuous validation and refinement.
Solution Approach 2:
The system incorporates feedback mechanisms where the model's predictions are validated against ground truth data during training, and performance is continuously monitored and improved. This feedback loop ensures that autonomous analysis maintains high reliability by learning from errors and refining its measurements over time.
4Reliability
If traditional imaging analysis is used, then current patient images can be examined, but comparing images over time to track progress is difficult
Solution Approach 1:
The system creates standardized digital representations of spinal anatomy at different time points using machine learning models. These consistent digital copies enable reliable comparison over time by eliminating variability in manual measurement techniques, allowing accurate tracking of spinal changes and treatment progress through longitudinal analysis.
Data Source
AI summary
Embodiments include exemplary systems. methods. and computer-accessible medium for analysis of anatomical images and identification of anatomical components and/or structures. In some embodiments. systems. devices. and methods described herein relate to identification of levels of a spine and other anatomical components associated with those levels.