Automated Vertebral Body Mapping for Surgical Planning
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Solution Overview
Problem
Current methods for vertebral body recognition in spinal imaging are prone to high variability and error due to manual processes and uncontrolled bending, leading to inaccurate measurement of joint motion and treatment outcomes.
Innovation Solution
The development of systems and methods using a computer application to map vertebral bodies in images with two or four points, create a prediction model through epochs and steps, and build an automated markup for improved vertebral recognition and surgical planning.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual processes are used for vertebral body recognition, then flexibility and adaptability are maintained, but measurement precision and reliability deteriorate due to high variability and error
Solution Approach 1:
The system enables automated vertebral body recognition that performs self-service by automatically mapping vertebral bodies in imaging data without requiring manual intervention. The prediction model autonomously identifies and maps vertebral structures, eliminating human operator variability while maintaining consistent measurement precision across different cases.
Solution Approach 2:
The patent replaces manual mechanical processes with an automated computational system. Instead of manual vertebral body mapping, the system uses a prediction model with automated mark-up functionality that processes imaging data algorithmically, substituting human manual operations with automated computational mechanisms to improve precision and reliability.
2Productivity
If automated mark-up systems are implemented, then productivity and measurement consistency improve, but device complexity and initial setup requirements increase
Solution Approach 1:
The system performs preliminary action by pre-training the prediction model with extensive imaging data before actual vertebral body recognition tasks. The automated mark-up system is pre-configured with mapping algorithms and vertebral body identification rules, enabling rapid processing of new imaging data without requiring complex setup for each individual case, thus improving productivity while managing complexity through advance preparation.
3Measurement precision
If multiple mapping points (two or four points) are used for vertebral body identification, then measurement precision improves, but the complexity of the recognition process increases
Solution Approach 1:
The system applies dynamics by adaptively selecting between two-point and four-point mapping approaches based on the specific vertebral body characteristics and imaging quality. The prediction model dynamically adjusts the mapping complexity for each vertebral body, using two points when sufficient and four points when greater precision is needed, optimizing the balance between measurement precision and process complexity on a case-by-case basis.
Data Source
AI summary
A system and method that includes storing a software application on a memory associated with a computer, which when executed by a processor causes the software application to develop a model of at least a portion of a spine, process images of the spine, recognize the vertebral bodies in the image, map the vertebral bodies, and display the images of the spine on a user interface associated with the computer. The mapped images can be use to develop a prediction model and/or a surgical plan.