Spinal surgical plan prediction using AI and vertebral relationships
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Solution Overview
Problem
Current methods for predicting spinal surgery outcomes fail to accurately account for multiple pathology risk factors and instrumentation failures, often leading to post-operative complications that may arise months or years after the surgery, and do not provide a comprehensive approach for determining an acceptable surgical plan.
Innovation Solution
The use of predictive modeling, such as machine learning and deep learning, to analyze three-dimensional pre-operative images and clinically relevant data to identify correlations between vertebral pairs and predict the likelihood of pathologies and instrumentation failures, allowing for a processor-based system to calculate overall risks and adjust surgical plans accordingly.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional prediction methods are used for spinal surgery outcomes, then the prediction process is simple, but the prediction accuracy is insufficient and cannot account for multiple pathology risk factors
Solution Approach 1:
The patent segments the prediction task into multiple independent modules: image processing module extracts vertebral parameters, machine learning module analyzes risk factors, and prediction module generates outcomes. Each module handles specific aspects separately, improving overall accuracy while managing complexity through modular design.
Solution Approach 2:
The patent introduces intermediate data structures including vertebral parameter extraction as intermediaries between raw images and final predictions. These intermediaries process and transform data in stages, allowing complex multi-factor analysis without overwhelming the prediction system.
2Reliability
If comprehensive risk factor analysis is performed to predict long-term complications, then the prediction accuracy improves, but the computational time and complexity increase
Solution Approach 1:
The patent performs preliminary extraction of vertebral parameters and pre-processing of medical images before the actual prediction. By preparing data in advance and organizing it into structured formats, the system reduces computational burden during the prediction phase, maintaining high reliability while reducing real-time computational time.
3Adaptability or versatility
If multiple vertebral pairs and relationships are analyzed to determine surgical plan acceptability, then the comprehensiveness of risk assessment improves, but the analysis complexity increases
Solution Approach 1:
The patent creates a universal analysis framework that handles multiple vertebral pairs, different pathology types, and various surgical scenarios through a single integrated system. The machine learning model is designed to process diverse inputs and predict multiple outcome types, providing comprehensive risk assessment without requiring separate analysis systems for each scenario.
Data Source
AI summary
A method for determining an acceptable spinal surgical plan for a subject using pathology prediction, comprising generating a potential spinal surgical plan, obtaining clinically relevant data of the subject, obtaining pre-operative three-dimensional images of a spinal region of the subject, determining relationships between pairs of vertebrae in the images, predicting relationships between pairs of vertebrae that are expected from the surgical plan, accessing a multiple patient database, obtaining sets of data from the database for patients with similar characteristics to the subject, determining risks of pathology types for the subject, using artificial intelligence to combine the determined risks to calculate an overall risk for pathology types for the subject, and if the overall risks are unacceptable, selecting an alternative spinal surgical plan, and if the said overall risks are acceptable, determining that said surgical plan is acceptable.