Spine Pre-Operative Planning Using ML for Surgical Approach Prediction
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Solution Overview
Problem
Surgeons face complexity and burden in pre-surgical planning, leading to limited adoption of automated planning systems.
Innovation Solution
An automated system using machine learning models to predict surgical conditions, approaches, and components based on patient data, surgeon preferences, and radiographic images, facilitating an intelligent pre-operative plan generation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual pre-surgical planning workflow is used, then surgeon control and flexibility are maintained, but planning complexity and burden increase significantly
Solution Approach 1:
The system enables self-service by allowing the planning system to automatically generate pre-surgical plans using machine learning models that analyze patient data, imaging, and surgical parameters without requiring manual surgeon input for every parameter, thereby reducing planning burden while maintaining quality
Solution Approach 2:
The patent replaces the manual mechanical workflow of pre-surgical planning with an automated intelligent system using machine learning models and algorithms that process patient data, imaging, and surgical parameters to generate plans, substituting human manual effort with computational automation
2Productivity
If automated planning system is implemented, then planning efficiency and accuracy are improved, but system complexity increases
Solution Approach 1:
The planning system is segmented into multiple specialized machine learning models, each handling specific aspects of pre-surgical planning such as anatomical analysis, surgical approach determination, and parameter optimization, allowing complex planning to be broken down into manageable modular components
Solution Approach 2:
The system achieves universality by designing a multi-functional platform that can handle various surgical procedures, patient types, and planning parameters through a common architecture of machine learning models, reducing the need for separate systems for different surgical scenarios
3Measurement precision
If comprehensive patient data analysis is performed, then prediction accuracy is enhanced, but data processing time increases
Solution Approach 1:
The system performs preliminary action by pre-processing and organizing patient data, imaging, and surgical parameters before the actual planning process, and by using trained machine learning models that have already learned from extensive datasets, enabling rapid accurate analysis during the actual planning phase
Solution Approach 2:
The system applies parameter changes by adjusting the complexity and depth of data analysis based on the specific surgical case requirements, using machine learning models that can dynamically select which parameters to analyze in detail versus those requiring standard analysis, optimizing the balance between accuracy and processing time
Data Source
AI summary
A method and system for automatically generating a pre-operative plan is disclosed that may include using a computing device to receive, from an electronic device associated with a physician, an identification message comprising identifying information associated with a target patient for a surgical procedure. The computing device may retrieve medical information associated with the target patient based on the identifying information, and apply machine learning models to identify a predicted condition of the target patient and to predict a surgical approach and one or more surgical components to use and may generate a surgical plan that comprises an indication of the surgical approach and an indication of the one or more surgical components.