Automated Spinal Surgical Planning System
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Solution Overview
Problem
Current spinal surgery techniques lack automation and precision in developing patient-specific surgical strategies, implant positioning, and compensatory mechanism simulations, leading to variable surgical outcomes.
Innovation Solution
The development of systems, methods, and devices that automate the generation of patient-specific spinal surgical strategies, including the definition of implant positions, surgical gestures, and compensatory mechanisms, using computer-implemented methods and predictive modeling.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If current manual spinal surgery techniques are used, then surgical flexibility and adaptability are maintained, but automation and precision in developing patient-specific surgical strategies are lacking
Solution Approach 1:
The system performs preliminary actions by automatically generating patient-specific surgical strategies, implant positions, and compensatory mechanism simulations before surgery. This includes pre-calculating surgical outcomes and creating detailed planning documents that guide the actual surgical procedure, thereby reducing intraoperative decision-making complexity.
Solution Approach 2:
The system creates a digital copy or virtual model of the patient's spine using imaging data, allowing surgical strategies to be developed and tested in silico before actual surgery. This virtual replica enables precise measurement and simulation without affecting the physical patient, improving automation while managing complexity through virtual prototyping.
2Manufacturing precision
If manual methods are used for defining implant positions and surgical gestures, then surgeon judgment is applied, but precision and consistency in surgical outcomes vary
Solution Approach 1:
The system replaces manual mechanical measurement and planning methods with automated computer-based imaging analysis and computational algorithms. This substitution enables precise calculation of implant positions and surgical gestures through software, improving consistency and precision while reducing the time required for surgical planning compared to traditional manual methods.
Solution Approach 2:
The system performs self-service by automatically analyzing patient-specific anatomy from imaging data and generating optimized surgical strategies without requiring extensive manual intervention. The automated algorithms independently determine implant positions, surgical gestures, and predict outcomes, thereby improving precision while minimizing the time investment required from surgeons.
3Adaptability or versatility
If traditional surgical planning is used, then general approaches are applied, but patient-specific customization and optimization are limited
Solution Approach 1:
The system applies local quality by customizing surgical strategies specifically for each patient's unique anatomy and condition. It analyzes patient-specific imaging data to determine individualized implant positions, surgical approaches, and compensatory mechanisms, thereby achieving high adaptability while managing complexity through automated patient-specific analysis rather than manual customization.
Solution Approach 2:
The system performs preliminary customization by generating patient-specific surgical strategies, implant designs, and outcome predictions before surgery. This pre-customization includes creating patient-specific surgical guides and simulating compensatory mechanisms, thereby achieving high adaptability to individual patient needs while reducing the complexity of intraoperative customization.
Data Source
AI summary
A surgical planning and assessment system is disclosed. The system may include a computing system having a processor, a data store, a patient specific planning and analysis module, and a display. The system may be configured to access a database storing a plurality of possible surgical plans. The computing system may store a target surgical plan including a plurality of patient specific inputs including at least one preoperative medical image of a spine of a target patient and analyze the target surgical plan to determine a predicted alignment of the spine of the target patient. The computing system may develop a plurality of predictive models including a predicted alignment of the spine of the target patient based on the target surgical plan and suggest at least one alternative surgical plan with respect to the target surgical plan.