Spinal Surgery Navigation With Machine Learning Planning Feedback
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Solution Overview
Problem
Current spinal surgery procedures face challenges in achieving patient-specific and standardized outcomes due to variations in surgeon expertise and technique choices, leading to inconsistent results and longer planning and operation times.
Innovation Solution
A spinal surgery navigation system utilizing machine learning algorithms processes various data inputs to generate personalized surgical plans, integrating pre-operative, intra-operative, and post-operative feedback to optimize surgical workflows and improve outcomes through computer-assisted navigation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If traditional spinal surgery procedures are performed without computer-assisted navigation, then the surgical process is simpler and faster to implement, but the surgical precision and patient-specific customization are reduced
Solution Approach 1:
The system performs pre-operative planning and generates patient-specific surgical plans before the actual surgery. This preliminary action includes creating 3D models of the patient's spine, determining optimal implant positions, and preparing navigation data structures, which enables high surgical precision without adding complex equipment during the procedure itself.
Solution Approach 2:
The patent introduces a computer-assisted navigation system that acts as an intermediary between the surgeon and the surgical field. This navigation system processes imaging data, generates visual guidance overlays, and provides real-time feedback without requiring direct physical intervention, thereby improving precision while maintaining operational simplicity.
2Reliability
If surgeon expertise and technique choices vary, then individualized surgical approaches can be adapted, but outcome consistency and standardization deteriorate
Solution Approach 1:
The navigation system provides localized, patient-specific guidance tailored to each anatomical region and surgical requirement. It generates customized visual overlays and navigation data that adapt to the specific anatomical features and surgical needs of each patient, ensuring consistent high-quality outcomes while preserving surgeon judgment and adaptability.
Solution Approach 2:
The system implements real-time feedback mechanisms during surgery, comparing actual surgical progress against the pre-planned optimal trajectory. This feedback loop enables surgeons to maintain outcome consistency by continuously adjusting their techniques based on navigation guidance, while still preserving the flexibility to adapt to intra-operative findings.
3Measurement precision
If more data processing and machine learning models are used, then surgical planning accuracy improves, but computation time and system resource requirements increase
Solution Approach 1:
The system performs computationally intensive data processing, machine learning model training, and 3D reconstruction during the pre-operative phase. By completing these time-consuming calculations before surgery, the system achieves high surgical planning accuracy without delaying the actual surgical procedure, thus resolving the time-accuracy trade-off.
Data Source
AI summary
A spinal surgery navigation system is disclosed for computer assisted navigation during spinal surgery. The spinal surgery navigation system is operative to obtain intra-operative feedback data and/or post-operative feedback data regarding spinal surgery outcome for a plurality of patients, and train a machine learning model based on the intra-operative feedback data and/or the post-operative feedback data. The operations obtain pre-operative patient data characterizing a spine of a defined-patient, generate a spinal surgery plan for the defined-patient based on processing the pre-operative patient data through the machine learning model, and provide the spinal surgery plan to a display device for review by a user.