Spinal Surgery Navigation With ML Planning and 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 techniques, longer planning times, and inconsistent surgical workflows, leading to variability in patient results.
Innovation Solution
A spinal surgery navigation system utilizing machine learning algorithms processes various data inputs to generate personalized surgical plans by training a machine learning model with intra-operative and post-operative feedback, integrating tracking systems and surgical robots for precise instrument guidance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional spinal surgery procedures are used with surgeon expertise variations, then surgical flexibility is maintained, but patient outcome consistency deteriorates
Solution Approach 1:
The system performs pre-operative planning and generates personalized surgical plans before the actual surgery. The machine learning model is trained on historical surgical data to predict optimal surgical approaches, implant positions, and procedural steps, allowing surgeons to execute predetermined plans with high precision during the actual surgery.
Solution Approach 2:
The system incorporates intra-operative feedback through tracking systems that monitor surgical instrument positions and compare them against the pre-operative plan. Real-time feedback is provided to guide surgeons, and post-operative outcomes are fed back into the machine learning model to continuously improve future surgical plans.
2Productivity
If manual surgical planning is used, then surgeon judgment is applied, but planning time increases
Solution Approach 1:
The system replaces manual surgical planning with an automated machine learning-based planning system. The machine learning model processes patient-specific anatomical data, imaging results, and surgical parameters to generate optimized surgical plans automatically, eliminating the time-consuming manual planning process while maintaining or improving plan quality.
Solution Approach 2:
The system utilizes various input parameters including patient demographics, anatomical measurements, imaging data, and surgical preferences to dynamically generate customized surgical plans. The machine learning model adjusts planning parameters based on the specific case characteristics, providing efficient personalized planning.
3Adaptability or versatility
If standardized surgical protocols are implemented, then workflow consistency is improved, but adaptability to patient-specific conditions deteriorates
Solution Approach 1:
The system provides localized, patient-specific surgical plans tailored to individual anatomical characteristics and surgical needs. Each surgical plan is customized based on the patient's unique spine anatomy, pathology, and surgical goals, while still following standardized procedural frameworks for safety and consistency.
Solution Approach 2:
The surgical navigation system integrates multiple functions including pre-operative planning, intra-operative tracking, real-time guidance, and post-operative analysis into a single unified platform. The machine learning model can handle various surgical procedures and patient types, providing universal applicability across different spinal surgery scenarios.
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.