Machine Learning Guidance for Navigated Spinal Surgery Planning
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Solution Overview
Problem
Existing surgical procedures for spinal surgeries face challenges in adapting to individual patient anatomies and selecting optimal implant configurations due to limitations in surgeons' ability to plan and execute procedures effectively, leading to suboptimal surgical outcomes.
Innovation Solution
A surgical guidance system utilizing machine learning to analyze patient-specific data, generate customized surgical plans, and provide real-time navigation assistance through an extended reality headset and robotic tools, ensuring precise implant placement and trajectory alignment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a surgeon manually plans and executes spinal surgery procedures, then the surgeon can exercise professional judgment and adaptability, but the surgical precision and consistency are limited by human capability
Solution Approach 1:
The surgical system is divided into distinct functional modules: pre-operative planning module, intra-operative navigation module, post-operative analysis module, machine learning model module, and robotic execution module. Each module handles specific tasks independently, allowing the complex system to achieve high precision through specialized sub-systems while managing overall complexity through modular architecture.
Solution Approach 2:
A machine learning model serves as an intermediary between the surgeon's intent and the robotic execution. The ML model processes pre-operative imaging data, patient-specific anatomy, and surgical goals to generate optimized surgical plans, which then guide the robotic system. This intermediary layer translates human judgment into precise, consistent executable instructions.
2Reliability
If distributed networked computers are used to collect and process feedback data from multiple prior patients, then the machine learning model can be trained on diverse data improving surgical outcomes, but the data processing time and computational resources increase
Solution Approach 1:
Feedback data from distributed networked computers is collected and processed in advance to train the machine learning model before actual surgical use. The system performs preliminary data aggregation, cleaning, and model training during off-hours or between surgical procedures, so that when surgery begins, the model is already trained and ready for rapid inference, minimizing real-time processing delays.
Solution Approach 2:
The system continuously collects feedback data from the distributed network and continuously retrains the machine learning model in the background without interrupting surgical operations. This continuous learning process ensures the model improves over time while maintaining operational continuity, as the trained model can be quickly deployed for new patients without waiting for complete data sets.
3Adaptability or versatility
If a machine learning model generates customized surgical plans for each patient, then the adaptability to individual patient anatomy improves, but the computational complexity and planning time increase
Solution Approach 1:
The machine learning model focuses computational resources on analyzing locally relevant patient-specific features such as individual vertebral geometry, disc space characteristics, and patient-specific anatomical variations. Rather than processing all possible surgical parameters uniformly, the model identifies and prioritizes the specific local anatomical features that most impact surgical planning for each patient, reducing overall computational complexity while maintaining high adaptability.
4Manufacturing precision
If real-time navigation information is provided to guide surgical tool placement, then the accuracy of implant placement improves, but the system complexity and operational burden on the surgeon increase
Solution Approach 1:
The navigation system provides continuous real-time feedback to the surgeon through the extended reality headset, displaying the current position of surgical tools relative to the planned trajectory and target implant location. This feedback loop allows the surgeon to make immediate adjustments to maintain alignment with the optimal path, achieving high placement precision through guided correction rather than requiring complex manual calculations or difficult manual alignment procedures.
Data Source
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AI summary
A surgical guidance system, for computer assisted navigation during spinal surgery, is operative to obtain feedback data provided by distributed networked computers for each of a plurality of prior patients who have undergone spinal surgery. The feedback data characterizes spinal geometric structures of the prior patient, characterizes a surgical procedure performed on the prior patient, characterizes an implant device that was surgically implanted into the prior patient's spine, and characterizes the prior patient's surgical outcome. The surgical guidance system trains a machine learning model based on the feedback data. The surgical guidance system obtains pre-operative data from one of the distributed network computers characterizing spinal geometric structures of a candidate patient for planned surgery, generates a surgical plan for the candidate patient based on processing the pre-operative data through the machine learning model, and provides at least a portion of the surgical plan to a display device.