ML-Based Neurological Structure Visualization in Spinal Surgery
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Solution Overview
Problem
Current surgical techniques, particularly in minimally invasive spinal surgery, face challenges in accurately identifying and avoiding neurological structures during procedures, leading to a higher incidence of neurological complications.
Innovation Solution
The development of a system and method that uses machine-learning networks and models to predictively locate and visualize neurological structures based on learned anatomical associations, allowing for the creation of patient-specific surgical plans to avoid contact with these structures.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If minimally invasive surgical techniques are used, then recovery time is reduced and post-operative pain is minimized, but the rate of nerve root injury increases significantly
Solution Approach 1:
The system performs preoperative planning and intraoperative guidance by predicting the locations of neurological structures before and during the surgical procedure. This preliminary identification of safe zones and neural structures allows surgeons to plan trajectories that avoid nerves, thereby reducing nerve root injury rates while maintaining the benefits of minimally invasive approaches
Solution Approach 2:
The system introduces an intermediary layer of computational prediction and visualization between the surgeon and the surgical field. By using machine learning models to predict neurological structure locations and overlaying this information on imaging data, the system mediates the surgical process to provide real-time guidance that prevents nerve injury
2Measurement precision
If CT imaging is used for preoperative planning, then musculoskeletal structures can be visualized, but neurological structures cannot be seen
Solution Approach 1:
The system creates a predictive copy or model of the neurological structures based on training data from MRI scans. This virtual model is then overlaid on the CT imaging data, allowing surgeons to visualize both musculoskeletal structures (from CT) and predicted neurological structures (from the predictive model) in the same coordinate system
Solution Approach 2:
The system merges CT imaging data (showing musculoskeletal structures) with predictive modeling output (showing neurological structures) into a unified visualization. This combination allows simultaneous viewing of both structure types in a single coordinate system, eliminating the need to switch between different imaging modalities
3Loss of information
If MRI imaging is used to visualize both musculoskeletal and neurological structures, then comprehensive anatomical information is obtained, but MRI cannot be used for preoperative surgical planning or intraoperative use
Solution Approach 1:
The system replaces the need for actual MRI scanning during surgery with a computational prediction model. The machine learning model, trained on MRI data, generates predictions of neurological structure locations that can be rapidly computed and overlaid on intraoperative imaging, substituting the slow, expensive MRI process with fast computational modeling
Solution Approach 2:
The system changes the parameters of the imaging approach by using CT scans (which are faster and more suitable for intraoperative use) combined with predictive modeling, rather than relying on MRI. The predictive model adjusts its output based on the specific patient anatomy observed in the CT scan, maintaining accuracy while adapting to the different imaging modality
Data Source
AI summary
A system may be configured to facilitate a medical procedure. Some embodiments may: acquire a scan corresponding to a region of interest (ROI); capture an image of a patient in real-time; identify, via a trained machine learning (ML) model using the acquired scan and the captured image, a Kambin's triangle; and overlay, on the captured image, a representation of the identified Kambin's triangle.