Robotic Rod Insertion Planning with Tower Movement Compensation
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Solution Overview
Problem
Current minimally invasive spinal surgery techniques face challenges in efficiently inserting rods into pedicle screws, as the procedure is time-consuming and not robust, especially when dealing with multiple screw levels, due to the complexity of aligning and adjusting for tower movement during rod insertion.
Innovation Solution
A robotic system with a computing device, imaging device, and navigation system that plans and executes the insertion of rods by calculating optimal paths based on preoperative and intraoperative imaging, using machine learning for tissue recognition and segmentation, and adjusting for tower movement in real-time, with robotic arms assisting in precise rod placement and path adjustment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional manual rod insertion techniques are used, then surgical flexibility is maintained, but procedure time increases and precision decreases
Solution Approach 1:
The patent replaces manual mechanical rod insertion with an automated robotic system that uses computer vision, machine learning, and robotic manipulation to perform the insertion procedure. The robotic arm with specialized end effectors substitutes the surgeon's hands and traditional manual instruments, enabling automated path calculation, real-time tower tracking, and precise rod placement without direct human manipulation during the critical insertion phase.
Solution Approach 2:
The robotic system performs self-positioning and self-adjustment during rod insertion. The system automatically tracks tower movement in real-time, recalculates insertion paths, and adjusts robotic arm positioning without requiring external intervention or manual recalibration. The machine learning models autonomously recognize anatomical structures and optimize insertion parameters based on intraoperative imaging data.
2Manufacturing precision
If real-time tower movement adjustment is implemented, then insertion accuracy is improved, but computational requirements and system complexity increase
Solution Approach 1:
The system implements continuous feedback loops where sensors track tower position and movement in real-time, feed this data to the control system, which then recalculates insertion paths and adjusts robotic arm positioning dynamically. The machine learning models continuously learn from intraoperative imaging and tower movement patterns to refine insertion accuracy throughout the procedure, creating a closed-loop control system that adapts to changing anatomical conditions.
Solution Approach 2:
The robotic system performs preliminary path calculation and positioning before actual rod insertion begins. Preoperative imaging and planning are completed in advance to establish initial insertion paths, and the system pre-positions the robotic arm and prepares insertion tools before the procedure starts. This preliminary preparation reduces real-time computational burden during the critical insertion phase.
3Productivity
If multiple robotic arms are used for rod insertion, then procedural efficiency is improved, but device complexity and cost increase
Solution Approach 1:
The robotic system divides the rod insertion task into separate functional segments handled by different robotic arms. One robotic arm is dedicated to holding and positioning the rod, another arm manages the insertion tool and force application, and additional arms may handle imaging or tower manipulation. This segmentation allows each arm to specialize in specific subtasks, improving overall procedural efficiency while maintaining manageable system complexity through modular architecture.
Data Source
AI summary
Systems and methods for calculating an insertion point and a path for a rod are provided. A surgical plan having at least one image and information about a position of at least one tower may be received. The at least one image may depict a surgical region. A soft tissue portion and at least one anatomical element may be identified in the at least one image. An insertion point and a path from the insertion point to the at least one tower may be calculated based on the identified soft tissue portion and at least one anatomical element. The rod may be inserted at the insertion point and along the path.