Pedicle Screw Placement Prediction for Faster Surgical Planning
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Solution Overview
Problem
Current surgical planning for pedicle screw placement in spine surgery is time-consuming, inefficient, and prone to errors due to limited access to relevant data and computational constraints, leading to high failure rates and complications.
Innovation Solution
A multi-staged imaging pipeline using machine learning models for medical imaging, including detection, segmentation, and prediction of pedicle screw placement, optimized for hardware constraints, with user input and constraint adjustment, and output via API.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If surgical planning is performed manually by surgeons reviewing imaging studies and determining screw placement, then surgeons can make informed decisions, but the process is time-consuming and tedious
Solution Approach 1:
The patent introduces an automated software system as an intermediary between the surgeon and the surgical planning process. This system processes medical images, identifies anatomical landmarks, calculates optimal screw trajectories, and generates placement recommendations, thereby automating time-consuming manual tasks while maintaining surgical decision-making authority
Solution Approach 2:
The patent replaces the mechanical/manual process of surgical planning with an automated computational system. Instead of manually reviewing images and calculating trajectories, the system uses algorithms to automatically process imaging data, identify anatomical structures, and compute optimal screw placement parameters
2Reliability
If surgeons manually review imaging studies and determine screw placement, then they can incorporate relevant data, but the process is tedious and may be infeasible due to data availability or time constraints
Solution Approach 1:
The patent enables the surgical planning system to perform self-service by automatically acquiring, processing, and analyzing relevant data without requiring manual intervention. The system independently reviews imaging studies, identifies anatomical landmarks, and generates placement recommendations, reducing the burden on surgeons while improving reliability through comprehensive data analysis
3Adaptability or versatility
If a large array of pedicle screw options are made available to surgeons, then device availability for different patient needs is ensured, but inefficiencies and inventory management challenges arise
Solution Approach 1:
The patent uses parameter changes to optimize device selection by automatically determining the appropriate screw specifications (length, diameter, trajectory) based on patient-specific anatomical measurements and surgical requirements. This automation reduces the need for surgeons to manually evaluate multiple options and improves inventory management efficiency
Data Source
AI summary
A computer-implemented method for placing pedicle screws is disclosed herein. The method inputs a medical image having at least one bone image which is then divided into spatial segments. A set number of screws are spaced in each segment according to a neural network trained on medical images of screw placement in a similar procedure.