Point Cloud Surgical Guide Prediction for Accurate Tool Alignment
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Solution Overview
Problem
Existing orthopedic surgery planning systems lack accuracy and surgeon confidence due to insufficient alignment guides based on similar patient cases, leading to improper tool alignment and increased failure probability of orthopedic prostheses.
Innovation Solution
A computing system uses a point cloud neural network (PCNN) to generate a second point cloud from a first patient bone cloud, determining tool alignment and alignment guides, trained on similar patients and experienced surgeons for improved accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If automated planning systems are used to predict tool alignments, then productivity is improved, but measurement precision deteriorates due to lack of accuracy in alignment prediction
Solution Approach 1:
The system creates a virtual copy of the patient's bone anatomy through point cloud generation from CT scan data. This digital replica is then processed by the point cloud neural network to predict tool alignments, allowing accurate predictions without direct physical measurement on the patient during surgery.
Solution Approach 2:
The patent replaces traditional mechanical measurement and manual planning methods with an automated neural network system. The point cloud neural network processes 3D point cloud data to automatically determine optimal tool alignments, substituting manual surgical planning with AI-based prediction.
2Reliability
If alignment guides based on similar patient cases are used, then reliability is improved, but device complexity increases due to need for case-specific customization
Solution Approach 1:
The system automatically adjusts alignment parameters by processing the unique point cloud data of each patient's bone anatomy through the neural network. The model adapts its predictions based on the specific geometric parameters extracted from each patient's scan, providing customized alignment guides without manual customization complexity.
Solution Approach 2:
The point cloud neural network performs self-adjustment and self-optimization by automatically learning from training data and adapting to each patient's unique anatomy. The system serves itself by automatically generating personalized alignment predictions without requiring external intervention or complex manual customization processes.
3Manufacturing precision
If point cloud neural network is applied to generate tool alignment, then manufacturing precision is improved, but loss of information increases due to data processing requirements
Solution Approach 1:
The system extracts essential geometric features and spatial relationships from the complex CT scan data and transforms them into a simplified point cloud representation. This extraction process retains the critical information needed for alignment prediction while reducing the complexity and volume of data that needs to be processed.
Solution Approach 2:
The point cloud serves as an intermediary representation between the raw CT scan data and the final alignment predictions. This intermediate format preserves essential geometric information while making the data more manageable and suitable for neural network processing, preventing information loss during the transformation process.
Data Source
AI summary
A method for predicting a tool alignment, the method comprising: obtaining, by a computing system, a first point cloud representing one or more bones of a patient; applying, by the computing system, a point cloud neural network to generate a second point cloud based on the first point cloud, the second point cloud, comprising points indicating the tool alignment; and determining, by the computing system, the tool alignment based on the points indicating the tool alignment.