Point Cloud Landmark Estimation for Orthopedic Tool Alignment
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Solution Overview
Problem
Selecting inappropriate tool alignment and prosthetic design during orthopedic surgery can lead to suboptimal surgical outcomes, increased failure probability, and complications, especially in cases involving morbid bones with deformities.
Innovation Solution
Utilizing a point cloud neural network (PCNN) to process a 3D representation of patient bones, generating an output point cloud with labeled landmarks to aid in orthopedic surgery planning, including visualization and selection of surgical guides, prosthetics, and tool alignments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If automated landmark estimation using point cloud neural networks is implemented, then measurement precision and manufacturing precision are improved, but device complexity increases
Solution Approach 1:
The patent replaces manual landmark identification by surgeons with an automated point cloud neural network system. The neural network processes 3D point cloud data of bone structures and automatically identifies and labels landmarks, substituting the mechanical/manual process with an intelligent computational system. This achieves high measurement precision while managing the complexity through algorithmic automation.
Solution Approach 2:
The patent introduces a point cloud neural network as an intermediary between the input 3D bone data and the surgical planning output. This intermediary component automatically processes the complex task of landmark identification, acting as a mediator that transforms raw imaging data into structured anatomical information without requiring direct manual intervention for each landmark.
2Device complexity
If manual landmark identification is used, then device complexity is reduced, but measurement precision and surgical planning accuracy deteriorate
Solution Approach 1:
The patent implements a self-service system where the point cloud neural network autonomously performs landmark identification without requiring continuous manual input or surgeon intervention for each landmark. The system processes 3D bone data independently, automatically extracting and labeling landmarks, thereby maintaining simplicity in the user interface while achieving high precision through automated computation.
3Productivity
If automated prosthetic selection and design are implemented, then productivity and manufacturing precision are improved, but device complexity and loss of information increase
Solution Approach 1:
The patent performs preliminary action by pre-processing 3D bone data into point cloud representations and pre-identifying landmark locations using the neural network before the actual surgical planning. This preliminary automated processing prepares all necessary anatomical information in advance, allowing the surgical planning to proceed more efficiently with reduced complexity during the actual procedure.
Solution Approach 2:
The patent segments the complex surgical planning task into distinct modules: (1) 3D data acquisition, (2) point cloud generation, (3) landmark identification via neural network, (4) surgical guide design, and (5) prosthetic selection. This segmentation allows each component to be optimized independently, managing overall system complexity while improving productivity through automated workflows.
Data Source
AI summary
A computing system may be configured to obtain the first point cloud representing one or more bones of a patient, process the first point cloud using one or more point cloud neural networks to generate an output point cloud, the output point cloud including labels indicating locations of one or more landmarks on the one or more bones of the patient, and output the output point cloud.