Bone Point Cloud Reconstruction for Pre-Morbid Prosthetic Planning

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Solution Overview

Problem

Existing surgical joint repair procedures face challenges in accurately selecting and positioning prosthetics due to the lack of pre-morbid characterization of patient anatomy, which is typically unavailable until after disease or injury.

Innovation Solution

Utilizing point cloud neural networks (PCNNs) to process patient anatomy and generate pre-morbid representations by identifying and removing pathological portions from point clouds, enabling accurate prosthetic selection and surgical planning.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Measurement precision

If traditional surgical planning methods are used without pre-morbid characterization, then the surgical procedure can proceed with available morbid anatomy data, but the accuracy of prosthetic selection and positioning is compromised

Engineering Contradiction:

Improveaccuracy of prosthetic selectionVSAvoidunavailability of pre-morbid anatomy data

Core Design Contradiction:

Measurement precisionVSLoss of information

Solution Approach 1:

The system performs preliminary reconstruction of pre-morbid anatomy through point cloud processing and neural network analysis before the actual surgical planning takes place. By analyzing current morbid anatomy and working backwards to predict the pre-morbid state, the system creates accurate pre-surgical models in advance, enabling precise prosthetic selection and positioning without needing actual historical data.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The patent replaces traditional mechanical measurement methods and direct observation of pre-morbid anatomy with computational point cloud processing and neural network algorithms. The system uses digital image correlation, point cloud registration, and machine learning models to substitute for the absent physical pre-morbid anatomical data, achieving accurate reconstruction through information processing rather than direct measurement.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

2Measurement precision

If point cloud neural networks are used to generate pre-morbid representations, then the accuracy of anatomical characterization is improved, but the computational complexity and processing requirements increase

Engineering Contradiction:

Improveaccuracy of pre-morbid characterizationVSAvoidcomputational processing complexity

Core Design Contradiction:

Measurement precisionVSDevice complexity

Solution Approach 1:

The complex task of pre-morbid characterization is divided into distinct processing stages: point cloud generation from imaging data, point cloud registration and alignment, neural network-based pathology identification, and pre-morbid state reconstruction. Each stage handles a specific aspect of the problem, making the overall complex system manageable and allowing for optimized processing at each step.

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The patent introduces point clouds as an intermediary representation between raw medical imaging data and the final pre-morbid anatomical model. The point cloud serves as a computationally efficient intermediate format that captures essential geometric information while enabling sophisticated neural network analysis, bridging the gap between image data and anatomical characterization.

Inventive Principle:

Principle #24Intermediary (Mediator)

3Reliability

If pathological portions are removed from the point cloud to generate accurate pre-morbid representations, then the quality of surgical planning data is improved, but the time required for data processing increases

Engineering Contradiction:

Improvequality of surgical planning dataVSAvoiddata processing time

Core Design Contradiction:

ReliabilityVSLoss of time

Solution Approach 1:

The patent replaces manual pathological portion identification and removal with automated neural network-based segmentation and filtering. The system uses machine learning models to automatically identify and exclude pathological regions from the point cloud, substituting time-consuming manual analysis with faster computational algorithms that maintain or improve accuracy.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The system performs self-service by automatically identifying pathological portions and removing them without requiring manual intervention. The neural network analyzes the point cloud structure, identifies abnormal regions based on learned patterns, and autonomously filters them out, enabling the system to prepare clean pre-morbid representations independently and efficiently.

Inventive Principle:

Principle #25Self-service

Data Source

PatentUS20250363626A1Automated pre-morbid characterization of patient anatomy using point clouds

Publication Date: 2025.11.27 HOWMEDICA OSTEONICS CORP

AI summary

A method for pre-morbid characterization of patient anatomy includes obtaining a first point cloud representing a morbid state of a bone of a patient, generating information indicative of at least one of pathological portions of the first point cloud or non-pathological portions of the first point cloud, the pathological portions of the first point cloud being portions corresponding to pathological portions of the morbid state of the bone, and the non-pathological portions of the first point cloud being portions corresponding to non-pathological portions of the morbid state of the bone, generating a second point cloud that includes points corresponding to the non-pathological portions, and does not include points corresponding to the pathological portions, generating, based on these second point cloud, a third point cloud representing a pre-morbid state of the bone, and outputting information indicative of the third point cloud representing the pre-morbid state of the bone.