Patient-Specific Cartilage Repair Kits From 3D Joint Segmentation
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Solution Overview
Problem
Conventional implant systems for cartilage repair are poorly customized to patients, leading to inadequate alignment and reduced effectiveness in repairing damaged cartilage, as they are manufactured in standard sizes and do not accurately represent the smooth surfaces of healthy cartilage tissue, and there is a challenge in generating accurate three-dimensional representations of joint surfaces from radiology images for segmentation and manufacturing.
Innovation Solution
A method and system that utilize trainable image segmentation and dynamical model processes to generate precise three-dimensional representations of joint surfaces from radiology image data, allowing for the creation of customized surgical kits with improved alignment and manufacturing of implants and guides tailored to individual patient anatomy, using trained segmentation and dynamical model control parameter sets to produce geometrical objects representing cartilage damage and surgical kit perimeters.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If standard-sized implants and guides are used in conventional systems, then manufacturing cost and simplicity are improved, but customization to patient anatomy and alignment accuracy deteriorate
Solution Approach 1:
The system performs preliminary actions by generating 3D representations of the patient's joint surfaces and calculating optimal implant positions and orientations before manufacturing. The surgical guide design is prepared in advance based on pre-operative imaging data, allowing custom implants to be manufactured with precise alignment specifications tailored to each patient's anatomy.
Solution Approach 2:
The system changes parameters by transitioning from fixed standard implant dimensions to variable custom dimensions based on patient-specific 3D joint surface measurements. The implant geometry, position, and orientation parameters are adjusted according to the calculated optimal values derived from the patient's radiology images and joint surface representations.
2Device complexity
If standard implants are used, then device complexity is reduced, but effectiveness in repairing damaged cartilage deteriorates due to poor customization
Solution Approach 1:
The system segments the joint surface analysis into distinct processing steps: generating 3D representations from radiology images, identifying damaged cartilage regions, calculating optimal implant positions, and designing custom surgical guides. This segmentation allows for systematic customization while managing complexity through structured workflows.
Solution Approach 2:
The system creates accurate 3D digital copies of the patient's joint surfaces from radiology images. These digital models serve as precise replicas that can be manipulated and measured to determine optimal implant specifications, ensuring the physical implant matches the patient's anatomy without requiring complex physical measurement procedures.
3Loss of information
If 3D representations are generated from radiology images using conventional segmentation, then imaging data is utilized, but surface smoothness and accuracy deteriorate compared to healthy cartilage surfaces
Solution Approach 1:
The system replaces manual or conventional automated segmentation processes with an optimized image processing workflow that generates smoother 3D surface representations. The substitution involves using advanced algorithms to process radiology images and reconstruct joint surfaces with improved smoothness and accuracy, better representing healthy cartilage topology.
Solution Approach 2:
The system changes processing parameters in the segmentation and surface reconstruction processes to improve output quality. By adjusting parameters related to surface smoothing, resolution, and reconstruction algorithms, the system generates 3D representations that more accurately reflect healthy cartilage surfaces while retaining essential anatomical information.
Data Source
AI summary
A method of manufacturing a surgical kit for cartilage repair in an articulating surface of a joint, comprising the steps of receiving radiology image data representing three dimensional image of a joint; generating a first three dimensional representation of a first surface of the joint in a trainable image segmentation process dependent on a trained segmentation process control parameter set and said radiology image data; generating a set of data representing a geometrical object based on said first surface, wherein said geometrical object is confined by said first surface; generating control software adapted to control a CAD or CAM system to manufacture a surgical kit for cartilage repair dependent on said set of data representing a geometrical object and on a predetermined model of components of said surgical kit.