Automated Drilling Template Design Using Neural Networks
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Solution Overview
Problem
Existing methods for designing drilling templates are complex and prone to measurement inaccuracies or design errors, leading to incorrect implant drilling due to user involvement in virtual or impression-based designs.
Innovation Solution
A method utilizing a 3D surface measuring device or X-ray/MRI to create a 3D model, combined with an artificial neural network (CNN) for machine learning to automatically design a drilling template, taking into account anatomical structures and planning implant positions, types, and drilling depths, allowing for fully automated production.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If user designs drilling template using virtual models or impressions, then the template can be customized to fit patient anatomy, but measurement inaccuracies and design errors occur leading to incorrect implant drilling
Solution Approach 1:
The system uses self-correction through iterative optimization where the neural network automatically adjusts template design parameters to eliminate measurement errors and ensure drilling accuracy without requiring manual user intervention for error correction
Solution Approach 2:
The patent replaces manual user design processes with an automated neural network system that uses machine learning algorithms to generate and optimize template designs, substituting human judgment with computational intelligence to eliminate user-induced errors
2Adaptability or versatility
If complex manual design methods are used for drilling templates, then customization to patient anatomy is achieved, but the design process becomes time-consuming and error-prone
Solution Approach 1:
The neural network is pre-trained on extensive dental anatomy data and implant planning scenarios, enabling it to rapidly generate customized template designs without requiring time-consuming manual analysis or iterative design adjustments by users
Solution Approach 2:
The patent replaces complex manual design procedures with an automated neural network system that performs rapid iterative optimization, substituting time-intensive human design processes with computational algorithms that generate customized templates in minutes
3Productivity
If automated production methods like 3D printing are used, then drilling templates can be produced quickly, but production accuracy may be compromised
Solution Approach 1:
The system performs preliminary error compensation by pre-calculating and correcting for known 3D printing dimensional deviations in the neural network output, ensuring that the final printed template achieves the required accuracy despite the limitations of additive manufacturing processes
Solution Approach 2:
The patent adjusts design parameters and tolerances in the digital model to account for 3D printing process variations, modifying the template geometry proactively to ensure that the final manufactured product meets precision requirements despite production speed priorities
Data Source
AI summary
A method for designing a drilling template, wherein a dental situation is measured by means of a 3D surface measuring device and a 3D surface model of the dental situation is produced and/or measured by means of an X-ray device or an MRI device, wherein the dental situation is measured and a volume model of the dental situation is produced, the method comprising the steps of: applying an artificial neural network for machine learning (convolutional neural network; CNN) to the 3D surface model of the dental situation and/or the volume model of the dental situation and/or to an initial 3D model of the drilling template; and automatically producing a ready made 3D model of the drilling template.