Dental Model Generation Using an Objective Function for Hard Constraints
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Solution Overview
Problem
Existing dental restoration workflows are time-consuming and subjective, and conventional machine learning methods struggle with data collection challenges, legal concerns, and the inability to enforce hard constraints, leading to suboptimal dental model generation.
Innovation Solution
A computer-implemented method using an objective function that incorporates quality estimation functions and constraints to iteratively transform dental models, ensuring compliance with hard constraints and optimizing shape parameters for improved dental model generation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If conventional machine learning methods are used to generate dental restorations, then automation is improved, but the ability to meet hard constraints deteriorates
Solution Approach 1:
The patent introduces an objective function as an intermediary between the machine learning model and the hard constraints. This objective function incorporates constraint satisfaction terms that guide the neural network to generate restorations meeting minimum thickness and other hard constraints, while still allowing automated generation through the neural network framework.
Solution Approach 2:
The patent changes the parameter space by transforming the restoration design problem into an optimization problem where the objective function evaluates multiple parameters including constraint compliance. By modifying the objective function to include constraint terms, the system automatically adjusts design parameters to satisfy hard constraints while maintaining automation.
2Adaptability or versatility
If virtual sculpting is used to create dental restorations, then customization is improved, but time consumption deteriorates
Solution Approach 1:
The patent applies preliminary action by pre-training the neural network on large datasets of dental restorations and their associated objective function evaluations. This pre-training allows the model to generate customized restorations quickly without requiring time-consuming virtual sculpting, as the network has already learned optimal design patterns during the preliminary training phase.
Solution Approach 2:
The patent replaces the mechanical virtual sculpting process with a computational neural network system. Instead of manually manipulating 3D models through virtual sculpting tools, the system uses automated neural network inference to generate restorations, substituting manual mechanical interaction with automated computational processing that is both faster and equally adaptable to customization needs.
3Measurement precision
If large datasets are collected for machine learning training, then model accuracy is improved, but data collection challenges and legal concerns deteriorate
Solution Approach 1:
The patent applies self-service by enabling the neural network to generate its own training data through synthetic data generation or by using publicly available dental datasets that do not require complex patient-specific data collection. The objective function and loss functions are designed to work with simplified datasets, allowing the model to achieve high accuracy without the burden of collecting and standardizing large amounts of sensitive patient data.
Data Source
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AI summary
Disclosed is a computer-implemented method of generating a dental model based on an objective function output, comprising creating an objective function comprising at least one quality estimation function which trains at least one machine learning method that generates quality estimation output, and an objective function output is the output of the objective function providing a model as an input data to the objective function and generating model-related objective function output; and modifying the model based on the model-related objective function output to transform the model to a generated model, wherein the generated model is the dental model.