Orthognathic Treatment Parameter Estimation Using Differentiable Simulator
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Solution Overview
Problem
Current methods for modeling treatment outcomes in clinical interventions, such as orthodontic procedures, are computationally intractable due to the large number of potential treatment parameters, making it difficult to predict and achieve desired patient goals effectively.
Innovation Solution
A differentiable simulator system that uses machine-learning models to estimate and update treatment parameters based on patient data and treatment goals, calculating an objective function to minimize the difference between predicted outcomes and desired goals, allowing for iterative tuning of parameters to achieve optimal treatment plans.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional methods are used to model treatment outcomes with many treatment parameters, then comprehensive treatment modeling is achieved, but computational intractability occurs
Solution Approach 1:
The patent replaces traditional mechanical/computational simulation methods with a machine learning model that has been trained on treatment data. Instead of computationally simulating each treatment parameter's effect through complex physical models, the ML model learns patterns from historical data and directly predicts treatment outcomes, substituting the mechanical simulation approach with a data-driven approach that is computationally efficient.
Solution Approach 2:
The patent performs preliminary training of the machine learning model using historical treatment data before actual treatment planning. This preliminary action pre-computes the relationships between treatment parameters and outcomes, so that during actual use, the model can quickly predict results without performing complex real-time computations. The heavy computational work is done in advance during the training phase.
2Measurement precision
If the number of treatment parameters is increased to improve treatment planning, then treatment accuracy is improved, but computational intractability worsens
Solution Approach 1:
The patent substitutes complex computational simulations with a pre-trained machine learning model. The ML model can handle multiple treatment parameters simultaneously and predict outcomes accurately without requiring computationally intensive simulations for each parameter combination, thus maintaining precision while improving efficiency.
Solution Approach 2:
The patent creates a computational copy or surrogate model of the complex treatment system through machine learning. Instead of directly simulating the complex interactions of multiple treatment parameters, the ML model learns to replicate the system's behavior from training data, providing accurate predictions at a fraction of the computational cost.
3Manufacturing precision
If traditional simulation methods are used, then detailed treatment modeling is possible, but iterative parameter tuning becomes infeasible
Solution Approach 1:
The patent replaces time-consuming traditional simulation methods with a fast machine learning inference process. The ML model can evaluate multiple treatment parameter configurations rapidly, enabling iterative tuning and optimization of treatment plans without the prohibitive time cost of traditional simulations for each iteration.
Solution Approach 2:
The patent performs preliminary training of the ML model on comprehensive treatment data, pre-computing the complex relationships between parameters and outcomes. This allows rapid iterative tuning during actual treatment planning, as the model can quickly predict outcomes for different parameter settings without performing expensive simulations each time.
Data Source
AI summary
A method includes receiving a) orthognathic patient data comprising information representing an initial state of a patient and b) plurality of treatment parameters that are based on a treatment goal for the patient. The method further includes generating an output state of the patient based on the initial state of the patient and the plurality of treatment parameters, calculating an objective function based on the output state of the patient and the treatment goal of the patient, and updating one or more of the treatment parameters based on the objective function.