Automated Teeth Transformation Simulation Using Segmentation Maps
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Solution Overview
Problem
Dentists struggle to effectively communicate the results of cosmetic dental procedures to patients using crude descriptions or images, as patients often rely on imagination to visualize the outcomes.
Innovation Solution
A system and method using machine learning techniques to simulate dental anatomy transformation by encoding and decoding input segmentation maps, allowing patients to visualize the results of cosmetic procedures through digital image synthesis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If dentists use descriptions or drawings to communicate cosmetic dental procedure results, then the communication method is simple and easy to implement, but the visualization quality is crude and requires patient imagination to understand the outcomes
Solution Approach 1:
The patent creates a digital copy of the patient's specific dental anatomy using 3D scanning technology. This personalized digital model is then used to generate accurate visualizations of procedural outcomes, replacing generic drawings with patient-specific digital replicas that can be transformed to show expected results.
Solution Approach 2:
The system transforms the digital dental model by modifying specific parameters such as tooth position, orientation, and shape to reflect the expected outcomes of cosmetic procedures. These parameter changes are applied to the patient's actual anatomy data, producing realistic visualizations of the transformed state.
2Manufacturing precision
If dentists use images of unrelated persons to illustrate procedure results, then the visualization is more concrete than drawings, but the patient cannot see their own specific anatomy and must rely on imagination to relate it to their own mouth
Solution Approach 1:
The system creates a precise digital copy of the patient's own dental anatomy through 3D scanning, replacing unrelated stock images with the patient's actual anatomical data. This personalized digital twin can then be manipulated to show procedure outcomes specific to their unique dental structure.
Solution Approach 2:
The patent performs preliminary 3D scanning and digital modeling of the patient's dental anatomy before the procedure. This advance creation of the digital model allows for pre-visualization of outcomes tailored to the patient's specific anatomy, eliminating the need to use generic images that require patient imagination to relate to their own mouth.
3Manufacturing precision
If a machine learning system is used to simulate teeth transformation, then the visualization accuracy and personalization are significantly improved, but the system complexity and computational requirements increase
Solution Approach 1:
The system performs preliminary training of the machine learning model using extensive dental anatomy datasets before actual patient applications. This pre-training phase establishes the foundational knowledge and transformation capabilities, allowing the system to efficiently process individual patient cases without requiring complex real-time computations during the actual simulation.
Solution Approach 2:
The patent introduces a segmentation map as an intermediary representation that simplifies the complex task of teeth transformation. The segmentation map breaks down the dental anatomy into discrete, manageable segments that can be independently transformed and reassembled, reducing the computational complexity while maintaining high accuracy in the final visualization.
Data Source
AI summary
Provided is a system and a method for image synthesis of dental anatomy transformation. In an aspect, there is provided a method comprising: receiving an input segmentation map; transforming the input segmentation map into an input latent vector using a trained encoder; transforming the input latent vector to an output latent vector using a trained transformer machine learning model; transforming the output latent vector to an output segmentation map using a trained decoder; and outputting the output segmentation map.