3D Tooth Shape Modeling for Un-Erupted Orthodontic Aligners
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Solution Overview
Problem
Conventional orthodontic aligners struggle to accurately predict the shape of un-erupted or partially erupted teeth, leading to generic space buffers that may not look natural and can interfere with tooth eruption or cause discomfort.
Innovation Solution
The use of 3D descriptors and automated agents to identify representative tooth shapes based on anatomical identifiers, using techniques like Elliptic Fourier Descriptors and spherical harmonics to create virtual 3D tooth models that guide the design of orthodontic appliances with customized cavities for un-erupted teeth.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If conventional generic tooth shape space buffers are used, then manufacturing is simpler and more standardized, but the appearance becomes unnatural and may interfere with tooth eruption
Solution Approach 1:
The patent applies parameter changes by transitioning from fixed generic tooth shape parameters to variable parameters that adapt to each patient's specific dental anatomy. The system uses 3D scanning data and machine learning models to generate customized tooth shape predictions, allowing the space buffer geometry to vary based on individual tooth size, shape, and eruption patterns while maintaining manufacturability through digital design and additive manufacturing technologies.
Solution Approach 2:
The patent implements preliminary action by predicting the future shape and position of un-erupted teeth before actual eruption occurs. The machine learning model analyzes current dental scans and developmental patterns to pre-determine the optimal space buffer geometry that will accommodate the erupting tooth, preventing interference and ensuring natural appearance from the outset of treatment.
2Device complexity
If generic space buffers are used, then device complexity is reduced, but reliability of accommodating actual tooth shape deteriorates
Solution Approach 1:
The patent applies copying by creating accurate digital replicas of the patient's existing teeth and using machine learning to predict the future形态 of un-erupted teeth. The system generates virtual 3D models that serve as precise templates for space buffer design, ensuring that the final appliance accurately matches the patient's specific dental anatomy rather than relying on generic forms.
Solution Approach 2:
The patent implements self-service through automated machine learning algorithms that independently analyze dental scan data, predict tooth eruption patterns, and generate customized space buffer designs without requiring manual intervention. The system self-optimizes the space buffer geometry based on the patient's unique dental characteristics, maintaining high reliability while managing complexity through automation.
3Loss of time
If conventional techniques are used, then treatment planning is faster and simpler, but the aligners may cause discomfort or interrupt tooth eruption
Solution Approach 1:
The patent applies feedback by using machine learning models that continuously learn from and adapt to the patient's actual tooth development patterns. The system compares predicted versus actual eruption progress, refines its predictions, and adjusts the space buffer design accordingly, ensuring optimal performance while minimizing discomfort and eruption interference throughout the treatment process.
Solution Approach 2:
The patent implements preliminary action by pre-planning the space buffer geometry based on predicted tooth eruption patterns before treatment begins. This advance preparation ensures that the aligners are designed to accommodate the erupting teeth from the start, preventing discomfort and eruption interference rather than addressing these issues reactively during treatment.
Data Source
AI summary
Systems, methods, and computer-readable media for orthodontic treatment planning for dentitions with un-erupted or partially erupted teeth. Three-dimensional (3D) descriptors may be indexed on a database by tooth type, with each 3D descriptor including spatial parameters associated with teeth of the same tooth type. The 3D descriptors may have a minimum distance to other 3D descriptors in 3D descriptor space. Methods may include receiving a tooth type of an at least partially un-erupted tooth of a patient, and estimating a shape of the at least partially un-erupted tooth based on at least one of the 3D descriptors with the same tooth type of the at least partially un-erupted tooth.