Flexible Arch Model for Dental Restoration
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Solution Overview
Problem
Current digital dentistry software lacks the ability to automatically generate anatomically correct relationships between multiple tooth units in a dental arch, making it difficult to create flexible arch models that can vary naturally and accurately represent the anatomical constraints of a dental arch.
Innovation Solution
The development of a Flexible Arch Model (FAM) using Principal Component Analysis (PCA) to capture and parameterize the variations of multiple real dental arches, allowing for the creation of a full arch model that can be fitted to an arbitrary digitized arch scan, with algorithms to align and scale individual tooth models to maintain anatomical correctness.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If statistical techniques are used to generate tooth models from library teeth, then a wide variation in tooth shapes can be achieved, but there is no means for assuring that the result continues to be a natural tooth shape
Solution Approach 1:
The patent applies Principal Component Analysis (PCA) to identify and parameterize the key anatomical features of teeth. By representing tooth geometry through statistical parameters derived from training data, the system can generate realistic tooth shapes while maintaining anatomical correctness. The PCA-based model captures the essential variations in tooth morphology and enforces anatomical constraints through these parameters.
Solution Approach 2:
The system uses an iterative optimization process that compares generated tooth models against anatomical constraints and training data. The feedback loop adjusts the statistical parameters to ensure that generated teeth maintain natural anatomical features while allowing for controlled variation. This feedback mechanism ensures anatomical correctness is preserved during the generation process.
2Measurement precision
If existing software tools are used to create crown restorations, then digital precision can be achieved, but skilled dental professionals are still required to perform operations manually
Solution Approach 1:
The patent implements an automated system that performs crown and arch model generation without requiring manual intervention by dental professionals. The PCA-based statistical models automatically generate anatomically correct tooth and arch models by processing scanned dental data through the statistical framework. The system serves itself by autonomously completing tasks that previously required skilled manual operations, while maintaining digital precision throughout the process.
3Ease of operation
If each crown model is treated separately by existing software, then individual tooth design can be performed, but anatomical relationships between consecutive tooth units cannot be enforced
Solution Approach 1:
The patent merges individual tooth models into a unified arch model that enforces anatomical relationships between consecutive teeth. The PCA-based arch model captures the statistical relationships and constraints between multiple teeth in the arch, allowing individual tooth designs to be generated while maintaining proper anatomical spacing, curvature, and orientation. This combining approach ensures that teeth are not designed in isolation but as part of the complete dental arch structure.
Data Source
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AI summary
A flexible arch model (FAM) is computed to capture and parameterize the variations of the multiple real dental arches in a training set to reconstruct missing teeth in a patient's dental anatomy. Building the FAM includes acquiring multiple sets of digitized dental arches with a pair of maxillary (upper) and mandibular (lower) jaws in the right relative position and gathering a pre-defined set of landmark points on the occlusal surface of each arch all in the same order and same corresponding positions across multiple samples. The gathered vectors of landmark points are used to perform statistical modeling (e.g. Principal Component Analysis) to create a linear subspace of the feature points with the basis of principal components (when PCA is used) found during the procedure.