Automated Dental Imaging Pipeline for Anatomical Localization
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Solution Overview
Problem
Current dental imaging technologies, such as cone beam computed tomography (CBCT), face challenges including time consumption, complexity in software usage, and the need for specialized training to interpret anatomical areas beyond the maxilla and mandible, while existing automated systems are limited in their ability to perform anatomical localization and condition classification with minimal image analysis training and are prone to visual ambiguities.
Innovation Solution
An automated parsing pipeline system and method that utilizes a combination of volumetric image processing, voxel parsing engines, and convolutional neural networks (CNNs) for anatomical localization and condition classification, capable of constructing a panorama of the teeth arch with elements of interest emphasized, allowing for the localization of anatomical structures and classification of tooth conditions with minimal training requirements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If CBCT technology is used for dental imaging, then three-dimensional view of oral-maxillofacial structures is achieved, but time consumption and complexity of software usage increase
Solution Approach 1:
The patent segments the CBCT imaging process into automated components: automatic acquisition of volumetric data, automatic segmentation of anatomical structures (teeth, jaws, soft tissues), and automatic generation of 2D panoramic views. This segmentation of tasks reduces manual intervention time while preserving 3D imaging capability through structured processing stages.
Solution Approach 2:
The patent introduces deep learning models and AI algorithms as intermediary systems between the CBCT scanner and the final diagnostic output. These intermediaries automatically process the volumetric data, perform anatomical localization, generate panoramas, and provide measurements, thereby reducing the time and complexity burden on dental professionals while maintaining imaging accuracy.
2Measurement precision
If CBCT technology is used for dental imaging, then three-dimensional view of oral-maxillofacial structures is achieved, but complexity of personnel training increases
Solution Approach 1:
The patent implements self-service automation where the system performs anatomical localization, structure segmentation, panorama generation, and measurement extraction without requiring specialized training. The AI models automatically identify and measure structures such as teeth, jaws, and soft tissues, eliminating the need for personnel to be fully acquainted with complex DICOM data handling and advanced imaging software.
Solution Approach 2:
The patent replaces the mechanical system of manual image analysis and interpretation with automated AI-based processing. Instead of requiring trained personnel to manually segment and measure structures from 3D CBCT data, the system uses deep learning models to automatically perform these tasks, significantly reducing training requirements while maintaining analytical precision.
3Ease of operation
If automated parsing pipeline is implemented for anatomical localization, then training requirements are reduced, but system complexity increases
Solution Approach 1:
The patent merges multiple complex functions into a single integrated automated parsing pipeline: volumetric image acquisition, anatomical structure segmentation, 2D panorama generation, and measurement extraction are combined into one unified system. This consolidation reduces the need for multiple separate trained systems while managing overall complexity through coordinated processing stages.
Solution Approach 2:
The patent creates a universal automated parsing pipeline that handles multiple tasks simultaneously: localizing anatomical structures, segmenting tissues, generating panoramic views, and extracting measurements. This multi-functional system reduces training requirements by providing a single comprehensive solution rather than multiple specialized tools, though it increases internal system complexity that is managed through modular AI architecture.
Data Source
AI summary
Disclosed is a system and method for classifying a tooth condition, comprising: a localization layer; a classification layer; a processor; a non-transitory storage element coupled to the processor; encoded instructions stored in the non-transitory storage element, wherein the encoded instructions when implemented by the processor, configure the automated parsing pipeline system to: localize a present anatomical landmark in a patient's oral or maxillofacial region depicted in a parsed image frame by the localization layer, said landmark comprising of a feature or features with anatomical or pathological significance; and classify a dental or maxillofacial condition based on measuring any kind of relation between two or more features within and/or between landmarks by the classification layer.