Automated Dental Imaging Localization and Diagnosis Pipeline
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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 for accurate interpretation, particularly for anatomical areas beyond the maxilla and mandible. Existing methods lack robustness for automated anatomical localization and condition classification, and do not effectively support deep learning applications for constructing panoramas with Elements of Interest (EoI) from CBCT images.
Innovation Solution
An automated parsing pipeline system and method that utilizes a combination of hardware and software components, including a processor, voxel parsing engine, and localization layer, to parse volumetric image data, localize anatomical structures, and classify conditions using deep learning models like fully convolutional networks (FCNs) and convolutional neural networks (CNNs), enabling the construction of EoI-focused panoramas and accurate 3D teeth segmentation masks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If CBCT technology is used for dental imaging, then 3D visualization capability is improved, but time consumption and operational complexity increase
Solution Approach 1:
The patent segments the complex CBCT image analysis process into distinct functional modules: a localization layer that identifies anatomical structures (teeth, jawbones, sinuses) and a separate parsing pipeline that processes the segmented data. This modular segmentation enables parallel processing and reduces the overall time consumption while maintaining 3D visualization quality.
Solution Approach 2:
The system performs preliminary automated localization and segmentation of anatomical structures before the dentist needs to interpret the images. The localization layer pre-identifies regions of interest and prepares structured data, so that when the dentist views the CBCT images, the heavy computational work has already been completed, reducing their time consumption and workload.
2Measurement precision
If CBCT technology is used for dental imaging, then 3D visualization capability is improved, but operational complexity increases
Solution Approach 1:
The CBCT imaging system incorporates self-service capabilities through automated image processing algorithms. The localization layer automatically identifies and segments anatomical structures without requiring manual intervention, and the parsing pipeline autonomously processes the data. This reduces the operational complexity for dental professionals while maintaining high 3D visualization capability.
Solution Approach 2:
The patent introduces an intermediary parsing pipeline between the raw CBCT data and the dentist's interpretation. This intermediary layer handles the complex software operations of segmentation, localization, and data structuring, translating complex computational tasks into simplified visual outputs that are easier for dental professionals to interpret.
3Productivity
If automated parsing pipeline is implemented, then productivity is improved, but device complexity increases
Solution Approach 1:
The automated parsing pipeline is segmented into specialized layers: a localization layer for identifying anatomical structures, a segmentation layer for separating different tissues, and a processing layer for data structuring. This segmentation allows each layer to be optimized independently for productivity while managing overall system complexity through modular design.
4Measurement precision
If deep learning models are used for condition classification, then diagnostic accuracy is improved, but device complexity increases
Solution Approach 1:
The deep learning system is segmented into a localization layer that handles anatomical structure identification and a separate classification layer that performs condition diagnosis. This segmentation allows the classification model to focus specifically on diagnostic accuracy while the localization layer handles the complexity of anatomical identification, thereby improving condition classification accuracy while managing model complexity through functional separation.
Data Source
AI summary
A system and method for automated localization, enumeration, and diagnoses of a tooth/condition. The system detects a condition for at least one defined localized and enumerated tooth structure within a cropped image from a full mouth series based on any one of a pixel-level prediction, wherein said condition is detected by at least one of detecting or segmenting a condition on at least one of the enumerated tooth structures within the cropped image by a 2-D R-CNN.