Automated CBCT and Intra-Oral Scan Alignment for Dental Measurements
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Solution Overview
Problem
Current dental imaging technologies, such as cone beam computed tomography (CBCT), face challenges in time consumption, complexity, and variability in interpretation, particularly for conditions like periodontal bone loss (PBL), with manual alignment of volumetric images and surface scans being time-consuming and prone to errors, lacking dynamic visualization and accurate measurement tools.
Innovation Solution
An automated parsing pipeline system using AI/ML models for anatomical localization and condition classification, integrating voxel parsing engines, localization layers, and alignment modules to align and classify dental structures, enabling precise distance measurements and gradient color mapping for enhanced visualization and analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual alignment of volumetric images and surface scans is performed, then diagnostic accuracy can be improved through precise anatomical localization, but time consumption increases significantly
Solution Approach 1:
The system performs self-alignment by automatically detecting corresponding anatomical landmarks between volumetric CBCT images and surface scan images, then computing transformation matrices to register the datasets without requiring manual intervention. The automated pipeline includes landmark detection, transformation matrix calculation, and application of registration parameters to align the images.
Solution Approach 2:
The patent replaces the manual mechanical alignment process with an automated computational system that uses AI/ML models to detect anatomical landmarks, calculate transformation matrices, and register images automatically. This substitutes human operator skill and time with algorithmic processing.
2Adaptability or versatility
If CBCT imaging technology is used, then three-dimensional view of oral-maxillofacial structures is provided, but complexity for personnel to become acquainted with imaging software and DICOM data increases
Solution Approach 1:
The system introduces an automated alignment pipeline as an intermediary layer between the complex CBCT imaging software/DICOM data and the user's diagnostic needs. This pipeline automatically handles the complexity of image registration, landmark detection, and data fusion, presenting simplified results to users without requiring them to master the underlying technical complexity.
Solution Approach 2:
The automated system performs self-alignment and self-processing of CBCT data without requiring users to manually configure complex imaging parameters or learn DICOM data handling. The system autonomously completes tasks that would otherwise require specialized training.
3Measurement precision
If deep learning is applied to interpret CBCT images, then interpretation accuracy is improved, but the system remains limited to 2D X-ray images rather than volumetric data
Solution Approach 1:
The patent extends deep learning capabilities from 2D image analysis to 3D volumetric data processing. The system processes volumetric CBCT images by extracting features across multiple dimensions and spatial resolutions, then integrates these with surface scan data through automated alignment. This enables AI-based interpretation of three-dimensional anatomical structures rather than limited to two-dimensional projections.
Solution Approach 2:
The system segments the volumetric CBCT data into anatomical regions of interest (teeth, bone, soft tissue) using automated segmentation algorithms. This allows deep learning models to process and interpret different anatomical structures separately while maintaining their three-dimensional context, enabling sophisticated analysis of volumetric data.
4Productivity
If automated alignment system is implemented, then time consumption is reduced, but measurement precision for dental conditions may be compromised without manual verification
Solution Approach 1:
The system incorporates feedback mechanisms where the alignment results are validated through consistency checks of anatomical landmarks and visual inspection tools. The automated pipeline allows for iterative refinement where discrepancies can be identified and corrected, ensuring measurement precision is maintained while achieving automated processing speeds.
Data Source
AI summary
A method for determining a dental condition comprising: receiving at least one volumetric image and at least one intra-oral surface scan image of a dental structure of a patient, wherein the dental structure includes at least one of a crown, root, gingiva or bone; aligning the at least one volumetric image with the at least one intra-oral surface scan image; identifying boundaries of the gingiva and the bone within the aligned images; and calculating distances between the identified boundary of the gingiva and cementoenamel junction (CEJ), the identified boundary of the bone and the CEJ, and the identified boundary of the gingiva and the identified boundary of the bone to determine the dental condition.