Dental Condition Gradient Color Map Visualization

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Solution Overview

Problem

Current dental imaging technologies, such as cone beam computed tomography (CBCT), face challenges in efficient anatomical localization, condition classification, and visualization of complex conditions like periodontal bone loss, due to time-consuming manual processes and subjective interpretations, which lead to inaccuracies and inconsistencies in diagnosis and treatment planning.

Innovation Solution

An automated parsing pipeline system and method using AI/ML models, specifically V-Net and DenseNet convolutional neural networks, for anatomical localization and condition classification, which includes voxel parsing, segmentation, and alignment of volumetric images with surface scans, enabling gradient color mapping and precise measurement visualization.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Reliability

If CBCT imaging technology is used to obtain 3D views of oral-maxillofacial structures, then diagnostic capability is improved, but time consumption and complexity increase

Engineering Contradiction:

Improvediagnostic capabilityVSAvoidtime consumption

Core Design Contradiction:

ReliabilityVSLoss of time

Solution Approach 1:

The patent segments the CBCT image data into multiple 2D slices and applies deep learning models to each slice independently, then aggregates the results. This segmentation allows parallel processing and reduces the time required to analyze the complete 3D dataset while maintaining diagnostic accuracy.

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The patent replaces manual interpretation of CBCT images by trained dentists with an automated deep learning system. The neural network automatically detects and classifies anatomical structures and pathologies, eliminating the time-consuming manual analysis process while maintaining or improving diagnostic reliability.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

2Ease of operation

If manual measurement and subjective interpretation are used to assess bone loss, then flexibility is maintained, but measurement precision and consistency deteriorate

Engineering Contradiction:

Improveflexibility in assessmentVSAvoidconsistency in diagnosis

Core Design Contradiction:

Ease of operationVSMeasurement precision

Solution Approach 1:

The patent implements a feedback mechanism where the deep learning model continuously refines its measurements by comparing detected bone loss against established anatomical references and clinical guidelines. This feedback loop ensures measurement precision and consistency while maintaining the flexibility to adapt to individual patient cases.

Inventive Principle:

Principle #23Feedback

Solution Approach 2:

The patent changes the measurement parameters from subjective visual assessment to objective quantitative data extraction. The system automatically measures bone loss in millimeters, calculates bone density values, and generates precise diagnostic reports, replacing manual estimation with standardized numerical parameters that ensure consistency.

Inventive Principle:

Principle #35Parameter changes

3Extent of automation

If deep learning is applied to interpret CBCT images, then automation is improved, but device complexity increases

Engineering Contradiction:

Improveautomation levelVSAvoidsystem complexity

Core Design Contradiction:

Extent of automationVSDevice complexity

Solution Approach 1:

The patent designs a universal deep learning framework that can process multiple types of dental imaging data (CBCT, panoramic X-rays, intraoral scans) through a single integrated system. This multi-functionality reduces overall system complexity by consolidating multiple specialized tools into one unified platform that handles various diagnostic tasks.

Inventive Principle:

Principle #6Universality (Multi-functionality)

Solution Approach 2:

The patent introduces an intermediary layer of software that bridges the gap between raw CBCT image data and final diagnostic conclusions. This intermediary processing layer automatically performs image preprocessing, anatomical segmentation, and pathology detection, simplifying the overall system architecture by encapsulating complex operations in a dedicated module.

Inventive Principle:

Principle #24Intermediary (Mediator)

4Productivity

If automated parsing pipeline is implemented for anatomical localization, then productivity is improved, but device complexity increases

Engineering Contradiction:

Improveprocessing speedVSAvoidpipeline complexity

Core Design Contradiction:

ProductivityVSDevice complexity

Solution Approach 1:

The patent segments the automated parsing pipeline into distinct functional modules: image preprocessing module, anatomical segmentation module, localization module, and classification module. Each module handles a specific task independently, which improves processing speed through parallel execution while managing complexity by dividing the overall system into manageable components with well-defined interfaces.

Inventive Principle:

Principle #1Segmentation

Data Source

PatentUS20250017704A1System and Method for Visualizing a Dental Condition as a Gradient Color Map

Publication Date: 2025.01.16 DIAGNOCAT INC

AI summary

A method for visualizing a dental condition as a gradient color map, comprising the steps of: receiving medical imagery of a dental structure, wherein the dental structure is at least one of a root, crown, or bone; determining a baseline in the received dental structure; determining a measured area of the received dental structure based on the determined baseline; calculating a distance to the baseline based on the determined measured area; visualizing the calculated distance as a gradient color map; and plotting measurement lines by rendering every fragment with a predefined distance value.