Caries Detection Using Multi-Wavelength Imaging and Pattern Matching
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current methods for dental caries detection, such as visual examination and radiographic imaging, are subjective and often miss early-stage caries. Existing imaging techniques using different wavelengths of light have proven difficult and computationally expensive, lacking reliability in detecting caries.
Innovation Solution
The use of a trained pattern matching agent, such as a machine learning agent, to analyze multiple images of the teeth taken from different angles, each with multiple channels (e.g., near-infrared, visible light, fluorescent). These images are used to identify caries centers and boundaries, which are then projected onto a 3D model of the teeth to determine consensus caries centers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional visual examination and radiographic imaging are used for caries detection, then the method is simple and widely available, but detection accuracy is poor and early-stage caries are often missed
Solution Approach 1:
The detection system segments the tooth surface into multiple regions and uses multiple imaging channels (visible light, near-infrared, fluorescent) to capture different characteristics of caries lesions. This segmentation allows the system to detect early-stage caries that may not be visible in traditional single-mode imaging by analyzing specific wavelength responses at different tooth locations.
Solution Approach 2:
The patent transitions from traditional 2D radiographic imaging to a multi-dimensional detection approach by combining multiple wavelength channels (visible light, near-infrared, fluorescent) with 3D surface mapping. This dimensional expansion enables the system to detect caries lesions that appear normal in conventional 2D X-rays by analyzing spectral characteristics across multiple dimensions simultaneously.
2Reliability
If multiple wavelength imaging techniques are used to detect caries, then detection capability improves, but the system becomes computationally expensive and difficult to assess
Solution Approach 1:
The system extracts and analyzes specific spectral features from each imaging channel separately before integrating the results. By extracting wavelength-specific information (visible light for surface structure, near-infrared for subsurface lesions, fluorescent for mineral density), the system reduces computational complexity while maintaining the reliability benefits of multi-wavelength imaging.
Solution Approach 2:
The patent introduces an intermediary processing layer that automatically aligns and fuses multiple imaging modalities using a trained pattern matching agent. This intermediary layer mediates between the raw multi-wavelength data and the final caries detection output, simplifying the assessment process by providing standardized confidence scores and location markers that are easy to interpret for dental professionals.
3Measurement precision
If subjective visual examination is performed, then the examination is quick and requires minimal equipment, but detection is unreliable and prone to false negatives
Solution Approach 1:
The system performs self-service by automatically analyzing the multi-wavelength images and generating caries detection results without requiring extensive operator intervention. The trained pattern matching agent autonomously processes the imaging data, identifies caries lesions, and provides confidence scores, reducing the skill threshold for reliable detection while maintaining high precision through automated spectral analysis.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach significantly improves the accuracy and reliability of dental caries detection, minimizing false negatives and false positives, and providing a more objective and efficient method for identifying caries at various stages.
Implementation Method 1
each image having two or more fields (e.g., wavelengths such as visible light, near-infrared, fluorescent, etc.)
Implementation Method 2
techniques such as light absorption, scattering, transmission, reflection and/or fluorescence
Implementation Method 3
techniques such as light absorption, scattering, transmission, reflection and/or fluorescence
Data Source
AI summary
Methods and apparatuses for dental caries detection and communication. These methods and apparatuses may be used in real time, as part of an intraoral scanning system, or at any point following intraoral scanning. These methods and apparatuses may use a plurality of different images of the teeth, each having two or more fields (e.g., wavelengths such as visible light, near-infrared, fluorescent, etc.), and may use a trained pattern matching agent to detect possible caries centers from each image, then may project some or all of the possible caries centers onto a 3D model of the teeth to identify consensus caries and caries centers.