AI Panoramic Lesion Visualization for Reduced Dental CT Use
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Solution Overview
Problem
Dental imaging methods, such as CT scans, are costly and expose patients to radiation, while panoramic images can obscure lesions due to anatomical overlap, leading to inaccurate visual identification and unnecessary CT scans.
Innovation Solution
A method using a machine-learned AI model to analyze panoramic images, identify regions of interest, and overlay indicators, with optional CT image viewing, to enhance lesion detection accuracy and reduce unnecessary scans.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If CT imaging is used to accurately determine lesion location and size, then measurement precision is improved, but cost and radiation exposure increase
Solution Approach 1:
The patent uses AI-based virtual CT imaging that generates simulated CT images from panoramic radiographs. This virtual copying approach provides CT-like detailed views without actual radiation exposure, resolving the contradiction between measurement precision and radiation/cost harm.
Solution Approach 2:
The patent introduces an AI-based image processing system as an intermediary between the panoramic radiograph and the diagnostic process. This intermediary transforms the 2D panoramic image into enhanced 3D-like visualizations, enabling accurate lesion detection without requiring actual CT scanning.
2Object-affected harmful factors
If panoramic imaging is used to reduce cost and radiation, then harmful factors are reduced, but measurement precision deteriorates due to overlapping anatomical structures
Solution Approach 1:
The patent transforms the 2D panoramic radiograph into 3D-like virtual CT images through AI processing. This dimensional transformation separates overlapping anatomical structures in the vertical dimension, allowing clear visualization of lesions in overlapping areas while maintaining the cost and radiation benefits of panoramic imaging.
Solution Approach 2:
The patent replaces the mechanical limitation of 2D overlapping structures with AI-based computational processing. The AI model processes the panoramic image data to create enhanced visualizations that overcome the geometric overlapping problem without requiring additional physical imaging equipment.
3Productivity
If AI-based lesion visualization is applied to assist dentists, then productivity is improved, but reliability decreases in overlapping areas
Solution Approach 1:
The patent segments the panoramic image into multiple regions and uses AI models to process each region separately, then integrates the results. This segmentation approach allows the AI to handle complex overlapping areas systematically, improving both productivity through automation and reliability through detailed regional analysis.
Solution Approach 2:
The patent incorporates confidence scoring and verification mechanisms where the AI system provides initial lesion detection results with confidence levels, and the system can request re-evaluation or additional processing when uncertainty is detected. This feedback loop improves reliability in challenging overlapping areas while maintaining high productivity.
Data Source
AI summary
The present disclosure relates to a dental lesion detection method and a system to which the method is applied. A dental lesion information visualization method according to the present disclosure includes the steps of acquiring data of a lesion analysis model which outputs data on the type and location of a lesion included in a panoramic image obtained by capturing an image of the oral cavity of a patient, and inputting a panoramic image into the lesion analysis model to output data on the type and location of a lesion and a lesion detection confidence score.