Dental Arch Image Analysis for Patient-Captured Photo Quality
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current orthodontic treatments require patients to transmit dental arch images to orthodontists for analysis, which is cumbersome and inefficient.
Innovation Solution
A method utilizing deep learning devices, preferably neural networks, to analyze dental arch images by creating a learning base of historical images, training the network, and determining tooth and image attributes, enabling automatic and immediate image classification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual analysis of dental arch images by orthodontists is used, then analysis accuracy is maintained, but time consumption and operational complexity increase
Solution Approach 1:
The system performs preliminary actions by creating a comprehensive learning base from historical images with known outcomes before actual analysis is needed. This pre-trained deep learning model enables rapid analysis without compromising accuracy, as the complex pattern recognition has already been established in advance.
Solution Approach 2:
The patent replaces the mechanical system of manual orthodontist analysis with an automated deep learning-based image analysis system. The neural network processes dental arch images automatically, substituting human manual examination while maintaining diagnostic accuracy and significantly reducing time consumption.
2Productivity
If deep learning analysis is implemented, then analysis speed and automation are improved, but system complexity increases
Solution Approach 1:
The deep learning system is designed with multi-functionality, serving multiple purposes: it analyzes dental arch images, identifies tooth attributes, detects orthodontic appliances, and provides diagnostic information. This universal approach consolidates multiple analysis functions into a single system, managing complexity through integration rather than proliferation of separate systems.
Solution Approach 2:
The patent introduces an intermediary layer - the deep learning model - that bridges raw image data and clinical diagnostic information. This intermediary processes images through multiple convolutional layers and neural networks, transforming complex image patterns into interpretable diagnostic outputs, thereby managing system complexity through structured intermediate processing stages.
3Ease of operation
If patients capture images without professional assistance, then ease of operation improves, but image quality and reliability may deteriorate
Solution Approach 1:
The system enables self-service by allowing patients to capture their own dental arch images using standard mobile devices without requiring professional assistance. The deep learning system is designed to process these patient-captured images directly, eliminating the need for professional image acquisition while maintaining diagnostic reliability through robust image analysis algorithms.
Solution Approach 2:
The patent implements beforehand cushioning by incorporating image quality assessment and preprocessing steps within the deep learning pipeline. The system anticipates variable image qualities from patient-captured photos and compensates through automated image enhancement, noise reduction, and quality filtering before analysis, ensuring reliable results despite non-professional capture conditions.
Data Source
AI summary
A method for analyzing an image, called “analysis image”, of a dental arch of a patient. The method in which the analysis image is submitted to a neural network, in order to determine at least one value of an image attribute relating to the analysis image. The analysis image is a photograph or an image taken from a film. The image attribute relates to a position or an orientation or a calibration of an acquisition apparatus used to acquire the analysis image or a combination thereof, or a quality of the analysis image, and in particular relating to the brightness, to the contrast or to the sharpness of the analysis image, or a combination thereof.