Dental Arch Deep Learning for Non-Visible Structure Analysis
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Solution Overview
Problem
Existing methods for analyzing non-visible parts of the mouth, such as embedded teeth and jaw bones, are limited by health risks from X-ray exposure and lack precision in predicting future dental changes, leading to delayed diagnoses and unnecessary treatments.
Innovation Solution
A deep learning method using a neural network to analyze dental arch representations, incorporating a learning base of historical dental structures, to determine attributes of both visible and non-visible dental objects, enhancing the orthodontist's information and predicting future changes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If X-ray tomographic acquisition is used to acquire information on non-visible parts of the mouth, then measurement precision of non-visible dental structures is improved, but object-affected harmful factors increase due to health risks from repeated X-ray exposure
Solution Approach 1:
The patent creates a digital 3D copy of the dental arch surface through optical scanning, which serves as a substitute for X-ray imaging. This digital model allows visualization and analysis of dental structures without exposing the patient to ionizing radiation, thereby maintaining measurement capability while eliminating harmful effects
Solution Approach 2:
The patent replaces the X-ray imaging system with an optical scanning system. Instead of using electromagnetic radiation (X-rays) to capture dental structure information, the system uses optical fields to create detailed surface models, substituting a harmful physical mechanism with a safe one
2Measurement precision
If conventional X-ray apparatus is used for tomographic acquisition, then measurement precision of non-visible dental parts is improved, but device complexity increases
Solution Approach 1:
The patent uses optical scanning to create a digital replica of the dental arch, replacing complex X-ray tomographic equipment with simpler optical sensors and processing systems. This digital copying approach achieves sufficient measurement precision for dental analysis while dramatically reducing device complexity
3Ease of operation
If only visible parts of the mouth are examined, then ease of operation is improved, but loss of information increases regarding non-visible dental structures
Solution Approach 1:
The patent transitions from 2D visual inspection to 3D digital modeling, adding a spatial dimension that reveals hidden dental structures. The optical scanning system captures depth information and creates three-dimensional representations, allowing examination of non-visible areas without complicating the operational process
4Reliability
If X-ray based tomographic acquisition is performed, then reliability of diagnosis is improved, but loss of time increases due to late diagnosis when patient complains of pain
Solution Approach 1:
The patent enables preliminary examination of dental structures using safe optical scanning before problems develop. By creating comprehensive 3D models during routine checkups, the system allows early detection of potential issues without waiting for pain symptoms, thereby improving reliability while preventing time loss associated with delayed diagnosis
Data Source
AI summary
Method of analysis of a diagnostic dental representation showing a dental arch of a current patient in several dimensions. The method includes creation of a learning base including more than 1,000 historical dental structures. Each historical dental structure includes a historical dental representation showing an arch of a historical patient in several dimensions and a historical specification containing a value for at least a first attribute relating to a dental object associated with the historical dental representation. The method includes training of at least one deep learning device by use of the learning base. The method includes submission of the diagnostic dental representation to the deep learning device in such a manner that it determines, for the diagnostic dental representation, at least one value for the first attribute.