Neural Network Tooth Type Identification from 3D Data
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Solution Overview
Problem
The accuracy of identifying tooth types using three-dimensional camera data varies significantly among operators due to differences in knowledge levels, particularly for teeth with similar shapes such as central incisors, lateral incisors, canines, and molars.
Innovation Solution
An identification device utilizing a neural network-based estimation model that receives three-dimensional data from a scanner, extracts features, and outputs accurate tooth type identification, reducing reliance on operator knowledge.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If operator identification based on three-dimensional image is used, then device complexity is reduced, but measurement precision varies depending on operator knowledge level
Solution Approach 1:
The patent replaces the manual identification process (relying on operator knowledge and experience) with an automated AI-based system. The neural network model processes three-dimensional data automatically, substituting human expertise with computational algorithms that provide consistent and objective tooth type identification without varying by operator skill level.
Solution Approach 2:
The system enables self-service identification where the AI model independently performs tooth type classification without requiring operator intervention or expertise. The neural network autonomously analyzes three-dimensional data and provides identification results, allowing the system to serve itself rather than relying on human operators for the critical analysis task.
2Device complexity
If manual tooth type identification is used, then device complexity is low, but reliability varies among operators
Solution Approach 1:
The patent substitutes manual identification operations with an automated neural network-based system. This replacement ensures that identification results are not influenced by operator variability, experience levels, or subjective judgment, thereby significantly improving reliability and consistency across different users and occasions.
Solution Approach 2:
The system incorporates feedback mechanisms where the neural network continuously processes three-dimensional data and provides identification results that can be verified and refined. The model learns from training data and can adapt to different tooth types and variations, ensuring consistent and reliable identification performance.
3Measurement precision
If AI-based identification is implemented, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The patent introduces an intermediary neural network model that sits between the three-dimensional data input and the final identification output. This intermediary component processes and analyzes the complex three-dimensional data, extracting relevant features and patterns to enable accurate tooth type identification while managing system complexity through a dedicated processing layer.
Solution Approach 2:
The system utilizes parameter changes in the neural network model, including adjusting network architecture, training parameters, and data processing parameters, to optimize identification accuracy. By carefully tuning these parameters and using pre-trained models, the system achieves high precision while controlling the complexity of the AI component.
Data Source
AI summary
An identification device that identifies a type of a tooth includes: an input unit that receives three-dimensional data including three-dimensional position information at each of a plurality of points forming the tooth; an identification unit that identifies a type of the tooth based on the three-dimensional data received by the input unit and an estimation model including a neural network; and an output unit that outputs an identification result obtained by the identification unit. The identification unit directly inputs the three-dimensional position information included in the three-dimensional data received by the input unit to the neural network included in the estimation model.