Automated Tooth Administration via Spline and Gap Detection
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Solution Overview
Problem
Current dental restoration workflows require manual input of tooth numbers and positions, which are time-consuming and error-prone, especially for new dental professionals, and lack automation in the recognition and calculation of tooth proposals.
Innovation Solution
A method and system that automates the tooth administration phase using a spline forming module, interdental gap detection, and machine learning engines to propose tooth number probabilities and compute the best fit tooth number distribution, eliminating the need for manual input and enhancing accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual input process is used for tooth numbers and positions, then dental professionals can control the restoration design, but the process becomes time-consuming and error-prone
Solution Approach 1:
The system performs automatic recognition and calculation of tooth proposals without requiring manual input from dental professionals. The automated tooth administration system processes 3D scan data, detects interdental gaps, and generates tooth number assignments independently, eliminating the time-consuming manual entry process while maintaining accuracy through algorithmic analysis
Solution Approach 2:
The manual mechanical process of inputting tooth numbers and positions is replaced with an automated computational system. The system uses 3D scanning data, machine learning models, and algorithms to automatically determine tooth proposals, substituting the manual administrative process with an automated digital workflow that reduces both time and human error
2Productivity
If automated tooth administration is implemented, then the process becomes faster and more accurate, but the system complexity increases
Solution Approach 1:
The automated tooth administration system is divided into distinct functional modules: 3D scanning module, interdental gap detection module, machine learning analysis module, and tooth proposal generation module. Each module handles a specific aspect of the process, making the complex system manageable and maintainable while achieving high productivity through parallel processing and specialized algorithms
Solution Approach 2:
The automated system is designed to handle multiple dental scenarios and tooth types through a unified platform. The machine learning models are trained on diverse datasets to recognize various interdental gap patterns, tooth morphologies, and anatomical variations, allowing the system to maintain high productivity across different clinical cases without requiring separate manual procedures for each scenario
Data Source
AI summary
A method and system to automate an administration and restoration generation process that includes forming a spline along a jaw, proposing potential interdental gaps, weighting the potential interdental gaps to obtain one or more delimiters, automatically proposing tooth number probabilities, and computing a best fit tooth number distribution to generate a patient specific restoration.


