AI Palatal Expansion Planning for Predictable Orthodontic Outcomes
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing palatal expansion treatments lack a systematic approach for predicting and adjusting the outcome of palatal expansion in patients, particularly in terms of amount, placement, and force application, which can vary significantly among individuals.
Innovation Solution
A method and system utilizing AI models to process craniofacial structure data, generate visualizations of predicted outcomes, and allow for real-time adjustments to palatal expansion plans, incorporating incremental expanders and 3D modeling for precise treatment planning.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional palatal expansion treatment is used without AI prediction, then treatment planning is simple, but treatment outcome predictability and personalization are poor
Solution Approach 1:
The patent replaces traditional manual treatment planning with an AI-based prediction system that processes craniofacial structure data to generate personalized treatment outcomes. The AI model substitutes the mechanical/manual planning process with an automated intelligent system that can predict treatment results with higher precision.
Solution Approach 2:
The system changes the approach by introducing multiple parameters for treatment customization including amount of expansion, vertical clearance measurements, placement of attachments, and transverse force application. These parameters are processed through the AI model to generate personalized treatment plans, transforming the planning process from binary decisions to multi-parameter optimization.
2Reliability
If personalized treatment parameters are customized for each patient, then treatment efficacy is improved, but treatment planning time and complexity increase
Solution Approach 1:
The AI model performs preliminary processing of craniofacial structure data and generates treatment predictions before the actual treatment begins. By pre-calculating and visualizing expected outcomes, the system eliminates the need for time-consuming iterative planning during treatment, allowing dentists to quickly adjust parameters based on pre-generated insights.
Solution Approach 2:
The system creates a virtual 3D copy of the patient's craniofacial structure and simulates treatment outcomes on this digital model rather than requiring physical trial-and-error planning. This virtual copying allows rapid generation of personalized treatment scenarios without time-consuming physical adjustments or multiple consultation cycles.
3Adaptability or versatility
If AI models are used to process craniofacial data and generate predictions, then treatment personalization is enhanced, but system complexity and data processing requirements increase
Solution Approach 1:
The AI model is designed to handle multiple treatment scenarios and parameter combinations within a single unified system. It can process various craniofacial data types, generate different treatment predictions, and accommodate multiple customization parameters, making the system universally applicable to diverse patient cases without requiring separate specialized tools for each function.
Solution Approach 2:
The patent introduces an intermediary visualization interface that translates complex AI predictions into intuitive 3D visual representations. This intermediary layer bridges the gap between the complex AI processing and the dentist's decision-making, making the system more manageable by presenting processed information in a user-friendly format that requires minimal technical expertise to interpret and apply.
4Manufacturing precision
If multiple treatment parameters are adjusted and visualized in real-time, then treatment precision is improved, but computational processing requirements and system resources increase
Solution Approach 1:
The system performs computational processing selectively rather than continuously - it processes data and generates visualizations only when treatment parameters are modified or when predictions are needed. This partial action approach maintains high precision for critical treatment decisions while reducing overall computational resource consumption by avoiding unnecessary continuous processing of all parameters.
Data Source
AI summary
A method provides a palatal expansion management tool. The method includes receiving scan data of a craniofacial structure of a patient, providing the scan data as input to an artificial intelligence (AI) model trained to provide a recommended palatal expansion treatment plan for the patient; receiving, as output from the AI model, the recommended palatal expansion treatment plan; and providing, to a user device, the recommended palatal expansion treatment plan.