Palatal Expansion Prediction Using Lower Arch Final Position

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Solution Overview

Problem

Existing palatal expansion treatments lack a systematic approach for predicting and adjusting the outcome based on individual patient craniofacial structures, leading to suboptimal treatment plans.

Innovation Solution

A method utilizing AI models to process craniofacial data and generate visualizations of predicted palatal expansion outcomes, allowing for adjustments and personalized treatment plans, including 3D and 2D model displays, and incremental expander fabrication.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Manufacturing precision

If traditional palatal expansion treatment is used without AI-based prediction, then the treatment process is simple, but the treatment outcome precision and personalization are insufficient

Engineering Contradiction:

Improvetreatment outcome precisionVSAvoidsystem complexity

Core Design Contradiction:

Manufacturing precisionVSDevice complexity

Solution Approach 1:

An AI-based prediction system is introduced as an intermediary between the treatment planning phase and the actual palatal expansion treatment. This system processes patient-specific craniofacial data, predicts treatment outcomes, and enables precise customization of expansion parameters, thereby achieving high precision without requiring complex manual intervention in the treatment execution phase

Inventive Principle:

Principle #24Intermediary (Mediator)

Solution Approach 2:

The traditional manual or experience-based treatment planning process is replaced with an automated AI prediction system. The system uses machine learning models to analyze craniofacial structures and generate personalized treatment parameters, substituting mechanical/manual planning with intelligent automation to achieve superior precision

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

2Adaptability or versatility

If AI-based prediction system is implemented, then treatment personalization and precision are improved, but data processing complexity and computational requirements increase

Engineering Contradiction:

Improvetreatment personalizationVSAvoiddata processing complexity

Core Design Contradiction:

Adaptability or versatilityVSDevice complexity

Solution Approach 1:

The system processes patient-specific craniofacial parameters such as bone density, jaw structure dimensions, and tooth positioning to generate customized treatment predictions. By transforming complex anatomical data into actionable treatment parameters, the system achieves high adaptability while managing data processing complexity through structured parameter transformation

Inventive Principle:

Principle #35Parameter changes

Solution Approach 2:

The data processing pipeline is segmented into distinct stages: data collection from multiple sources, data cleaning and normalization, AI-based prediction modeling, and treatment parameter generation. This segmentation allows each stage to be optimized independently, reducing overall processing complexity while maintaining high personalization capability

Inventive Principle:

Principle #1Segmentation

3Reliability

If incremental palatal expanders are used with AI prediction, then treatment effectiveness is enhanced, but the number of treatment stages and time required increase

Engineering Contradiction:

Improvetreatment effectivenessVSAvoidtreatment duration

Core Design Contradiction:

ReliabilityVSDuration of action of moving object

Solution Approach 1:

The AI prediction system performs preliminary analysis of the patient's craniofacial structure and predicts the optimal treatment pathway before treatment begins. This preliminary action identifies the minimum necessary treatment stages and parameters needed to achieve the desired outcome, thereby enhancing effectiveness while minimizing treatment duration through data-driven optimization

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The system incorporates feedback mechanisms where treatment progress is continuously monitored and compared against AI predictions. This feedback loop allows for real-time adjustment of treatment parameters and staging, ensuring optimal effectiveness while reducing unnecessary treatment time through adaptive optimization based on actual patient response

Inventive Principle:

Principle #23Feedback

Data Source

PatentUS20260069380A1Automated palatal expansion based on lower arch final position

Publication Date: 2026.03.12 ALIGN TECHNOLOGY INC

AI summary

A method provides automated palatal expansion treatment planning based on lower arch final position. The method includes identifying a plurality of reference points within one or more three-dimensional (3D) dental models of a patient; determining one or more tooth measurements based on the plurality of reference points; determining, based on the one or more tooth measurements, a recommended palatal expansion amount for a palatal expansion treatment plan for the patient; and providing, to a user device, the recommended palatal expansion amount.