# AI-enhanced 3D tooth movement forecasting in clear aligner therapy using deep morphometric modelling: A prospective validation study

## TL;DR

This study shows that AI can predict early issues in clear aligner therapy by analyzing 3D scans, helping improve treatment planning.

## Contribution

A deep morphometric AI model is developed and validated for forecasting early tracking deviations in clear aligner therapy.

## Key findings

- The model achieved 85% accuracy in predicting early aligner tracking deviation.
- It had an RMSE of 0.19 mm for clinically significant deviations.

## Abstract

Clear aligner therapy often encounters early tracking deviations that compromise treatment efficiency, creating a need for predictive tools
that identify risk at the outset. Therefore, it is of interest to develop and validate a deep morphometric AI model capable of forecasting
early aligner tracking deviation using baseline and first-week 3D intraoral scans. Hence, a prospective sample of 40 adults was analyzed
using a graph-convolutional neural network trained on geometric mesh features extracted from sequential scans. The model demonstrated strong
performance, achieving 85% accuracy with an RMSE of 0.19 mm in predicting clinically significant early deviation. Thus, we show that AI-driven
morphometric analysis offers a promising approach for early risk detection and improved treatment planning in clear aligner therapy.

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Source: https://tomesphere.com/paper/PMC13018322