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.

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.