Title

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

Authors

Rattan Khurana¹, Archana², Kanish Aggarwal³, Sharvari Bhat⁴, Afrah Fatima⁵, Abida Parveen^6,7 & Heena Dixit⁸

Affiliation

¹Department of Orthodontics and Dentofacial Orthopaedics, Gian Sagar Dental College and Hospital, Ram Nagar (Rajpura), Patiala, Punjab, India; ²Department of Orthodontics and Dentofacial Orthopedic, HKDETS dental college and hospital humnabad, Bidar, Karnataka, India; ³Department of Orthodontics, Bhojia Dental College and Hospital, Baddi, Himachal Pradesh, India; ⁴Consultant Orthodontist, the Dental Residence, Pune, Maharashtra, India; ⁵Department of Orthodontics, Panineeya Institute of Dental Sciences and Research Centre, Hyderabad, Telangana, India; ⁶Department of Orthodontics and Dentofacial Orthopaedics, MMCDSR, Maharishi Markandeshwar (Deemed-to-be University), Mullana, Ambala, Haryana, India; ⁷Department of Dentistry, Sheikh Bhikhari Medical College, Hazaribagh, Jharkhand, India; ⁸Department of Medical Health Administration, Index Institute, Malwanchal University, Index City, Nemawar Road, Indore, Madhya Pradesh, India; *Corresponding author

Email

Rattan Khurana - E-mail: khuranarattan1306@gmail.com
Archana- E-mail: archanahebli24@gmail.com
Kanish Aggarwal - E-mail: kanishaggarwal@gmail.com
Sharvari Bhat - E-mail: sharvaribhat@gmail.com
Afrah Fatima - E-mail: af.fatima3@gmail.com
Abida Parveen - E-mail: p.abida2191@gmail.com
Heena Dixit - E-mail: drheenatiwari@gmail.com

Article Type

Research Article

Date

Received November 15, 2025; Revised December 15, 2025; Accepted December 15, 2025, Published December 15, 2025

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.

Keywords

Orthodontic aligners, artificial intelligence, clinical decision-making, treatment outcome prediction, orthodontics

Citation

Khurana et al. Bioinformation 21(12): 4753-4755 (2025)

Edited by

Rashmi Daga

ISSN

0973-2063

Publisher

Biomedical Informatics

License

This is an Open Access article which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. This is distributed under the terms of the Creative Commons Attribution License.