Title
Authors
Affiliation
1Department of Orthodontics & Dentofacial Orthopedics, Sri Ramakrishna Dental College & Hospital, Coimbatore, India; 2Department of Orthodontics and Dentofacial Orthopaedics, NIMS Dental College & Hospital, NIMS University, Rajasthan, Jaipur, India; 3Department of Orthodontics and Dentofacial Orthopaedics, Manav Rachna Dental College, Faridabad, India; 4Department of Periodontology, The Oxford Dental College, Bengaluru, Karnataka, India; 5Department of Prosthodontics, Kalinga Institute of Dental Sciences, KIIT Deemed To Be University, Bhubaneswar, Odisha, India; 6Department of Orthodontics and Dentofacial Orthopaedics, Narsinhbhai Patel Dental College and Hospital, Sankalchand Patel University, Visnagar, India; *Corresponding author
K Ushanandhini - E-mail: nandhiniusha1247@gmail.com
Ashish Kumar - E-mail: drashish055@gmail.com
Abhita Malhotra - E-mail: abhita2387@hotmail.com
G. Kavyashree - E-mail: drkavyashreegowda@gmail.com
Esther Lhingneihoi Mate - E-mail: estherlhingneihoimate@gmail.com
Shobhit Saxena - E-mail: shobhit.fds@spu.ac.in
Article Type
Research Article
Date
Received February 1, 2026; Revised February 28, 2026; Accepted February 28, 2026, Published February 28, 2026
Abstract
Accurate prediction of three-dimensional tooth movement remains a major challenge in orthodontic treatment planning, with conventional methods showing error rates of 30-50% for complex movements. Therefore, it is of interest to develop and evaluate an artificial intelligence model for predicting orthodontic tooth movement using digital treatment records and intraoral scan data. A deep learning framework combining convolutional neural networks and recurrent neural networks was trained on 4,218 orthodontic cases comprising 892,476 individual tooth movement records across multiple treatment stages. The model achieved an overall prediction accuracy of 91.3%, with a mean absolute error of 0.24 mm for linear movement and 1.87° for angular movement, significantly outperforming traditional prediction approaches (p = 0.001). Thus, we show that AI-based tooth movement prediction can enhance orthodontic treatment planning accuracy, reduce chairside time and improve overall clinical outcomes.
Keywords
Artificial Intelligence, orthodontic tooth movement, deep learning, treatment prediction, digital orthodontics
Citation
Ushanandhini et al. Bioinformation 22(2): 675-682 (2026)
Edited by
Hiroj Bagde
ISSN
0973-2063
Publisher
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.
