Development of an AI model to predict tooth movement during orthodontic treatment.

Ushanandhini K, Ashish Kumar, Abhita Malhotra, Kavyashree G, Esther Lhingneihoi Mate, Shobhit Saxena

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Article in Bioinformation, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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6 authors.

Ushanandhini KDepartment of Orthodontics & Dentofacial Orthopedics, Sri Ramakrishna Dental College & Hospital, Coimbatore, India.

Ashish KumarDepartment of Orthodontics and Dentofacial Orthopaedics, NIMS Dental College & Hospital, NIMS University, Rajasthan, Jaipur, India.

Abhita MalhotraDepartment of Orthodontics and Dentofacial Orthopaedics, Manav Rachna Dental College, Faridabad, India.

Kavyashree GDepartment of Periodontology, The Oxford Dental College, Bengaluru, Karnataka, India.

Esther Lhingneihoi MateDepartment of Prosthodontics, Kalinga Institute of Dental Sciences, KIIT Deemed To Be University, Bhubaneswar, Odisha, India.

Shobhit SaxenaDepartment of Orthodontics and Dentofacial Orthopaedics, Narsinhbhai Patel Dental College and Hospital, Sankalchand Patel University, Visnagar, India.

Funding

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6 · The paper itself

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°C 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.

Indexed as

Artificial Intelligencedeep learningdigital orthodonticsorthodontic tooth movementtreatment prediction

Identifiers

PMID42109326

DOI10.6026/973206300220675

PMCPMC13150245

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