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

Rattan Khurana, Archana, Kanish Aggarwal, Sharvari Bhat, Afrah Fatima, Abida Parveen, Heena Dixit

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Article in Bioinformation, 2025. 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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7 authors.

Rattan KhuranaDepartment of Orthodontics and Dentofacial Orthopaedics, Gian Sagar Dental College and Hospital, Ram Nagar (Rajpura), Patiala, Punjab, India.

ArchanaDepartment of Orthodontics and dentofacial orthopedic, HKDETS dental college and hospital humnabad, Bidar, Karnataka, India.

Kanish AggarwalDepartment of Orthodontics, Bhojia Dental College and Hospital, Baddi, Himachal Pradesh, India.

Sharvari BhatConsultant Orthodontist, the Dental Residence, Pune, Maharashtra, India.

Afrah FatimaDepartment of Orthodontics, Panineeya Institute of Dental Sciences and Research Centre, Hyderabad, Telangana, India.

Abida ParveenDepartment of Orthodontics and Dentofacial Orthopaedics, MMCDSR, Maharishi Markandeshwar (Deemed-to-be University), Mullana, Ambala, Haryana, India.

Heena DixitDepartment of Medical Health Administration, Index Institute, Malwanchal University, Index City, Nemawar Road, Indore, Madhya Pradesh, India.

Funding

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6 · The paper itself

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.

Indexed as

artificial intelligenceclinical decision-makingOrthodontic alignersorthodonticstreatment outcome prediction

Identifiers

PMID41908026

DOI10.6026/973206300214753

PMCPMC13018322

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LicenceCC BY

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