An Artificial Intelligence System for Staging the Spheno‐Occipital Synchondrosis
This study introduces a novel artificial intelligence-based pipeline that reliably automates the classification of the SOS fusion stages using advanced deep learning models, with the highest accuracy achieved by ConvNext + Conv Attention. These models enhance the efficiency, scalability and consistency of SOS staging while minimising manual intervention from the clinician, underscoring the potential for AI-driven solutions in orthodontics and clinical workflows.
Authors
- Halil Ayyıldız (ORCID: https://orcid.org/0000-0001-8633-1764)
- Lauren Mills
- Marouane Tliba (ORCID: https://orcid.org/0000-0002-1696-7764)
- Mohammed H. Elnagar (ORCID: https://orcid.org/0000-0002-2332-9250)
- Omid Halimi Milani
- Ahmet Enis Çetin (ORCID: https://orcid.org/0000-0002-3449-1958)
- Rashid Ansari (ORCID: https://orcid.org/0000-0001-6477-6578)
- Veerasathpurush Allareddy (ORCID: https://orcid.org/0000-0001-8199-6206)
- Amanda Nikho
Institutions
- Illinois College (US)
- Tanta University (EG)
- Kütahya Dumlupınar Üniversitesi (TR)
- University of Illinois Chicago (US)
- Kutahya Saglik Bilimleri Universitesi
Publication Details
- Journal
- Orthodontics and Craniofacial Research
- Published
- 2025-09-02
- DOI
- https://doi.org/10.1111/ocr.70018
- Primary Topic
- Dental Radiography and Imaging
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- American Association of Orthodontists Foundation