Skel-Net: automatic prediction of skeletal pattern on scanned lateral cephalograms using anatomical prior-guided deep learning network
By leveraging anatomic priors and longitudinal data, Skel-Net enables dynamic and personalized predictions of craniofacial growth. This framework will facilitate early and precise orthodontic interventions, enhancing treatment efficiency, stability, and overall patient outcomes.
Authors
- Won-Jin Yi (ORCID: https://orcid.org/0000-0002-5977-6634)
- Su Yang (ORCID: https://orcid.org/0000-0002-7876-1462)
- Eun Sun Song
- Seung‐Pyo Lee
Institutions
- Seoul National University (KR)
- National University College (PR)
Publication Details
- Journal
- BMC Oral Health
- Published
- 2025-10-31
- DOI
- https://doi.org/10.1186/s12903-025-06771-z
- Primary Topic
- Orthodontics and Dentofacial Orthopedics
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- National Research Foundation
- Ministry of Trade, Industry and Energy
- National Research Foundation of Korea
- Ministry of Science and ICT, South Korea