Clinical and MRI markers for acute vs chronic temporomandibular disorders using a machine learning and deep neural networks
Behavioral and TMJ-related structural factors are key predictors of chronic TMD and may aid early identification. Timely recognition may support personalized strategies and improve outcomes.
Authors
- Q‐Schick Auh (ORCID: https://orcid.org/0000-0002-5570-326X)
- Yung‐Kyun Noh (ORCID: https://orcid.org/0000-0002-6372-9267)
- Jeong Hoon Lee (ORCID: https://orcid.org/0000-0002-1789-8270)
- Dohoon Kim (ORCID: https://orcid.org/0000-0001-6370-9744)
- Yeon‐Hee Lee (ORCID: https://orcid.org/0000-0001-7323-0411)
- Seonggwang Jeon
Institutions
- Korea Institute for Advanced Study (KR)
- Kyung Hee University Dental Hospital (KR)
- Center for Systems Biology (US)
- Hanyang University (KR)
- Stanford University (US)
Publication Details
- Journal
- Communications Medicine
- Published
- 2025-09-29
- DOI
- https://doi.org/10.1038/s43856-025-01081-5
- Citations
- 1
- Primary Topic
- Temporomandibular Joint Disorders
- Type
- article
- Field-Weighted Citation Impact
- 2.14
Funders
- Seoul National University
- Kyung Hee University