Haematologic Data Improves Long‐Term Prediction Accuracy of Artificial Intelligence Models for Temporomandibular Disorders
This study showed the potential of machine learning models in predicting long-term TMD prognosis using clinical and haematological features. In addition, these findings highlight the importance of including both subjective pain assessments and systemic haematologic markers for the development of aetiology-based diagnostic systems for TMD to enhance clinical decision-making and prognosis prediction accuracy.
Authors
- Changhee Joo (ORCID: https://orcid.org/0000-0003-1690-2298)
- Il‐San Cho
- Taegun An
- Ji Woon Park (ORCID: https://orcid.org/0000-0002-0625-7021)
- Moon Jong Kim (ORCID: https://orcid.org/0000-0002-7961-4616)
Institutions
- Seoul National University (KR)
- Korea University (KR)
- Seoul National University Dental Hospital (KR)
Publication Details
- Journal
- Journal of Oral Rehabilitation
- Published
- 2025-05-14
- DOI
- https://doi.org/10.1111/joor.14030
- Primary Topic
- Temporomandibular Joint Disorders
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- Korea University
- Ministry of Science and ICT, South Korea
- Institute for Information and Communications Technology Promotion