Machine learning versus clinicians for detection and classification of oral mucosal lesions
By providing performance comparable to oral surgeons and superior to general dentists, the developed multi-class model could support the clinical evaluation of oral lesions, potentially enabling earlier diagnosis of potentially malignant disorders, enhancing patient management and improving patient prognosis.
Authors
- Ghazal Aarabi (ORCID: https://orcid.org/0000-0001-5484-2594)
- Antonín Tichý (ORCID: https://orcid.org/0000-0002-6260-9992)
- Yoana Malenova
- Ekaterina V. Tolstaya (ORCID: https://orcid.org/0000-0002-8893-2683)
- Akhilanand Chaurasia (ORCID: https://orcid.org/0000-0002-8356-9512)
- Falk Schwendicke (ORCID: https://orcid.org/0000-0003-1223-1669)
- Sebastian Paris (ORCID: https://orcid.org/0000-0002-1302-8761)
- David Steybe (ORCID: https://orcid.org/0000-0003-3885-6659)
- Julia Schwärzler
Institutions
- University of Geneva (CH)
- Universität Hamburg (DE)
- King George's Medical University (IN)
- LMU Klinikum (DE)
- Institute for Sports Medicine (DE)
- University Medical Center Hamburg-Eppendorf (DE)
- Istanbul Technical University (TR)
- Charité - Universitätsmedizin Berlin (DE)
- Ludwig-Maximilians-Universität München (DE)
Publication Details
- Journal
- Journal of Dentistry
- Published
- 2025-07-20
- DOI
- https://doi.org/10.1016/j.jdent.2025.105992
- Citations
- 7
- Primary Topic
- Oral Health Pathology and Treatment
- Type
- article
- Field-Weighted Citation Impact
- 10.26