Artificial intelligence for detection and classification of furcation defects using radiographic imaging: A systematic review
AI algorithms, particularly advanced deep learning models applied to well-annotated 2D radiographs, show promise for accurate furcation defect detection. Nonetheless, the field exhibits methodological and reporting heterogeneity. Future research should prioritize standardized protocols, direct comparisons with clinicians, and development of clinically translatable AI tools to improve early and accurate diagnosis of furcation involvement.
Authors
- Eleftherios G. Kaklamanos (ORCID: https://orcid.org/0000-0002-0513-5110)
- Lazaros Tsalikis (ORCID: https://orcid.org/0000-0002-2916-3511)
- Vasiliki P. Koidou (ORCID: https://orcid.org/0000-0003-3944-0233)
- Georgios S. Chatzopoulos (ORCID: https://orcid.org/0000-0002-6951-8794)
Institutions
- University of Minnesota (US)
- Queen Mary University of London (GB)
- European University Cyprus (CY)
- Aristotle University of Thessaloniki (GR)
- Mohammed Bin Rashid University of Medicine and Health Sciences (AE)
Publication Details
- Journal
- Imaging Science in Dentistry
- Published
- 2025-01-01
- DOI
- https://doi.org/10.5624/isd.20250101
- Citations
- 3
- Primary Topic
- Advanced X-ray and CT Imaging
- Type
- article
- Field-Weighted Citation Impact
- 2.02