From inconsistent annotations to ground truth: Aggregation strategies for annotations of proximal carious lesions in dental imagery
Most AI applications for dental image analysis are trained on a single annotation, usually resulting from aggregated multi-examiner annotations of each image. However, since these annotations are usually aggregated in an in vivo setting where no definitive ground truth is available, the choice of aggregation strategy plays a crucial role.
Authors
- Antonín Tichý (ORCID: https://orcid.org/0000-0002-6260-9992)
- Gerd Göstemeyer
- Falk Schwendicke (ORCID: https://orcid.org/0000-0003-1223-1669)
- M. Büttner
- Noah F. Nordblom (ORCID: https://orcid.org/0000-0002-2705-4194)
- Vera Lúcia Mota Klein
- Sarina Rolle
Institutions
- Charles University (CZ)
- Berlin Institute of Health at Charité - Universitätsmedizin Berlin (DE)
- Charité - Universitätsmedizin Berlin (DE)
- Ludwig-Maximilians-Universität München (DE)
Publication Details
- Journal
- Journal of Dentistry
- Published
- 2025-04-01
- DOI
- https://doi.org/10.1016/j.jdent.2025.105728
- Citations
- 8
- Primary Topic
- Dental Radiography and Imaging
- Type
- article
- Field-Weighted Citation Impact
- 14.32
Funders
- Deutsche Forschungsgemeinschaft