The influence of a deep learning tool on the performance of oral and maxillofacial radiologists in the detection of apical radiolucencies
AI did not significantly enhance radiologists' overall diagnostic accuracy. However, it showed potential to enhance efficiency, particularly advantageous for non-expert clinicians. The expertise of radiologists remains vital for accuracy, underscoring the complementary role of AI in dental diagnostics.
Authors
- Lyudmila Tuzova
- Zaid Badr (ORCID: https://orcid.org/0000-0002-5638-3288)
- Dmitry Tuzoff (ORCID: https://orcid.org/0000-0002-3852-8471)
- André Mol
- Donald A. Tyndall (ORCID: https://orcid.org/0000-0002-5373-1814)
- Sergio Uribe (ORCID: https://orcid.org/0000-0003-0684-2025)
- Manal Hamdan (ORCID: https://orcid.org/0000-0003-4081-7675)
Institutions
- Marquette University (US)
- Georgia Institute of Technology (US)
- University of North Carolina Health Care (US)
- Riga Technical University (LV)
- St. Petersburg Department of Steklov Institute of Mathematics (RU)
- LMU Klinikum (DE)
- Riga Stradiņš University (LV)
- Ontario Brain Institute (CA)
- Ludwig-Maximilians-Universität München (DE)
Publication Details
- Journal
- Dentomaxillofacial Radiology
- Published
- 2024-12-04
- DOI
- https://doi.org/10.1093/dmfr/twae054
- Citations
- 5
- Primary Topic
- Dental Radiography and Imaging
- Type
- article
- Field-Weighted Citation Impact
- 2.47