Development and evaluation of an AI model for dental implant type detection: A comparison of diagnostic accuracy between a deep learning model and dental professionals
A high-performing model identified implant brands on periapical radiographs and outperformed clinicians across experience levels. Comparative analysis across YOLO architectures validated its measurable advantage in accuracy and speed. Lack of external validation and dataset imbalance are important limitations; future work will include external, multisite data and human-AI workflow evaluation.
Authors
- Abeer Ahmed (ORCID: https://orcid.org/0000-0002-8251-7147)
- Amr Ahmed Azhari (ORCID: https://orcid.org/0000-0002-8749-4714)
- Zainab Majed Alsadah
- Khaled Ahmed Fawaz (ORCID: https://orcid.org/0000-0002-2368-1545)
- Walaa Magdy Ahmed (ORCID: https://orcid.org/0000-0003-1810-8733)
- Anas Lahiq (ORCID: https://orcid.org/0009-0004-8767-1313)
- Abdulrahman Almufti
Institutions
- Cairo University (EG)
- King Abdulaziz University (SA)
- Riyadh Elm University (SA)
- King Fahad Hospital Jeddah (SA)
- Asir Central Hospital (SA)
- Artificial Intelligence in Medicine (Canada) (CA)
Publication Details
- Journal
- Journal of Prosthodontics
- Published
- 2025-11-26
- DOI
- https://doi.org/10.1111/jopr.70064
- Primary Topic
- Dental Radiography and Imaging
- Type
- article
- Field-Weighted Citation Impact
- 0.00