Multi-Regional deep learning models for identifying dental restorations and prosthesis in panoramic radiographs
This research provides compelling evidence supporting the practicality and effectiveness of AI-driven automated dental restoration identification from panoramic radiographs, offering a highly promising and efficient solution for enhancing clinical diagnostic accuracy and streamlining workflows in dental practice. This AI, achieving 94.15% accuracy on diverse panoramic radiographs, automates the detection of numerous dental restorations. It provides compelling evidence for AI's practicality in significantly enhancing diagnostic precision and streamlining clinical workflows, offering a highly efficient tool for modern dental practice and improved patient record management.
Authors
- Zohaib Khurshid (ORCID: https://orcid.org/0000-0001-7998-7335)
- Shehzad Hasan (ORCID: https://orcid.org/0000-0001-9792-4661)
- Vorapat Trachoo (ORCID: https://orcid.org/0000-0002-1478-1122)
- Onanong Silkosessak (ORCID: https://orcid.org/0000-0001-6275-3671)
- Thantrira Porntaveetus (ORCID: https://orcid.org/0000-0003-0145-9801)
- Maria Waqas (ORCID: https://orcid.org/0000-0002-8054-5128)
- Fatima Faridoon
Institutions
- NED University of Engineering and Technology (PK)
- Chulalongkorn University (TH)
- Riphah International University (PK)
- King Faisal University (SA)
Publication Details
- Journal
- BMC Oral Health
- Published
- 2025-11-05
- DOI
- https://doi.org/10.1186/s12903-025-07138-0
- Citations
- 3
- Primary Topic
- Dental Radiography and Imaging
- Type
- article
- Field-Weighted Citation Impact
- 5.37
Funders
- Chulalongkorn University
- Health Systems Research Institute
- Thailand Science Research and Innovation