Developing an AI-powered tool for radiographic feedback on working length determination in pre-clinical endodontic training
The AI-powered feedback system demonstrated high accuracy with strong user acceptance. By delivering instant, constructive feedback on working length determination, it supports effective learning and skill refinement in endodontic education. It is also beneficial in classrooms with large student populations. Future work will expand the dataset and integrate additional stages of root canal training into a unified AI-based educational platform.
Authors
- Walid El‐Shafai (ORCID: https://orcid.org/0000-0001-7509-2120)
- Sanaa Aljamani (ORCID: https://orcid.org/0000-0001-6592-4923)
- Abdulaziz AlHaddad
- Rawan Abu Zaghlan (ORCID: https://orcid.org/0000-0001-9542-3126)
- Mousa AL-Akhras
- Iman AlMomani
Institutions
- University of Jordan (JO)
- Prince Sultan University (SA)
- Jordan Hospital (JO)
- King Abdullah University Hospital (JO)
- Intelligent Systems Research (United States) (US)
- Menoufia University (EG)
Publication Details
- Journal
- Frontiers in Dental Medicine
- Published
- 2026-02-27
- DOI
- https://doi.org/10.3389/fdmed.2026.1730454
- Primary Topic
- Dental Research and COVID-19
- Type
- article
- Field-Weighted Citation Impact
- 0.00