Use of artificial intelligence for detection of MB2 canals in maxillary first molars on CBCT: a systematic review and meta-analysis
AI, particularly deep learning, shows promise in detecting MB2 canals on CBCT. While current evidence is preliminary, standardised AI training and reporting protocols, together with larger multicenter studies, are needed to validate these tools. Clinically, AI could serve as a supplementary aid to improve diagnostic consistency and reduce missed canals during endodontic treatment.
Authors
- Kimia Baghaei (ORCID: https://orcid.org/0000-0002-3759-2113)
- Farshad Khosraviani (ORCID: https://orcid.org/0000-0001-6304-4770)
- Zohaib Khurshid (ORCID: https://orcid.org/0000-0001-7998-7335)
- Thanaphum Noom Osathanon (ORCID: https://orcid.org/0000-0003-1649-6357)
- Mahjube Entezar-e-Ghaem (ORCID: https://orcid.org/0000-0002-3273-3175)
- Mahmood Dashti (ORCID: https://orcid.org/0000-0003-2013-0374)
- Niloofar Ghadimi (ORCID: https://orcid.org/0000-0001-9189-721X)
- Sara Esmaeili (ORCID: https://orcid.org/0009-0005-0877-7695)
Institutions
- University of Alberta (CA)
- Chulalongkorn University (TH)
- Başkent University (TR)
- Shahid Sadoughi University of Medical Sciences and Health Services (IR)
- Islamic Azad University Medical Branch of Tehran (IR)
- Research Institute for Endocrine Sciences (IR)
- King Faisal University (SA)
- Shahid Beheshti University (IR)
- Shahid Beheshti University of Medical Sciences (IR)
Publication Details
- Journal
- BMC Oral Health
- Published
- 2025-12-01
- DOI
- https://doi.org/10.1186/s12903-025-07254-x
- Citations
- 1
- Primary Topic
- Endodontics and Root Canal Treatments
- Type
- article
- Field-Weighted Citation Impact
- 1.79