Machine Learning Models in the Detection of MB2 Canal Orifice in CBCT Images
The success rates (AUC, precision, recall) of ML algorithms in the detection of MB2 were remarkable in our study. It was also observed that when the target variable was specified, significant success rates such as 86.8% in precision and 92.5% in recall were achieved. The present study showed promising results in the ML-based detection of MB2 canal using axial CBCT slices.
Authors
- Shishir Shetty (ORCID: https://orcid.org/0000-0002-8097-6024)
- Saad W. Albayatti
- Mohammed Amjed Alsaegh (ORCID: https://orcid.org/0000-0003-1986-0855)
- Raghavendra Shetty (ORCID: https://orcid.org/0000-0003-1209-7295)
- Leena R. David (ORCID: https://orcid.org/0000-0001-5604-4764)
- Dilber Uzun Ozsahin (ORCID: https://orcid.org/0000-0002-3873-1410)
- Hiba Al-Daghestani (ORCID: https://orcid.org/0000-0002-8558-8725)
- İlker Özşahin (ORCID: https://orcid.org/0000-0002-3141-6805)
- Sangeetha Narasimhan (ORCID: https://orcid.org/0000-0002-8912-6368)
- Meliz Yuvalı
- Renita Castelino
Institutions
- Nitte University (IN)
- Ajman University (AE)
- University of Sharjah (AE)
- Near East University (CY)
Publication Details
- Journal
- International Dental Journal
- Published
- 2025-03-27
- DOI
- https://doi.org/10.1016/j.identj.2025.02.025
- Citations
- 5
- Primary Topic
- Dental Radiography and Imaging
- Type
- article
- Field-Weighted Citation Impact
- 8.95