Application of artificial intelligence-based detection of furcation involvement in mandibular first molar using cone beam tomography images- a preliminary study
The deep learning model (ResNet101V2) can accurately detect the FI in axial CBCT images. However, since our study was preliminary in nature and carried out with relatively smaller dataset, a study with larger dataset will further confirm the accuracy of deep learning models.
Authors
- Shishir Shetty (ORCID: https://orcid.org/0000-0002-8097-6024)
- Sausan Al Kawas
- Wael Talaat (ORCID: https://orcid.org/0000-0002-5784-3784)
- Waad Kheder (ORCID: https://orcid.org/0000-0001-6938-9913)
- Zaid Hamdoon (ORCID: https://orcid.org/0000-0001-8334-4519)
- Leena R. David (ORCID: https://orcid.org/0000-0001-5604-4764)
- Anirudh B. Acharya (ORCID: https://orcid.org/0000-0002-2062-5791)
- Dilber Uzun Ozsahin (ORCID: https://orcid.org/0000-0002-3873-1410)
- Natheer Al‐Rawi (ORCID: https://orcid.org/0000-0002-7483-6594)
Institutions
- Gulf Medical University (AE)
- University of Sharjah (AE)
Publication Details
- Journal
- BMC Oral Health
- Published
- 2024-12-04
- DOI
- https://doi.org/10.1186/s12903-024-05268-5
- Citations
- 11
- Primary Topic
- Dental Radiography and Imaging
- Type
- article
- Field-Weighted Citation Impact
- 5.43