Morphotype stratification of radix entomolaris in mandibular molars
CBCT-derived features, particularly canal volume and curvature, effectively predict RE complexity. The integration of these features into machine learning models provides a clinically valuable framework for morphotype classification and personalized endodontic planning. These findings support the adoption of AI-assisted diagnostics in managing anatomically complex root canal systems.
Authors
- Suganya Panneer Selvam (ORCID: https://orcid.org/0000-0002-9272-6444)
- Krishnasamy Nitya (ORCID: https://orcid.org/0000-0001-6094-1206)
- Ramya Ramadoss (ORCID: https://orcid.org/0000-0001-9408-3158)
- Shruti Vishal Dev (ORCID: https://orcid.org/0000-0002-0186-6143)
- Sandhya Sundar (ORCID: https://orcid.org/0000-0001-5085-8337)
- K Hema Shree
Institutions
- Saveetha University (IN)
Publication Details
- Journal
- Journal of Oral Biology and Craniofacial Research
- Published
- 2025-10-15
- DOI
- https://doi.org/10.1016/j.jobcr.2025.10.001
- Citations
- 1
- Primary Topic
- Forensic Entomology and Diptera Studies
- Type
- article
- Field-Weighted Citation Impact
- 1.67