Accuracy of AI in Detecting Bifid Mandibular Canal on CBCT: A Diagnostic Accuracy Study

The goal of this observational study is to evaluate how accurately a deep learning-based artificial intelligence (AI) model can detect and segment bifid mandibular canals (BMCs) on cone-beam computed tomography (CBCT) scans in Egyptian patients. This condition is a key anatomical variation that, if missed, may cause surgical complications such as nerve injury. The study uses previously collected CBCT scans of individuals aged 15 and older from the Oral and Maxillofacial Radiology Department at Cairo University. The scans will be analyzed retrospectively. The main questions it aims to answer are: How closely does the AI model's segmentation of the mandibular canal match the expert manual segmentation? How accurate is the AI model in identifying the presence or absence of bifid mandibular canals? Participants are not actively involved. Instead, anonymized CBCT data will be analyzed using the AI model and compared to expert annotations to measure diagnostic performance.