Comparative analysis of five AI platforms for mandibular canal segmentation on CBCT images.
Authors
Affiliations (7)
Affiliations (7)
- King Abdullah International Medical Research Center, Department of Maxillofacial Surgery & Diagnostic Sciences, College of Dentistry, King Saud Bin Abdulaziz University for Health Sciences, Ministry of National Guard Health Affairs, Riyadh, Kingdom of Saudi Arabia. Electronic address: [email protected].
- King Abdullah International Medical Research Center, College of Dentistry, King Saud Bin Abdulaziz University for Health Sciences, Ministry of National Guard Health Affairs, Riyadh, Kingdom of Saudi Arabia.
- King Abdullah International Medical Research Center, Department of Preventive Dental Sciences, College of Dentistry, King Saud Bin Abdulaziz University for Health Sciences, Ministry of National Guard Health Affairs, Riyadh, Kingdom of Saudi Arabia.
- King Abdullah International Medical Research Center, Department of Maxillofacial Surgery & Diagnostic Sciences, College of Dentistry, King Saud Bin Abdulaziz University for Health Sciences, Ministry of National Guard Health Affairs, Riyadh, Kingdom of Saudi Arabia.
- King Abdullah International Medical Research Center, Department of Restorative & Prosthetic Dental Sciences, College of Dentistry, King Saud Bin Abdulaziz University for Health Sciences, Ministry of National Guard Health Affairs, Riyadh, Kingdom of Saudi Arabia.
- Department of Dentomaxillofacial Radiology, Faculty of Dentistry, Ankara University, Ankara, Turkey.
- Department of Oral Medicine, Faculty of Dentistry, Mansoura University, Mansoura, Egypt.
Abstract
Accurate mandibular canal (MC) identification on cone-beam computed tomography (CBCT) is vital to prevent inferior alveolar nerve injury during oral and maxillofacial procedures. Manual segmentation is time-consuming and operator-dependent, while artificial intelligence (AI) offers automated, reproducible alternatives. This study compared the accuracy of automated MC segmentation across five AI platforms using standardized quantitative and qualitative evaluations. A total of 120 anonymized CBCT scans (240 MCs) were analyzed using five fully automated AI-based segmentation platforms: Atomica (Atomica AI, USA), BlueSkyPlan (Blue Sky Bio, USA), Craniocatch (Craniocatch, Türkiye), 3D Slicer (open-source, USA), and Relu Creator (Relu BV, Belgium). Expert-annotated models served as reference. Accuracy was quantified as unsigned mean surface deviation and categorized as optimal (<0.5 mm), acceptable (0.5-2.0 mm), or unacceptable (>2.0 mm). Qualitative evaluation employed a five-point anatomical fidelity scale. Segment-wise, laterality, and scanner-wise effects were also assessed. Significant performance differences were observed among platforms (p < 0.001). Relu Creator and 3D Slicer achieved the highest overall accuracy (≈0.5 mm) with no >2.0 mm deviations in the complete-canal analysis. Craniocatch showed moderate accuracy, while Atomica and BlueSkyPlan exhibited greater variability and more deviations > 2.0 mm. Qualitative scores reflected similar trends. Regionally, middle canal segments showed the best accuracy, with higher deviations near the mandibular and mental foramina. Scanner- and side-related effects were statistically significant but clinically negligible. AI-based MC segmentation accuracy varies across platforms. Relu Creator and 3D Slicer achieved near-expert performance suitable for clinical use, while others require expert verification. Independent benchmarking and multi-scanner validation are essential for safe implementation. This study provides evidence-based guidance on the accuracy of AI tools for automated MC segmentation, supporting safer surgical planning by identifying which AI-generated outputs can be trusted and where expert verification remains essential to prevent nerve injury.