Comparison Of Accuracy Of Mandibular Canal Segmentation Tool Using Manual Method And Artificial Intelligence In Cone Beam Computed Tomography - Jeevitha S, Jebarani; Sitaraman, Prasanthi (Journal of Neonatal Surgery, 2025)
Background: Cone Beam Computed Tomography (CBCT) has become an essential imaging modality in the visualization of the maxillofacial region, gradually replacing traditional CT due to its superior imaging capabilities and reduced radiation exposure. Accurate assessment of the mandibular canal (MC) is vital for preoperative planning to avoid complications during surgical procedures. Artificial Intelligence (AI)-driven tools, such as nerve canal segmentation software, offer rapid and precise tracing of the Inferior Alveolar Nerve Canal (IANC), extending from the mandibular foramen to the mental foramen. This study aims to evaluate and compare the accuracy of AI-driven segmentation with manual tracing methods to assess their reliability and potential for clinical applications. Materials And Methods: This study included 100 large field-of-view (16 × 17 cm) CBCT scans taken for routine screening, acquired using the CS 9600 machine with exposure parameters of 120 KVp, 5 mA, and 24 s. The CBCT scans were analysed using CS imaging software. The inclusion criteria focused on scans from individuals over 18 years of age without any pathologies, fractures, or prior surgeries affecting the mandible. Exclusion criteria included individuals under 18 years of age, patients with syndromes affecting mandibular growth, and post – mandibular surgeries. The mandibular canal was traced using an AI-driven nerve canal segmentation tool and the results were compared with manual tracings performed by two observers with varying levels of experience (2 and 6 years). The overlap of the traced canals was categorized as complete, partial, or no overlap. Additionally, the diameter of the traced canal was assessed, with AI-driven tools defaulting to 2.5 mm. Screenshots of the traced canals in reconstructed panoramic views were used for comparison, and transparency adjustments were applied to evaluate overlaps. Results: The AI-driven segmentation demonstrated high accuracy, with 94% of cases showing complete overlap between AI and manual tracings, 5% showing partial overlap, and only 1% displaying no overlap. Morphological assessment revealed consistency in tracing quality across most cases. The AI tool maintained the default canal diameter of 2.5 mm with no significant deviations in 98% of cases. The remaining 2% exhibited minor variations, highlighting potential limitations in specific cases with unique anatomical variations. These findings underline the reliability of AI tools in accurately identifying and tracing the mandibular canal, with results comparable to manual methods. Conclusion: The study demonstrates that AI-driven nerve canal segmentation tools offer a highly accurate and time-efficient approach for assessing the mandibular canal, with results closely aligning with manual tracings. While AI tools showed minimal discrepancies in a small percentage of cases, they hold significant promise as a complementary tool in preoperative planning and diagnostic workflows. However, further research incorporating larger datasets and varied imaging parameters is essential to optimize the use of AI tools in clinical practice.