Human-AI Collaboration for Mandibular Canal Tracing Accuracy on Cone-beam Computed Tomography: A Multi-Evaluator Study

This is the authors' abstract. We don't add key points for this paper.

Objectives To evaluate the accuracy, efficiency, and reliability of manual, artificial intelligence (AI)-driven, and AI-assisted tracing methods for locating the mandibular canal (MC) on cone-beam computed tomography (CBCT) across clinical scenarios and evaluators. Methods Phase 1 established a calibration reference standard using a dry human mandible. In Phase 2, five evaluators assessed ten CBCT scans using manual and AI-assisted methods, while one expert performed all methods. The dataset included a range of anatomical variations and alterations. MC tracings were evaluated using Dice Similarity Coefficient (DSC) and Hausdorff Distance (HD95), and boundary precision differences was analyzed with a linear mixed-effects model. Accuracy, efficiency, and agreement were assessed. Results A total of 292 MC tracings were evaluated. Manual tracing showed excellent intra-evaluator consistency (DSC: 0.99, HD95: 1.11) but low inter-evaluator agreement (ICC: 0.30). AI-assisted methods demonstrated near-perfect intra-evaluator reliability (DSC: 0.81, HD95: 2.2) and good inter-evaluator agreement (ICC: 0.72, DSC: 0.91, HD95: 1.23). Manual tracing showed the highest accuracy (DSC: 0.98) and was the slowest (6.2 min). The AI-driven method showed strong accuracy (DSC: 0.87) and was the fastest (13-20 s). The AI-assisted method balanced speed (4.2 min) and accuracy (DSC: 0.83), with superior boundary precision compared to manual ( p = 3×10 -4 ) and AI-driven methods ( p = 1.0). Conclusion Manual tracing remains the most accurate method, time-intensive, and variable across evaluators. AI-driven tracing improved efficiency but was less reliable in complex cases. AI-assisted tracing balanced accuracy and efficiency, improved boundary precision, and reduced variability, supporting its role in clinical decision-making. Clinical Relevance The AI-assisted mandibular canal tracing method provides an effective balance between accuracy and efficiency. This reduces inter-user variability and improves reliability across users, even in complex anatomical cases.