The Effectiveness of Deep Learning-Based Convolutional Neural Networks for Automated Detection of the Inferior Alveolar Nerve Canal on Cone Beam Computed Tomography (CBCT) Scans: A Systematic Review of Accuracy, Reliability, and Clinical Applicabilit

Background: Precise identification of the Inferior Alveolar Nerve Canal (IANC) on the Cone Beam Computed Tomography (CBCT) is essential to avoid surgical complications. Manual identification is labor-intensive and operator-dependent. Deep learning-based Convolutional Neural Networks (CNNs) hold promise, yet their clinical usefulness is unknown. Objective: To systematically assess the effectiveness, reliability, and clinical usefulness of CNNs for IANC detection on CBCT scans, emphasizing limitations and directions for future research. Methods: A systematic review of PubMed, Science Direct, and Google Scholar was performed for studies published between 2014 and 2024. All studies that applied CNNs for the detection of IANC were included, measuring the most important metrics like accuracy, sensitivity, and specificity. Bias was determined with the Risk of Bias 2 (RoB2) tool, and data regarding CNN architecture, dataset size, and performance were extracted. Two reviewers selected the studies and extracted the data independently, and disagreements were resolved by a third reviewer. Inter-reviewer agreement was determined by using Cohen’s Kappa. Results: Nine studies using U-Net, ResNet, and DenseNet achieved high accuracy (85%–97%), sensitivity (84%–95%), and specificity (85%–96%), better than manual detection. Small sample sizes, dataset constraints, and risk of overfitting compromised generalizability. Conclusion: CNNs enhance IANC detection accuracy and reliability but need dataset heterogeneity, standardized metrics for evaluation, and external validation for clinical use.