A Comparative Analysis of DeepDentalNet: An AI-Assisted Voxel-Based Segmentation of Sinus, Mandibular Canal, and Missing Tooth Regions in CBCT-Based Implant Planning

Dental implant planning is a critical aspect of restorative dentistry that requires precise preoperative assessment of bone structures to ensure optimal implant placement and long-term success. Traditional methods rely heavily on manual interpretation of Cone Beam Computed Tomography (CBCT) scans and are sometimes time-intensive and susceptible to inter-observer variability. In this study, a deep learning-based approach using a 3D UNET model with residual connections is proposed to automate the segmentation of key anatomical structures, including the missing tooth bone region, sinus, and mandibular canal. The proposed model was trained using a combination of Dice Loss and Binary Cross-Entropy (BCE) Loss, ensuring accurate segmentation. The model achieved Dice Similarity Coefficients (DSC) of 94.1% for the missing tooth region, 91.8% for the maxillary sinus, and 92.5% for the mandibular canal, demonstrating high segmentation accuracy. Additionally, bone length and breadth were measured automatically. The proposed AI-driven methodology streamlines implant planning, enhances efficiency, and reduces manual workload, offering a promising tool for clinical applications.