Developing an Artificial Intelligence Solution to Autosegment the Edentulous Maxillary Bone for Implant Planning

Objectives: Digital dental implant planning using panoramic radiographs and cone beam computed tomography (CBCT) imaging is labor-intensive and prone to error due to clinician fatigue, limited digital expertise, and time constraints. Artificial intelligence (AI) offers a promising solution by automating image analysis. This study aimed to develop a deep learning system for segmenting edentulous maxillary ridges to support automated implant planning. Materials and Methods: A total of 209 CBCT scans were retrieved from the University Dental Hospital Sharjah image archive (Romexis, Planmeca), of which 77 met the inclusion criteria. Manual segmentation was performed using 3D Slicer software and reviewed by a third examiner. A convolutional neural network (CNN) based on the U-Net architecture was developed using the Medical Open Network for AI (MONAI) framework. The dataset was split into training (90%) and testing (10%) sets. Statistical Analysis: Model performance was evaluated using the Dice Similarity Coefficient (DSC). Low-scoring cases (DSC