AI-Based Anatomical Landmark Recognition for Robot-Assisted Dynamic Dental Implant Surgery

This paper presents the development of an AI-based anatomical landmark recognition framework designed for robot-assisted dynamic dental implant surgery. The proposed framework integrates a YOLOv11 deep learning model with a RealSense D405 RGB-D (Red, Green, Blue and Depth) camera, mounted on a collaborative robot, to detect predefined dental landmarks in real time. These 2D detections are projected into 3D space using depth information and subsequently registered to a preoperative STL model derived from CBCT (Cone Beam Computed Tomography) scans using Unity. The implant position, defined in the planning phase relative to anatomical landmarks, is localized intraoperatively without the use of fiducial markers and projected on the STL model of the mandible/maxilla.