Technology clinically validated by experts

To develop and validate a novel artificial intelligence (AI) tool for automated segmentation of mandibular incisive canal on cone beam computed tomography (CBCT) scans.

To train and validate a cloud-based convolutional neural network (CNN) model for automated segmentation (AS) of dental implant and attached prosthetic crown on cone-beam computed tomography (CBCT) images.

Convolutional neural network-based automated maxillary alveolar bone segmentation on cone-beam computed tomography images.

Accurate mandibular canal (MC) detection is crucial to avoid nerve injury during surgical procedures. Moreover, the anatomic complexity of the interforaminal region requires a precise delineation of anatomical variations such as the anterior loop (AL). Therefore, CBCT-based presurgical planning is recommended, even though anatomical variations and lack of MC cortication make canal delineation challenging. To overcome these limitations, artificial intelligence (AI) may aid presurgical MC delineation

Automated detection and labelling of teeth and small edentulous regions on cone-beam computed tomography using convolutional neural networks

AI and AR technologies can be successfully used in guided implant surgery for authentic 3D planning that may replace conventional software.