Deep learning–based automated assessment of alveolar bone loss in CBCT for periodontitis

Agreement between artificial intelligence and periodontists in the detection of periodontal bone loss on panoramic radiographs

The application of deep learning in intelligent assessment of horizontal bone loss in mandibular first molars

AI-Guided Cbct Diagnosis of Oral Lesions for Periodontal and Prosthodontic Planning

Cone-beam computed tomography (CBCT) provides 3D assessment of oral lesions and periodontal/peri-implant defects, but its interpretation is time-consuming and observer dependent. Thus the aim of our study was to evaluate the diagnostic accuracy and clinical utility of an AI-guided CBCT analysis system for detecting ora...

Deep Learning-Based Automated Detection and Geometric Severity Assessment of Open Gingival Embrasures in Intraoral Photographs: A Multicentre Study.

The proposed approach provides objective OGE assessment from clinical intraoral photographs and may support future remote orthodontic monitoring after further validation with patient-acquired images.

Automated Tooth Numbering and Periodontal Bone Loss Severity Detection Using Residual U-Net and Tooth Long Axis in Panoramic Radiography

Periodontitis is a major cause of tooth loss, and its assessment from panoramic radiographs remains subjective. This study proposes an integrated framework for automatic tooth numbering and periodontal bone loss severity assessment using a residual U-Net architecture. The framework performs cemento-enamel junction (CEJ...

Automatic classification of alveolar bone defects via 3D CBCT for dental implant preoperative evaluation

Accurate assessment of alveolar bone defects is critical to the success of dental implant surgery, as it directly influences preoperative planning and surgical outcomes. However, the clinically adopted Terheyden classification method relies heavily on dentists' subjective judgment of Cone-Beam Computed Tomography (CBCT...