Radiographic Data Segmentation as a Tool in Machine Learning and Deep Learning Artificial Intelligence Algorithms.
Authors
Affiliations (5)
Affiliations (5)
- Oral and Maxillofacial Medicine and Diagnostic Sciences, Case Western Reserve University School of Dental Medicine, 9601 Chester Avenue, Office #245C, Cleveland, OH 44106, USA. Electronic address: [email protected].
- Inonu University Faculty of Dentistry Department of Oral and Maxillofacial Radiology, Elazig road 15. km Battalgazi/Malatya 44280, Türkiye.
- Texas A & M University, College of Dentistry, 3302 Gaston Avenue, Dallas, TX 75246, USA.
- Eskisehir Osmangazi University Faculty of Dentistry Department of Oral and Maxillofacial Radiology, Meselik campus, Eskisehir 26040, Turkey.
- Penn Dental Medicine, University of Pennsylvania, Robert Schattner Center, Suite # 214, 240 S 40th Street, Philadelphia, PA 19104, USA.
Abstract
This study reviews radiographic data segmentation as a cornerstone of machine learning (ML) and deep learning (DL) in dentistry. After outlining artificial intelligence (AI), ML, and DL concepts, it highlights convolutional neural networks-driven tasks-classification, detection, and pixel/voxel segmentation-across panoramic, periapical, bitewing, and cone beam computed tomography imaging. Automated tooth numbering, restoration and implant labeling, caries delineation, endodontic morphology and fractures, periapical and periodontal lesions, and peri-implant bone loss show strong performance metrics, often matching or surpassing clinicians while markedly accelerating workflows. The study underscores AI's potential to improve accuracy and efficiency while maintaining essential human oversight.