Clinical Validation of Deep Learning for Segmentation of Multiple Dental Features in Periapical Radiographs

Periapical radiographs are routinely used in dental practice for diagnosis and treatment planning purposes. However, they often suffer from artifacts, distortions, and superimpositions, which can lead to potential misinterpretations. Thus, an automated detection system is required to overcome these challenges. Artificial intelligence (AI) has been revolutionizing various fields, including medicine and dentistry, by facilitating the development of intelligent systems that can aid in performing complex tasks such as diagnosis and treatment planning. The purpose of the present study was to verify the diagnostic performance of an AI system for the automatic detection of teeth, caries, implants, restorations, and fixed prosthesis on periapical radiographs. A dataset comprising 1000 periapical radiographs collected from 500 adult patients was analyzed by an AI system and compared with annotations provided by two oral and maxillofacial radiologists. A strong correlation (R > 0.5) was observed between AI perception and observers 1 and 2 in carious teeth (0.7-0.73), implants (0.97-0.98), restored teeth (0.85-0.89), teeth with fixed prosthesis (0.92-0.94), and missing teeth (0.82-0.85). The automatic detection by the AI system was comparable to the oral radiologists and may be useful for automatic identification in periapical radiographs.