ArticleBioengineering (Basel, Switzerland)2024
Clinical Validation of Deep Learning for Segmentation of Multiple Dental Features in Periapical Radiographs.
Article in Bioengineering (Basel, Switzerland), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Multi-class anatomical landmark detection in periapical radiographs with deep learning.Odontology · 2026Article
- AI-Based Detection of Periapical Lesions in Endodontic Radiographs: a Diagnostic Accuracy Study.Maedica · 2026Article
- FDA-Approved AI Solutions in Dental Imaging: A Narrative Review of Applications, Evidence, and Outlook.International dental journal · 2026Review
- Deep learning approach for tooth numbering and restoration detection on pediatric periapical radiographs in mixed dentition.Clinical oral investigations · 2025Article
- Deep learning-based segmentation of caries, implants, fixed prosthesis, and restorations on bitewing radiographs: A retrospective study.Science progressArticle
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Authors and funding
12 authors.
Funding
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Abstract
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.
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