Automatic feature segmentation in dental panoramic radiographs
Rohan Jagtap, Yalamanchili Samata, Amisha Parekh, Pedro Tretto, Tamara Vujanovic, Purnachandrarao Naik, Jason Griggs, Alan Friedel, Maxine Feinberg, Prashant Jaju, Michael D. Roach, Mini Suri, Michelle Briner GarridoObjective
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 panoramic radiography.
Methods
This is a cross-sectional study. A dataset comprising 1000 panoramic radiographs collected from 500 adult patients was analyzed by an AI system and compared with annotations provided by two oral and maxillofacial radiologists.
Results
A strong correlation (R > 0.5) was observed between AI perception and observers 1 and 2 in carious teeth (0.691–0.878), implants (0.770–0.952), restored teeth (0.773–0.834), teeth with fixed prostheses (0.972–0.980), and missing teeth (0.956–0.988).
Discussion
Panoramic radiographs are commonly used for diagnosis and treatment planning. However, they often suffer from artifacts, distortions, and superimpositions, leading to potential misinterpretations. Thus, an automated detection system is required to tackle these challenges. Artificial intelligence (AI) has revolutionized various fields, including dentistry, by enabling the development of intelligent systems that can assist in complex tasks such as diagnosis and treatment planning.
Conclusion
The automatic detection by the AI system was comparable to oral radiologists and may be useful for automatic identifications in panoramic radiographs. These findings signify the potential for AI systems to enhance diagnostic accuracy and efficiency in dental practices, potentially reducing the likelihood of diagnostic errors caused by unexperienced professionals.