Automatic feature segmentation in dental panoramic radiographs.

Autor:	Jagtap R; Division of Oral & Maxillofacial Radiology, Department of Care Planning & Restorative Sciences, University of Mississippi Medical Center School of Dentistry, Jackson, MS, USA., Samata Y; Department of Oral Medicine and Radiology, SIBAR Institute of Dental Sciences, Guntur, AP, India., Parekh A; Department of Biomedical Materials Science, School of Dentistry, University of Mississippi Medical Center, Jackson, MS, USA., Tretto P; Department of Oral Surgery, Regional Integrated University of Alto Uruguai and Missions, Erechim, Brazil., Vujanovic T; Southeast A Regional Representative, American Association for Dental, Oral and Craniofacial Research National Student Research Group President, Local Chapter of Student Research Group. Dental Student, UMMC School of Dentistry Class of 2025, University of Mississippi Medical Center, Jackson, MS, USA., Naik P; Department of Oral Medicine and Radiology, SIBAR Institute of Dental Sciences, Guntur, AP, India., Griggs J; Department of Biomedical Materials Science, School of Dentistry, University of Mississippi Medical Center, Jackson, MS, USA., Friedel A; VELMENI Inc., Sunnyvale, CA, USA., Feinberg M; VELMENI Inc., Sunnyvale, CA, USA., Jaju P; Department of Oral Medicine and Radiology, Rishiraj College of Dental Sciences & Research Centre, Bhopal, MP, India., Roach MD; Department of Biomedical Materials Science, School of Dentistry, University of Mississippi Medical Center, Jackson, MS, USA., Suri M; VELMENI Inc., Sunnyvale, CA, USA., Garrido MB; Department of Oral Pathology, Radiology and Medicine, Kansas City School of Dentistry, University of Missouri, Kansas City, MO, USA.
Jazyk:	angličtina
Zdroj:	Science progress [Sci Prog] 2024 Oct-Dec; Vol. 107 (4), pp. 368504241286659.
DOI:	10.1177/00368504241286659
Abstrakt:	Objective: 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. Competing Interests: Declaration of conflicting interestsThe authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.
Databáze:	MEDLINE
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