Artificial Intelligence in Maxillofacial Radiology: A Bibliometric Study

¹Assistant Professor, Department of Oral and Maxillofacial Radiology, Faculty of Dentistry, University of Beykent, Buyukcekmece, Istanbul, Turkey

²Assistant Professor, Department of Oral and Maxillofacial Radiology, Faculty of Dentistry, Bezmialem Vakif University, Fatih, Istanbul, Turkey

³Research Assistant, Department of Oral and Maxillofacial Radiology, Faculty of Dentistry, Bezmialem Vakif University, Fatih, Istanbul, Turkey

*Corresponding Author: Elifhan ALAGOZ, Department of Oral and Maxillofacial Radiology, Bezmialem Vakif University, Vatan Street, Adnan Menderes Avenue, 34093, Fatih, Istanbul, Turkey; Tel No: +90 542 342 59 82; e-mail: [email protected]; [email protected]; [email protected]

Received Date: January 17, 2023

Publication Date: January 31, 2023

Citation: Salli GA, et al. (2023). Artificial Intelligence in Maxillofacial Radiology: A Bibliometric Study. Mathews J Dentistry. 7(1):33.

Copyright: Salli GA, et al. © (2023)

ABSTRACT

Purpose: The aim of this study is to evaluate the global trend in Artificial İntelligence (AI) research involving maxillofacial radiology and query a large database for a comprehensive analysis for those wishing to do more research in this area. Materials & Methods: All publication searches were performed using the PubMed databases. From January 1989 to March 2022, all AI-related publications were selected using following search terms: “artificial intelligence dental radiology”, “deep learning dental radiology”,  “machine learning dental radiology”, “Convolutional Neural Network dental radiology”,  “neural network dental radiology”. Totally, 971 articles were found, 732 articles were excluded, 239 articles were included and analyzed for the specified bibliometric criterias. Then including publications were categorized by country of origin, institution, type of article, journal name, impact factor of journal, subspecialties, study design, publication year, number of citation, AI tecnic and imaging modality. Statistical analysis was performed using IBM SPSS Statistics version 28.0(IBM, Chicago, IL). Results: According to results, an increase was observed in the number of publications over the years, the most publications were made in 2021(100) and the most publications from Korea (48). The institution that conducts the most studies on AI is Charité-Universitätsmedizin (8.44%) and the journal in which the studies are published the most is Dentomaxillofacial radiology (24%). The most cited publication was the Korean study published in 2018, with 283 citations. Panoramic radiograph (75) was the most used imaging technique, and CNN (99) was used from AI techniques. Conclusion: This analysis provides researchers with a comprehensive overview of AI-related research in maxillofacial radiology, providing guidance for future studies.

Keywords: Bibliometric analysis, Artificial Intelligence in maxillofacial radiology, CNN, Deep learning, Machine learning