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Artificial intelligence in oral and maxillofacial radiology: what is currently possible?

 Min-Suk Heo  ;  Jo-Eun Kim  ;  Jae-Joon Hwang  ;  Sang-Sun Han  ;  Jin-Soo Kim  ;  Won-Jin Yi  ;  In-Woo Park 
 DENTOMAXILLOFACIAL RADIOLOGY, Vol.50(3) : 20200375, 2021-03 
Journal Title
Issue Date
Artificial Intelligence* ; Humans ; Neural Networks, Computer ; Radiography ; Radiologists ; Radiology*
Artificial Intelligence ; Dentistry ; Radiology
Artificial intelligence, which has been actively applied in a broad range of industries in recent years, is an active area of interest for many researchers. Dentistry is no exception to this trend, and the applications of artificial intelligence are particularly promising in the field of oral and maxillofacial (OMF) radiology. Recent researches on artificial intelligence in OMF radiology have mainly used convolutional neural networks, which can perform image classification, detection, segmentation, registration, generation, and refinement. Artificial intelligence systems in this field have been developed for the purposes of radiographic diagnosis, image analysis, forensic dentistry, and image quality improvement. Tremendous amounts of data are needed to achieve good results, and involvement of OMF radiologist is essential for making accurate and consistent data sets, which is a time-consuming task. In order to widely use artificial intelligence in actual clinical practice in the future, there are lots of problems to be solved, such as building up a huge amount of fine-labeled open data set, understanding of the judgment criteria of artificial intelligence, and DICOM hacking threats using artificial intelligence. If solutions to these problems are presented with the development of artificial intelligence, artificial intelligence will develop further in the future and is expected to play an important role in the development of automatic diagnosis systems, the establishment of treatment plans, and the fabrication of treatment tools. OMF radiologists, as professionals who thoroughly understand the characteristics of radiographic images, will play a very important role in the development of artificial intelligence applications in this field.
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2. College of Dentistry (치과대학) > Dept. of Oral and Maxillofacial Radiology (영상치의학교실) > 1. Journal Papers
Yonsei Authors
Han, Sang Sun(한상선) ORCID logo https://orcid.org/0000-0003-1775-7862
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