Literature DB >> 33197209

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

Min-Suk Heo1, Jo-Eun Kim2, Jae-Joon Hwang3, Sang-Sun Han4, Jin-Soo Kim5, Won-Jin Yi1, In-Woo Park6.   

Abstract

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.

Entities:  

Keywords:  Artificial Intelligence; Dentistry; Radiology

Mesh:

Year:  2020        PMID: 33197209      PMCID: PMC7923066          DOI: 10.1259/dmfr.20200375

Source DB:  PubMed          Journal:  Dentomaxillofac Radiol        ISSN: 0250-832X            Impact factor:   2.419


  53 in total

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Review 3.  Convolutional neural networks for dental image diagnostics: A scoping review.

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4.  Detection and diagnosis of dental caries using a deep learning-based convolutional neural network algorithm.

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Journal:  J Dent       Date:  2018-07-26       Impact factor: 4.379

5.  Evaluation of an artificial intelligence system for detecting vertical root fracture on panoramic radiography.

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Journal:  Oral Radiol       Date:  2019-09-18       Impact factor: 1.852

6.  Automatic diagnosis for cysts and tumors of both jaws on panoramic radiographs using a deep convolution neural network.

Authors:  Odeuk Kwon; Tae-Hoon Yong; Se-Ryong Kang; Jo-Eun Kim; Kyung-Hoe Huh; Min-Suk Heo; Sam-Sun Lee; Soon-Chul Choi; Won-Jin Yi
Journal:  Dentomaxillofac Radiol       Date:  2020-07-03       Impact factor: 2.419

Review 7.  Clinical applications and performance of intelligent systems in dental and maxillofacial radiology: A review.

Authors:  Ravleen Nagi; Konidena Aravinda; N Rakesh; Rajesh Gupta; Ajay Pal; Amrit Kaur Mann
Journal:  Imaging Sci Dent       Date:  2020-06-18

8.  Dental age estimation using the pulp-to-tooth ratio in canines by neural networks.

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Journal:  Imaging Sci Dent       Date:  2019-03-25

9.  DeNTNet: Deep Neural Transfer Network for the detection of periodontal bone loss using panoramic dental radiographs.

Authors:  Jaeyoung Kim; Hong-Seok Lee; In-Seok Song; Kyu-Hwan Jung
Journal:  Sci Rep       Date:  2019-11-26       Impact factor: 4.379

10.  Artificial intelligence in bone age assessment: accuracy and efficiency of a novel fully automated algorithm compared to the Greulich-Pyle method.

Authors:  Christian Booz; Ibrahim Yel; Julian L Wichmann; Sabine Boettger; Ahmed Al Kamali; Moritz H Albrecht; Simon S Martin; Lukas Lenga; Nicole A Huizinga; Tommaso D'Angelo; Marco Cavallaro; Thomas J Vogl; Boris Bodelle
Journal:  Eur Radiol Exp       Date:  2020-01-28
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  8 in total

1.  The crucial role of dentomaxillofacial radiology for AI research in dental medicine - why it's time for our specialty to lead the way!

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2.  Deep learning for automated detection and numbering of permanent teeth on panoramic images.

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3.  A fully automated method of human identification based on dental panoramic radiographs using a convolutional neural network.

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Review 4.  Where Is the Artificial Intelligence Applied in Dentistry? Systematic Review and Literature Analysis.

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Journal:  Healthcare (Basel)       Date:  2022-07-08

5.  Canal-Net for automatic and robust 3D segmentation of mandibular canals in CBCT images using a continuity-aware contextual network.

Authors:  Bo-Soung Jeoun; Su Yang; Sang-Jeong Lee; Tae-Il Kim; Jun-Min Kim; Jo-Eun Kim; Kyung-Hoe Huh; Sam-Sun Lee; Min-Suk Heo; Won-Jin Yi
Journal:  Sci Rep       Date:  2022-08-05       Impact factor: 4.996

6.  Comparing the effectiveness of two diagnostic approaches for the interpretation of oral radiographic lesions by dental students.

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7.  Automatic Classification System for Periapical Lesions in Cone-Beam Computed Tomography.

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Review 8.  Digital surgery for gastroenterological diseases.

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  8 in total

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