Imaging Science in Dentistry

Korean Academy of Oral and Maxillofacial Radiology (대한영상치의학회)
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An overview of deep learning in the field of dentistry

  • Hwang, Jae-Joon (Department of Oral and Maxillofacial Radiology, School of Dentistry, Pusan National University, Dental Research Institute) ;
  • Jung, Yun-Hoa (Department of Oral and Maxillofacial Radiology, School of Dentistry, Pusan National University, Dental Research Institute) ;
  • Cho, Bong-Hae (Department of Oral and Maxillofacial Radiology, School of Dentistry, Pusan National University, Dental Research Institute) ;
  • Heo, Min-Suk (Department of Oral and Maxillofacial Radiology and Dental Research Institute, School of Dentistry, Seoul National University)
  • Received : 2018.11.27
  • Accepted : 2018.12.17
  • Published : 2019.03.31
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Abstract

Purpose: Artificial intelligence (AI), represented by deep learning, can be used for real-life problems and is applied across all sectors of society including medical and dental field. The purpose of this study is to review articles about deep learning that were applied to the field of oral and maxillofacial radiology. Materials and Methods: A systematic review was performed using Pubmed, Scopus, and IEEE explore databases to identify articles using deep learning in English literature. The variables from 25 articles included network architecture, number of training data, evaluation result, pros and cons, study object and imaging modality. Results: Convolutional Neural network (CNN) was used as a main network component. The number of published paper and training datasets tended to increase, dealing with various field of dentistry. Conclusion: Dental public datasets need to be constructed and data standardization is necessary for clinical application of deep learning in dental field.

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References

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