| REVIEW ARTICLE |
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| Year : 2019 | Volume
: 11
| Issue : 1 | Page : 3-10 |
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The Use of Deep Convolutional Neural Networks in Biomedical Imaging: A Review
Yu-Cheng Chen1, Derek Jin-Ki Hong2, Chia-Wei Wu1, Muralidhar Mupparapu MDS, Dip. ABOMR 2
1 Department of Learning Sciences and Technologies, Division of Oral and Maxillofacial Radiology, University of Pennsylvania School of Dental Medicine, Philadelphia, PA, USA
2 Department of Oral Medicine, Division of Oral and Maxillofacial Radiology, University of Pennsylvania School of Dental Medicine, Philadelphia, PA, USA
Correspondence Address:
Muralidhar Mupparapu
Department of Oral Medicine, Division of Oral and Maxillofacial Radiology; Professor of Oral Medicine; Director, Division of Oral and Maxillofacial Radiology, University of Pennsylvania School of Dental Medicine, Philadelphia, PA 19104
USA
 Source of Support: None, Conflict of Interest: None  | Check |
DOI: 10.4103/jofs.jofs_55_19
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Introduction: This review sought to present fundamental principles of deep convolutional neural networks (DCNNs) and provides an overview of its applications in medicine and dentistry. Materials and Methods: Scientific databases including PubMed, Science Direct, Web of Science, JSTOR, and Google Scholar were used to search for relevant literature on DCNN and its applications in the medical and dental fields from 2010 to September 2018. Two independent reviewers rated the articles based on the exclusion and inclusion criteria, and the remaining articles were reviewed. Results: The comprehensive literature search yielded 110,750 citations. After applying the exclusion and inclusion criteria, 340 articles remained that pertained to the use of DCNN in medicine and dentistry. Further exclusion based on nonbiomedical applications yielded a total of 26 articles for review. Conclusion: Advances in the development of neural network systems have permeated into the medical and dental fields, particularly in imaging and diagnostic testing. Researchers are attempting to use deep learning as an aid to assess medical images in clinical applications and its optimization will provide powerful tools to the next generation. However, the authors caution that these tools serve as supplements to improve diagnosis and not replace the medical professional.
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