Applications of deep learning in dentistry

Corbella, S and Srinivas, S and Cabitza, F (2021) Applications of deep learning in dentistry. In: Oral Surgery, Oral Medicine, Oral Pathology and Oral Radiology, 132 (2). pp. 225-238.

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Abstract

Over the last few years, translational applications of so-called artificial intelligence in the field of medicine have garnered a significant amount of interest. The present article aims to review existing dental literature that has examined deep learning, a subset of machine learning that has demonstrated the highest performance when applied to image processing and that has been tested as a formidable diagnostic support tool through its automated analysis of radiographic/photographic images. Furthermore, the article will critically evaluate the literature to describe potential methodological weaknesses of the studies and the need for further development. This review includes 28 studies that have described the applications of deep learning in various fields of dentistry. Research into the applications of deep learning in dentistry contains claims of its high accuracy. Nonetheless, many of these studies have substantial limitations and methodological issues (e.g., examiner reliability, the number of images used for training/testing, the methods used for validation) that have significantly limited the external validity of their results. Therefore, future studies that acknowledge the methodological limitations of existing literature will help to establish a better understanding of the usefulness of applying deep learning in dentistry. © 2020 Elsevier Inc.

Item Type:	Journal Article
Publication:	Oral Surgery, Oral Medicine, Oral Pathology and Oral Radiology
Publisher:	Mosby Inc.
Additional Information:	The copyright for this article belongs to Mosby Inc.
Keywords:	artificial intelligence; deep learning; dentistry; external validity; human; image processing; reliability; review
Department/Centre:	Division of Interdisciplinary Sciences > Centre for Nano Science and Engineering
Date Deposited:	22 Feb 2023 03:41
Last Modified:	22 Feb 2023 03:41
URI:	https://eprints.iisc.ac.in/id/eprint/80438

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