Artificial Intelligence for the Computer-aided Detection of Periapical Lesions in Cone-beam Computed Tomographic Images.

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Abstract

The aim of this study was to use a Deep Learning (DL) algorithm for the automated segmentation of cone-beam computed tomographic (CBCT) images and the detection of periapical lesions.

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Journal

Journal ID (iso-abbrev): J Endod

Title: Journal of endodontics

Publisher: Elsevier BV

ISSN (Electronic): 1878-3554

ISSN (Print): 0099-2399

Publication date (Electronic): Jul 2020

Volume: 46

Issue: 7

Affiliations

[1 ] Department of Endodontics, School of Dental Medicine, University of Pennsylvania, Philadelphia, Pennsylvania. Electronic address: fsetzer@upenn.edu.

[2 ] Private Practice, University of Pennsylvania, Philadelphia, Pennsylvania.

[3 ] School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, Tempe, Arizona.

[4 ] Department of Oral Medicine, School of Dental Medicine, University of Pennsylvania, Philadelphia, Pennsylvania.

Article

Publisher Item ID: S0099-2399(20)30235-1

DOI: 10.1016/j.joen.2020.03.025

PubMed ID: 32402466

SO-VID: 279f47e3-e68c-4520-a2de-e5434b2d9eb1

History

Keywords: digital imaging/radiology,Artificial intelligence,periapical lesion,Deep Learning,cone-beam computed tomography,U-Net

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Keywords: digital imaging/radiology, Artificial intelligence, periapical lesion, Deep Learning, cone-beam computed tomography, U-Net

Artificial Intelligence for the Computer-aided Detection of Periapical Lesions in Cone-beam Computed Tomographic Images.

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Abstract

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Artificial Intelligence in Medicine

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