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Articles Published Processes
9/7/2020 9:18:26 AM | Browse: 713 | Download: 1396
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Received |
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2020-05-25 14:56 |
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Peer-Review Started |
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2020-05-25 14:58 |
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To Make the First Decision |
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Return for Revision |
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2020-07-04 01:33 |
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Revised |
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2020-07-15 08:30 |
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Second Decision |
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2020-08-21 12:30 |
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Accepted by Journal Editor-in-Chief |
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Accepted by Executive Editor-in-Chief |
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2020-08-21 21:11 |
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Articles in Press |
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2020-08-21 21:11 |
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Publication Fee Transferred |
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Edit the Manuscript by Language Editor |
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Typeset the Manuscript |
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2020-08-29 15:27 |
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Publish the Manuscript Online |
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2020-09-07 09:18 |
| ISSN |
2644-3260 (online) |
| Open Access |
This article is an open-access article that was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution NonCommercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/ |
| Copyright |
© The Author(s) 2020. Published by Baishideng Publishing Group Inc. All rights reserved. |
| Article Reprints |
For details, please visit: http://www.wjgnet.com/bpg/gerinfo/247
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| Permissions |
For details, please visit: http://www.wjgnet.com/bpg/gerinfo/207
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| Publisher |
Baishideng Publishing Group Inc, 7041 Koll Center Parkway, Suite 160, Pleasanton, CA 94566, USA |
| Website |
http://www.wjgnet.com |
| Category |
Radiology, Nuclear Medicine & Medical Imaging |
| Manuscript Type |
Minireviews |
| Article Title |
Artificial intelligence and pituitary adenomas: A review
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| Manuscript Source |
Invited Manuscript |
| All Author List |
Elvira Guerriero, Lorenzo Ugga and Renato Cuocolo |
| ORCID |
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| Funding Agency and Grant Number |
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| Corresponding Author |
Renato Cuocolo, MD, PhD, Doctor, Doctor, Research Fellow, Department of Advanced Biomedical Sciences, University of Naples “Federico II”, via Pansini 5, Naples 80131, Italy. renato.cuocolo@unina.it |
| Key Words |
Pituitary adenoma; Machine learning; Deep learning; Radiomics; Texture analysis; Magnetic resonance imaging |
| Core Tip |
Machine learning (ML) has seen an explosion of interest in medical imaging because of its capability of analyzing large amounts of data. Recent studies applied ML techniques to the imaging of pituitary adenomas. The purpose of our review was to describe the main concepts in ML and its current and potential applications in imaging analysis of pituitary tumors. |
| Publish Date |
2020-09-07 09:18 |
| Citation |
Guerriero E, Ugga L, Cuocolo R. Artificial intelligence and pituitary adenomas: A review. Artif Intell Med Imaging 2020; 1(2): 70-77 |
| URL |
https://www.wjgnet.com/2644-3260/full/v1/i2/70.htm |
| DOI |
https://dx.doi.org/10.35711/aimi.v1.i2.70 |
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