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12/26/2023 12:35:45 PM | Browse: 311 | Download: 1223
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Received |
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2023-10-03 21:25 |
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Peer-Review Started |
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2023-10-03 21:27 |
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First Decision by Editorial Office Director |
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2023-10-09 08:17 |
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Return for Revision |
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2023-10-09 08:17 |
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Revised |
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2023-11-13 07:42 |
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Publication Fee Transferred |
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Second Decision by Editor |
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2023-11-28 00:05 |
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Second Decision by Editor-in-Chief |
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Final Decision by Editorial Office Director |
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2023-12-05 05:35 |
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Articles in Press |
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2023-12-05 05:35 |
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Edit the Manuscript by Language Editor |
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Typeset the Manuscript |
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2023-12-22 11:38 |
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Publish the Manuscript Online |
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2023-12-26 02:32 |
| ISSN |
1949-8470 (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) 2023. 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 |
Observational Study |
| Article Title |
Methods for improving colorectal cancer annotation efficiency for artificial intelligence-observer training
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| Manuscript Source |
Unsolicited Manuscript |
| All Author List |
Matthew Grudza, Brandon Salinel, Sarah Zeien, Matthew Murphy, Jake Adkins, Corey T Jensen, Curtis Bay, Vikram Kodibagkar, Phillip Koo, Tomislav Dragovich, Michael A Choti, Madappa Kundranda, Tanveer Syeda-Mahmood, Hong-Zhi Wang and John Chang |
| Funding Agency and Grant Number |
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| Corresponding Author |
John Chang, MD, PhD, Doctor, Doctor, Department of Radiology, Banner MD Anderson Cancer Center, 2940 E. Banner Gateway Drive, Suite 315, Gilbert, AZ 85234, United States. changresearch1@gmail.com |
| Key Words |
Artificial intelligence; Colorectal cancer; Detection |
| Core Tip |
Minimizing diagnostic errors for colorectal cancer may be most effectively performed with artificial intelligence (AI) second observer. Supervised training of AI-observer will require high volume of annotated training cases. Comparing skip-slice annotation and AI-initiated annotation shows that skipping slices does not affect the training outcome while AI-initiated annotation does not significantly improve annotation time. |
| Publish Date |
2023-12-26 02:32 |
| Citation |
Grudza M, Salinel B, Zeien S, Murphy M, Adkins J, Jensen CT, Bay C, Kodibagkar V, Koo P, Dragovich T, Choti MA, Kundranda M, Syeda-Mahmood T, Wang HZ, Chang J. Methods for improving colorectal cancer annotation efficiency for artificial intelligence-observer training. World J Radiol 2023; 15(12): 359-369
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| URL |
https://www.wjgnet.com/1949-8470/full/v15/i12/359.htm |
| DOI |
https://dx.doi.org/10.4329/wjr.v15.i12.359 |
Copyright © 1993-2026 Baishideng Publishing Group Inc, 7041 Koll Center Parkway, Suite 160, Pleasanton, CA 94566, USA. All rights reserved, including rights relating to text and data mining, AI training, and similar technologies. For open-access content, the applicable copyright and licensing terms govern.
