| 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
|
| 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 |
|
| Corresponding Author |
John Chang, MD, PhD, 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 |
| URL |
https://www.wjgnet.com/1949-8470/full/v15/i12/359.htm |
| DOI |
https://dx.doi.org/10.4329/wjr.v15.i12.359 |
