Publication Name	World Journal of Radiology
Manuscript ID	88654
Country	United States

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, 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.
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

Received	2023-10-03 21:25
Peer-Review Started	2023-10-03 21:27
To Make the First Decision
Return for Revision	2023-10-09 08:17
Revised	2023-11-13 07:42
Second Decision	2023-11-28 00:05
Accepted by Journal Editor-in-Chief
Accepted by Executive Editor-in-Chief	2023-12-05 05:35
Articles in Press	2023-12-05 05:35
Publication Fee Transferred
Edit the Manuscript by Language Editor
Typeset the Manuscript	2023-12-22 11:38

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.
Permissions	For details, please visit: http://www.wjgnet.com/bpg/gerinfo/207
Publisher	Baishideng Publishing Group Inc, 7041 Koll Center Parkway, Suite 160, Pleasanton, CA 94566, USA
Website	http://www.wjgnet.com