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©The Author(s) 2020. Published by Baishideng Publishing Group Inc. All rights reserved.
World J Clin Oncol. Nov 24, 2020; 11(11): 918-934
Published online Nov 24, 2020. doi: 10.5306/wjco.v11.i11.918
Published online Nov 24, 2020. doi: 10.5306/wjco.v11.i11.918
Artificial intelligence in dentistry: Harnessing big data to predict oral cancer survival
Man Hung, Jungweon Park, Sara Moazzami, College of Dental Medicine, Roseman University of Health Sciences, South Jordan, UT 84095, United States
Man Hung, Department of Orthopaedic Surgery Operations, University of Utah, Salt Lake City, UT 84108, United States
Man Hung, College of Social Work, University of Utah, Salt Lake City, UT 84112, United States
Man Hung, Division of Public Health, University of Utah, Salt Lake City, UT 84108, United States
Man Hung, Department of Educational Psychology, University of Utah, Salt Lake City, UT 84109, United States
Eric S Hon, Department of Economics, University of Chicago, Chicago, IL 60637, United States
Jerry Bounsanga, Research Section, Utah Medical Education Council, Salt Lake City, UT 84102, United States
Bianca Ruiz-Negrón, College of Social and Behavioral Sciences, University of Utah, Salt Lake City, UT 84112, United States
Dawei Wang, Data Analytics Unit, Walmart Inc., Bentonville, AR 72716, United States
Author contributions: Hung M and Hon ES contributed to study conception; Hung M provided study supervision; Hung M and Wang D contributed to research design, data analysis, visualization and results interpretation; Hung M, Hon ES and Bounsanga J contributed to data acquisition; Hung M, Park J, Moazzami S, Ruiz-Negrón B and Wang D contributed to manuscript drafting; Hung M, Park J, Hon ES, Bounsanga J and Wang D contributed to manuscript revision; all authors approved the final version of the manuscript.
Institutional review board statement: This is not a human subject research study. Per the United States federal regulations (45 CFR 46, category 4), this study is deemed exempt and does not require review from Institutional Review Board since the data were deidentified and publicly available.
Informed consent statement: This is not a human subject research study. This study used secondary data that were already collected and were publicly available online. Therefore, signed informed consent form is not relevant.
Conflict-of-interest statement: The authors declare that there is no conflict of interest regarding this work.
Data sharing statement: The data supporting the findings of this study can be accessed at: https://seer.cancer.gov/.
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/
Corresponding author: Man Hung, PhD, Professor, Research Dean, College of Dental Medicine, Roseman University of Health Sciences, 10894 S River Front Parkway, South Jordan, UT 84095, United States. mhung@roseman.edu
Received: June 24, 2020
Peer-review started: June 24, 2020
First decision: September 18, 2020
Revised: October 6, 2020
Accepted: October 20, 2020
Article in press: October 20, 2020
Published online: November 24, 2020
Processing time: 147 Days and 10.3 Hours
Peer-review started: June 24, 2020
First decision: September 18, 2020
Revised: October 6, 2020
Accepted: October 20, 2020
Article in press: October 20, 2020
Published online: November 24, 2020
Processing time: 147 Days and 10.3 Hours
Core Tip
Core Tip: Oral cancer is the sixth most prevalent cancer worldwide. The goal of this study was to come up with machine learning algorithms to predict the length of oral cancer survival and to explore the most important factors that were responsible for it. Age at diagnosis, primary cancer site, tumor size and year of diagnosis were found to be the most important factors predictive of oral cancer survival. Year of diagnosis represents an important new discovery in the literature. Using artificial intelligence, we developed a tool that can be used for oral cancer survival prediction and for medical decision making.