Impact of artificial intelligence-based autosegmentation of organs at risk in low- and middle-income countries

Authors

Solomon Kibudde, Uganda Cancer Institute
Awusi Kavuma, Uganda Cancer Institute
Yao Hao, Washington University School of Medicine in St. Louis
Tianyu Zhao, Washington University School of Medicine in St. Louis
Hiram Gay, Washington University School of Medicine in St. Louis
Jacaranda Van Rheenen, Washington University School of Medicine in St. Louis
Pavan Mukesh Jhaveri, Baylor College of Medicine
Minjmaa Minjgee, National Cancer Center of Mongolia
Enkhsetseg Vanchinbazar, National Cancer Center of Mongolia
Urdenekhuu Nansalmaa, National Cancer Center of Mongolia
Baozhou Sun, Baylor College of Medicine

Journal

Advances in Radiation Oncology

Publication Date

11-1-2024

Volume

9

Issue

11

First Page

101638

Document Type

Open Access Publication

DOI

10.1016/j.adro.2024.101638

Rights and Permissions

Kibudde S, Kavuma A, Hao Y, Zhao T, Gay H, Van Rheenen J, Jhaveri PM, Minjgee M, Vanchinbazar E, Nansalmaa U, Sun B. Impact of Artificial Intelligence-Based Autosegmentation of Organs at Risk in Low- and Middle-Income Countries. Adv Radiat Oncol. 2024 Oct 5;9(11):101638. doi: 10.1016/j.adro.2024.101638. © 2024 The Author(s). Published by Elsevier Inc. on behalf of American Society for Radiation Oncology. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).

Recommended Citation

Kibudde, Solomon; Kavuma, Awusi; Hao, Yao; Zhao, Tianyu; Gay, Hiram; Van Rheenen, Jacaranda; Jhaveri, Pavan Mukesh; Minjgee, Minjmaa; Vanchinbazar, Enkhsetseg; Nansalmaa, Urdenekhuu; and Sun, Baozhou, "Impact of artificial intelligence-based autosegmentation of organs at risk in low- and middle-income countries." Advances in Radiation Oncology. 9, 11. 101638 (2024).
https://digitalcommons.wustl.edu/oa_4/5489

Department

ICTS (Institute of Clinical and Translational Sciences)