Impact of Artificial Intelligence-Based Autosegmentation of Organs at Risk in Low- and Middle-Income Countries
AI-based autosegmentation generates OAR contours of comparable quality to manual segmentation for both pelvic and HN cancer patients in LMICs, with substantial time savings.
Authors
- Solomon Kibudde (ORCID: https://orcid.org/0000-0002-4013-2476)
- Baozhou Sun (ORCID: https://orcid.org/0000-0003-2316-2195)
- Enkhsetseg Vanchinbazar
- Minjmaa Minjgee
- Jacaranda van Rheenen (ORCID: https://orcid.org/0000-0001-5127-1298)
- Hiram A. Gay (ORCID: https://orcid.org/0000-0001-9766-3537)
- Tianyu Zhao (ORCID: https://orcid.org/0000-0002-2696-273X)
- Awusi Kavuma (ORCID: https://orcid.org/0000-0003-4596-7437)
- Urdenekhuu Nansalmaa
- Pavan Jhaveri
- Yao Hao
Institutions
- Baylor College of Medicine (US)
- Washington University in St. Louis (US)
- National Center for Communicable Diseases (MN)
- Uganda Cancer Institute (UG)
Publication Details
- Journal
- Advances in Radiation Oncology
- Published
- 2024-10-05
- DOI
- https://doi.org/10.1016/j.adro.2024.101638
- Citations
- 4
- Primary Topic
- Advances in Oncology and Radiotherapy
- Type
- article
- Field-Weighted Citation Impact
- 1.62
Funders
- Washington University in St. Louis
- American Association of Physicists in Medicine