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http://hdl.handle.net/20.500.12188/31280| Title: | Potential of E-Learning Interventions and Artificial Intelligence-Assisted Contouring Skills in Radiotherapy: The ELAISA Study | Authors: | Rasmussen, Mathis Ersted Akbarov, Kamal Titovich, Egor Nijkamp, Jasper Albertus Van Elmpt, Wouter Primdahl, Hanne Lassen, Pernille Cacicedo, Jon Cordero-Mendez, Lisbeth Uddin, A F M Kamal Mohamed, Ahmed Prajogi, Ben Brohet, Kartika Erida Nyongesa, Catherine Lomidze, Darejan Prasiko, Gisupnikha Ferraris, Gustavo Mahmood, Humera Stojkovski, Igor Isayev, Isa Mohamad, Issa Shirley, Leivon Kochbati, Lotfi Eftodiev, Ludmila Piatkevich, Maksim Bonilla Jara, Maria Matilde Spahiu, Orges Aralbayev, Rakhat Zakirova, Raushan Subramaniam, Sandya Kibudde, Solomon Tsegmed, Uranchimeg Korreman, Stine Sofia Eriksen, Jesper Grau |
Keywords: | e-learning Artificial Intelligence Assisted Contouring Skills in Radiotherapy |
Issue Date: | 5-Sep-2024 | Publisher: | Lippincott, Williams & Wilkins | Journal: | JCO Global Oncology | Abstract: | Most research on artificial intelligence-based auto-contouring as template (AI-assisted contouring) for organs-at-risk (OARs) stem from high-income countries. The effect and safety are, however, likely to depend on local factors. This study aimed to investigate the effects of AI-assisted contouring and teaching on contouring time and contour quality among radiation oncologists (ROs) working in low- and middle-income countries (LMICs). | URI: | http://hdl.handle.net/20.500.12188/31280 | DOI: | 10.1200/GO.24.00173 |
| Appears in Collections: | Faculty of Medicine: Journal Articles |
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| File | Description | Size | Format | |
|---|---|---|---|---|
| Global_Oncology.pdf | 4.76 MB | Adobe PDF | View/Open |
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