Assessing the external validity of machine learning-based detection of glaucoma

• المؤلفون الرئيسيون: Li, Chi, Chua, Jacqueline, Schwarzhans, Florian, Husain, Rahat, Girard, Michaël J. A., Majithia, Shivani, Tham, Yih-Chung, Cheng, Ching-Yu, Aung, Tin, Fischer, Georg, Vass, Clemens, Bujor, Inna, Kwoh, Chee Keong, Popa-Cherecheanu, Alina, Schmetterer, Leopold, Wong, Damon
• مؤلفون آخرون: School of Computer Science and Engineering
• التنسيق: مقال
• اللغة: English
• منشور في: 2023
• الموضوعات: Engineering::Computer science and engineering; Engineering::Bioengineering; Glaucoma; Machine Learning
• الوصول للمادة أونلاين: https://hdl.handle.net/10356/168760

Studies using machine learning (ML) approaches have reported high diagnostic accuracies for glaucoma detection. However, none assessed model performance across ethnicities. The aim of the study is to externally validate ML models for glaucoma detection from optical coherence tomography (OCT) data. W...