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• W4313444384 abstract "This study utilized a method of bibliographic analysis and text mining on the literature from databases of Web of Science (WOS) and Scopus. About 79 and 632 related articles are collected from WOS and Scopus, respectively. After removing the duplicates, 708 publications are obtained by this study. A text mining and discussion related to AI-based methods, applications, issues, and solutions are discussed. Findings show that the focus on macular detection is increasingly rising academically since 2014. The major disciplines fall in ophthalmology, neurosciences, internal medicine, and science and technology. The USA, China, and Italy are the most contributing countries. The hottest topic is identified on macular fovea (MF) detection with AI methods based on optical coherence tomography (OCT) and colorful fundus images, especially for diagnosis and treatments of glaucoma, cataract, diabetic retinopathy (DR), age-related macular degeneration (AMD), and diabetic macular edema (DME). Issues and challenges are identified in this study, which include data safety and ethic approval, AI-based model interpretability, explainability and interpretation, and clinical examination with real-world data. A solution of explainable artificial intelligence (XAI) method is suggested. Moreover, this research indicated that interdisciplinary research and collaboration among countries, authors, and institutions should be encouraged. Thus, this article contributes a reference value for future research." @default.
• W4313444384 created "2023-01-06" @default.
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• W4313444384 date "2023-01-01" @default.
• W4313444384 modified "2023-09-30" @default.
• W4313444384 title "A Bibliographic Study of Macular Fovea Detection: AI-Based Methods, Applications, and Issues" @default.
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• W4313444384 doi "https://doi.org/10.1007/978-981-19-7184-6_24" @default.
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