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• W2897855897 startingPage "251584141982717" @default.
• W2897855897 abstract "Deep learning has recently gained high interest in ophthalmology due to its ability to detect clinically significant features for diagnosis and prognosis. Despite these significant advances, little is known about the ability of various deep learning systems to be embedded within ophthalmic imaging devices, allowing automated image acquisition. In this work, we will review the existing and future directions for ‘active acquisition’–embedded deep learning, leading to as high-quality images with little intervention by the human operator. In clinical practice, the improved image quality should translate into more robust deep learning–based clinical diagnostics. Embedded deep learning will be enabled by the constantly improving hardware performance with low cost. We will briefly review possible computation methods in larger clinical systems. Briefly, they can be included in a three-layer framework composed of edge, fog, and cloud layers, the former being performed at a device level. Improved egde-layer performance via ‘active acquisition’ serves as an automatic data curation operator translating to better quality data in electronic health records, as well as on the cloud layer, for improved deep learning–based clinical data mining." @default.
• W2897855897 created "2018-10-26" @default.
• W2897855897 creator A5015017045 @default.
• W2897855897 creator A5029816810 @default.
• W2897855897 creator A5036457457 @default.
• W2897855897 creator A5055334565 @default.
• W2897855897 date "2019-01-01" @default.
• W2897855897 modified "2023-10-16" @default.
• W2897855897 title "Embedded deep learning in ophthalmology: making ophthalmic imaging smarter" @default.
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