  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 edge
layer performance via "active acquisition" serves as an automatic data curation
operator translating to better quality data in electronic health records
(EHRs), as well as on the cloud layer, for improved deep learning-based
clinical data mining.
