Embedded deep learning in ophthalmology: Making ophthalmic imaging smarter

TL;DR

This paper reviews the integration of embedded deep learning in ophthalmic imaging devices, focusing on active acquisition techniques that enhance image quality and clinical diagnostics through improved hardware and computation frameworks.

Contribution

It introduces the concept of active acquisition embedded deep learning in ophthalmology and discusses its potential to improve image quality and clinical outcomes.

Findings

Embedded deep learning enables automatic, high-quality image acquisition.

Improved hardware performance facilitates real-time active acquisition.

Enhanced image quality supports more robust clinical diagnostics.

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 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.