The Algorithm Will See You Now: Artificial Intelligence May Transform Retinal Screening

by: William C. Ou; Charles C. Wykoff, MD, PhD

The AI Explosion

The pursuit of artificial intelligence (AI)—machine simulation of human cognitive behavior—began in earnest in the 1950s. Advances in the 21st century, particularly in machine learning (eg, teaching computers to learn from and make predictions on data without being explicitly programmed), have brought AI into the current cultural spotlight. At the forefront of these developments is the explosion of deep learning (DL).[1]
DL has its basis in artificial neural networks (ANNs), structures of interconnected nodes that mimic the synaptic connections of the human nervous system. By adjusting the strengths (weights) of connections between nodes based on training data, ANNs can “learn” to perform specific tasks, such as image recognition.
ANNs are organized into layers that feed information into each other: an input layer, an output layer, and one or more hidden layers in between. Having several hidden layers (hence, “deep” learning) facilitates multiple levels of feature abstraction, such as building from pixels to edges to shapes and so on. Although computationally intensive, such technological advances as the integration of graphics processing units have made training DL networks much more feasible.

 Historically, medicine has been no stranger to AI research, with one early example being automated interpretation of ECGs.[2] Radiology, in particular, is amenable to incorporation of computer systems, with computer-aided diagnosis systems currently commercially available in such areas as mammography and detection of lung nodules on chest radiographs.[3] In ophthalmology, computer-aided diagnosis has been investigated for glaucoma, age-related macular degeneration, and diabetic retinopathy.[4,5] In the anterior segment, AI and data-driven approaches have led to the Hill-RBF calculator for intraocular lens power.[6] ….

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Source: Medscape
Image: Medscape