First attempts in 1950s
The first attempts at this approach to AI date back to the 1950s. Inventors created the perceptron, a mathematical model based on neural networks in biology.
Inputs resemble dendritic inputs and outputs resemble axonal outputs. However, the programs didn’t work well because they did not have enough data or computing power.
Advances in computing power, especially graphical processing units, and the large number of digital photographs now available have given a boost to artificial neural networks, said Dr Chang. “We have many more training photos that can be analyzed by a computer and, thus, come up with a way to classify them,” he said.
Likewise, computing power has been growing exponentially, and if it follows its present curve, computers will match the human brain in 50 years or so, he said.
“It’s an opportunity to train algorithms to be just as powerful as how we go through 12 years of training to become ophthalmologists,” Dr. Chang said. Now, programmers are creating multilayered, neural networks that search for subtle patterns, a type of AI called deep learning.
A few years ago, a programmer trying to make a computer diagnose diabetic retinopathy might have told it to look for hemorrhage and exudation, and to locate the macula and the optic disc. In a recent paper, researchers at Google used 9,963 images from 4,997 patients to train a computer to recognize diabetic retinopathy. The computer achieved 97.5% sensitivity and 93.4% specificity.
“You’re using the power of mathematics and statistics to allow the computer to see what stands out between what you classify as normal and what you classify as diabetic retinopathy,” said Dr. Chang. “The algorithm performed remarkably well and this is happening in every industry for computer vision.”