Artificial Neurons

An artificial neuron represents, at the most basic level, how AI makes a single decision. Data comes in as an input (x). The question then becomes how we can utilize ML to create a mathematical function using weights (w), bias (b), a summation function (E), and an activation function (f) to ultimately reach the output (y) that we want.

ML strategies for an artificial neuron can be grouped into (a) supervised learning, where inputs and outputs are known and provided to the artificial neuron in a training environment, or (b) unsupervised learning, where no labels are available in a training set and the ML is responsible for structuring the entire algorithm. Care1 uses supervised learning.

Example of Supervised Learning in Eyecare by an Artificial Neuron

Let’s use a hypothetical example, where we want to use ML to create an artificial neuron that can assist us with deciding when to treat a patient for glaucoma, based on the cup:disc ratio of the optic disc within the eye. (This is a purely hypothetical example, because in reality, we make treatment decisions based on numerous variables and not just entirely on the cup:disc ratio). These are example photographs of optic nerves with cup:disc ratios ranging from 0.0 to 1.0.

These are the mathematical steps that we would take to implement ML to solve the above question:

Step 1: Control for all other variables, such as age, IOP, VF, OCT, etc. (this is controlling for w, the weights, except for the single weight we are trying to solve)

Step 2: Obtain a large set of patients and photograph all of their eyes.

Step 3: Label all the eyes with their cup:disc ratio (this is x, the input)

Step 4: Show the photos to different eye doctors, and obtain a universal recommendation for each with regards to whether or not they would start medication for each of the patients (this is y, the output)

Step 5: Create mathematical formulas which continually self-adjust factors w, b, E and f until a mathematical function is created which can reliably predict if the eye doctor will treat or not, based on the cup:disc ratio (this is the machine-learning process)

Step 6: Continuously test the resulting mathematical function on additional patients in different clinical situations, comparing the mathematically predicted patient recommendation against what doctors acting as independent validators recommend (this is the clinical validation). Modify the function to progressively improve accuracy.