Applications of Artificial Intelligence in Ophthalmology: General Overview

Table 1

Introduction of existing CML techniques in the AI medical field.

Classifiers	Principles
Decision trees	(i) Tree-like structure
	(ii) Solve classification and regression problems based on rules to binary split data
Random forests	(i) Ensemble a multitude of decision trees for classification
	(ii) The ultimate prediction is made by majority voting
Support vector machines	Build a hyperplane that separates the positive and negative examples as wide as possible to minimize the separation error
Bayesian classifiers	(i) Based on the probability approach
	(ii) Assign a new sample to the category with maximum posterior probability, depending on the given prior probability, cost function, and category conditional density
k-nearest neighbors	Search for k-nearest training instances and classify a new instance into the most frequent class of these k instances
k-means	Partition n samples into k clusters in which each sample belongs to the cluster with the nearest mean
Linear discriminant analysis	(i) Create predictive functions that maximize the discrimination between previously established categories
Neural networks	(i) Consists of a collection of connected units, which can process signals
	(ii) Connections between them can transmit a signal to another
	(iii) Units are organized in layers
	(iv) Signals travel from the input layer to the output layer