Table 1.

Definitions of subclasses within AI

Subclass	Definition
Machine learning (ML)	ML involves computer science that is able to perform desired tasks based on input data. When provided with sufficient data, algorithms can recognize patterns in data and train the model to perform better. After completion of the final model, the algorithm can be applied to new unknown data [5]
Decision tree (DT)	Within a DT model, multiple factors are classified into tree branches. Based on the algorithm, these branches are divided into nodes, forming several tree pathways. In the end, this model tends to find the smallest tree that optimally fits the data [6]
Gradient boosting (GBM)	In GBM, weights are added to several factors after classification. Afterwards an assessment of weights occurs, in which weights are modified based on the difficulty to classify the factors. this process is repeated until a final optimal model is generated [7]
Random forest (RF)	RF involves the formation of multiple decision trees with specific values for predictors. This technique combines all decision trees in order to build an accurate model for predictions [8]
Support vector machine (SVM)	SVM models use mapped input data to discover the optimal boundary to separate several classes and values [9]
Deep learning	As a specific branch of machine learning, deep learning can recognize patterns within datasets by using multiple processing layers. Within each layer, weights are present for several factors within the model. After the training process, an optimal model is built to perform on new data [10]
Artificial neural networks (ANNs)	Similar to our brain system, data is passed through multiple processing layers within ANNs. Each layer contains weights in order to make decisions for the resulting output. By repeat of this process, this model can improve results and produce the most accurate model in the end [11]
Convolutional neural networks (CNNs)	CNNs are a specific type of neural networks, however no weights are used in the layers. Instead, multiple layers are functioning as filters to register patterns or regions of images [12]
Radiomics	A radiomics model analyzes images in order to retrieve specific texture features that are registered as a 0 or 1. By detecting these features, various pathologies could be recognized [13]

Abbreviations: ML, machine learning; DT, decision tree; GBM, gradient boosting machine; RF, random forest; SVM, support vector machine; ANN, artificial neural networks; CNN, convolutional neural networks