Artificial intelligence in traumatic brain injury: Brain imaging analysis and outcome prediction: A mini review

Table 1 Characteristics of the main machine learning algorithms

Traditional machine learning algorithms	Main characteristics
K-nearest neighbors	Pixels are based on their similarity to neighbors. Adopted in early TBI studies for lesion segmentation
Support vector machines	Regions of interest are distinguished using hyperplanes. Adopted for classifying brain tissue types in TBI
Random forest	Learning method that uses decision trees for pixel classification. Adopted in multimodal MRI segmentation
K-means clustering	Unsupervised algorithm that groups pixels based on intensity values. Adopted for quick but coarse lesion detection
Gaussian mixture model	Probabilistic model that assumes pixel intensities follow a Gaussian distribution. Adopted in segmentation tasks

TBI: Traumatic brain injury; MRI: Magnetic resonance image.