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.

Table 2 Characteristics of the main deep learning algorithms

Deep Learning algorithms	Main characteristics
CNNs	Automatic learning of hierarchical features from brain images. Widely adopted to TBI segmentation
Fully convolutional networks	Replacement of fully connected layers with convolutional layers, enabling pixel-wise classification. Adopted for segmenting lesions and hemorrhages in TBI MRI scans
Recurrent neural networks & long short-term memory	Adopted in time-series analysis for dynamic brain imaging data. LSTMs combined with CNNs useful in sequential TBI segmentation across multiple time poi

TBI: Traumatic brain injury; MRI: Magnetic resonance image; CNNs: Convolutional neural networks; LSTMs: Long short-term memory.