Copyright ©The Author(s) 2023.
World J Clin Cases. Jun 6, 2023; 11(16): 3725-3735
Published online Jun 6, 2023. doi: 10.12998/wjcc.v11.i16.3725
Table 1 Different applications for artificial intelligence for fetal brain magnetic resonance imaging

Different applications
AAI for preprocessing of fetal images(1) Automatic image quality assessment to detect artifacts on T2 HASTE sequences during fetal MRI (Gagoski et al[9]); (2) Automatically detects fetal landmarks (using 15 key points–upper limb and lower limb joints, eyes, and bladder) (Xu et al[10]); (3) Fetal motion correction (Hou et al[11]); and (4) Predicting fetal motion directly from acquired images in real-time (Singh et al[12])
BAI for post-processing of fetal images(1) U-net-based brain extraction algorithm to autonomously segment normal fetal brains (Li et al[14]); and (2) Localize, segment, and perform super-resolution reconstruction for the automated fetal brain (Ebner et al[15])
CAI for reconstruction of fetal imagingFully automatic framework for fetal brain reconstruction, consisting of four stages (Ebner et al[15])
DAI for gestational age prediction(1) Predicting GA from fetal brain MRI acquired after the first trimester, which was compared to a BPD (Kojita et al[19]); and (2) An end-to-end, attention-guided deep learning model that predicts GA (Shen et al[20])
EAI for fetal brain extraction(1) The automatic brain extraction method for fetal MRI employs a multi-stage 2D U-Net with deep supervision (DS U-net) (Lou et al[24]); and (2) A brain mask for an MRI stack using a two-phase random forest classifier and one estimated high-order Markov random field solution (Ison et al[23])
FAI for fetal brain segmentation(1) U-net-like convolutional neural network (Auto-net) (Mohseni Salehi et al[29]); CNN using images with synthetically induced intensity inhomogeneity as data augmentation (Mohseni Salehi et al[29]); (2) Pipeline for performing ICV localization, ICV segmentation, and super-resolution reconstruction in fetal MR data in a sequential manner (Tourbier et al[32]); (3) Automatic method for fetal brain segmentation from MRI data, and a normal volumetric growth chart based on a large cohort (Link et al[33]); (4) Fetal Brain magnetic resonance Acquisition Numerical phantom, to simulate various realistic magnetic resonance images of the fetal brain and its class labels (de Dumast et al[34]); (5) SIMOU-Net, a hybrid network for fetal brain segmentation. Was inspired by the original U-Net fused with the HED network (Rampun et al[36]); and (6) Incorporating spatial and channel dimensions-based multi-scale feature information extractors into its encoding-decoding framework (Long et al[35])
GAI for fetal brain linear measurement(1) AI for the anteroposterior (A/P) diameter of the pons and the A/P diameter and S/I height of the vermis (Deng et al[40]); and (2) A fully automatic method that computes three key fetal brain MRI parameters: 1-CBD, 2-BBD, 3-TCD (Avisdris et al[41])
HAI for automatically localizing fetal anatomyAutomatically localizing fetal anatomy, notably the brain, using extracted superpixels (Alansary et al[42])
IAI for classification of brain pathologyClassification using several machine-learning classifiers, including DQDA, K-NN, random forest, naive Bayes, and RBF neural network classifiers (Attallah et al[45])
JAI for placenta detection(1) U-net-based CNN to separate the uterus and placenta (Shahedi et al[51]); and (2) automatic placenta segmentation by deep learning on different MRI sequences (Specktor-Fadida et al[52])
KAI for functional fetal brain MRIAn auto-masking model with fMRI preprocessing stages from existing software (Rutherford et al[53])