Table 3.
Articles published using AI to improve fetus body monitoring: objective, backbone methods, optimization, fetal age, and AI tasks
| Study | Objective | Backbone Methods/Framework | Optimization/Extractor methods | Fetal age | AI tasks |
|---|---|---|---|---|---|
| Fetal Part Structures | |||||
| (Maraci et al., 2015) | To identify the fetal skull, heart and abdomen from ultrasound images | SVM as the classifier | Gaussian Mixture Model (GMM) Fisher Vector (FV) |
26th week | classification |
| (Liu et al., 2021) | To segment the seven key structures of the neonatal hip joint | Neonatal Hip Bone Segmentation Network (NHBSNet) | Feature Extraction Module Enhanced Dual Attention Module (EDAM) Two-Class Feature Fusion Module (2-Class FFM) Coordinate Convolution Output Head (CCOH) |
16 - 25 weeks. | segmentation |
| (Rahmatullah et al., 2014) | To segment organs head, femur, and humerus in ultrasound images using multilayer super pixel images features | Simple Linear Iterative Clustering (SLIC) Random forest |
Unary pixel shape feature image moment |
N/A | segmentation |
| (Weerasinghe et al., 2021) | To automate kidney segmentation using fully convolutional neural networks. | FCNN: U-Net & UNET++ | N/A | 20 to 40 weeks | segmentation |
| (Burgos-Artizzu et al., 2020) | To evaluate the maturity of current Deep Learning classification techniques for their application in a real maternal-fetal clinical environment | CNN DenseNet-169 | N/A | 18 to 40 weeks | classification |
| (Cai et al., 2020) | To use the learnt visual attention maps to guide standard plane detection on all three standard biometry planes: ACP, HCP and FLP. | Temporal SonoEyeNet (TSEN) Temporal attention module: Convolutional LSTM Video classification module: Recurrent Neural Networks (RNNs)+ |
CNN feature extractor: VGG-16 | N/A | classification |
| (Ryou et al., 2019) | To support first trimester fetal assessment of multiple fetal anatomies including both visualization and the measurements from a single 3D ultrasound scan | Multi-Task Fully Convolutional Network (FCN) U-Net |
N/A | 11 to 14 weeks | Segmentation Classification |
| (Sridar et al., 2019) | To automatically classify 14 different fetal structures in 2D fetal ultrasound images by fusing information from both cropped regions of fetal structures and the whole image | support vector machine (SVM)+ Decision fusion | Fine-tuning AlexNet CNN | 18 to 20 weeks | Classification |
| (Chen et al., 2017) | To automatic identification of different standard planes from US images | T-RNN framework: LSTM |
Features extracted using J-CNN classifier | 18 to 40 weeks | Classification |
| (Cai et al., 2018) | To classify abdominal fetal ultrasound video frames into standard AC planes or background | M-SEN architecture Discriminator CNN |
Generator CNN | N/A | Classification |
| (Gao and Noble, 2019) | To detect multiple fetal structures in free-hand ultrasound | CNN Attention Gated LSTM |
Class Activation Mapping (CAM) | 28 to 40 weeks | classification |
| (Yaqub et al., 2015) | To extract features from regions inside the images where meaningful structures exist. | Guided Random Forests | Probabilistic Boosting Tree (PBT) | 18 to 22 weeks | Classification |
| (Chen et al., 2015) | To detect standard planes from US videos | T-RNN LSTM (Transferred RNN) |
Spatio-Semporal Feature J-CNN |
18 to 40 weeks | Classification |
| Anatomical Structures | |||||
| (Yang et al., 2019) | To propose the first and fully automatic framework in the field to simultaneously segment fetus, gestational sac and placenta, | 3D FCN + RNN hierarchical deep supervision mechanism (HiDS) | BiLSTM module denoted as FB-nHiDS | 10 - 14 weeks | Segmentation |
| (Looney et al., 2021) | To segment the placenta, amniotic fluid, and fetus. | FCNN | N/A | 11 - 19 weeks | Segmentation |
| (Li et al., 2017) | To segment the amniotic fluid and fetal tissues in fetal US images | The encoder-decoder network based on VGG16 | N/A | 22ND weeks | Segmentation |
| (Ryou et al., 2016) | To localize the fetus and extract the best fetal biometry planes for the head and abdomen from first trimester 3D fetal US images | CNN | Structured Random Forests | 11 - 13 weeks | Classification |
| (Toussaint et al., 2018) | To detect and localize fetal anatomical regions in 2D US images | ResNet18 | Soft Proposal Layer (SP) | 22 - 32 weeks | Classification |
| (Ravishankar et al., 2016) | To reliably estimate abdominal circumference | CNN + Gradient Boosting Machine (GBM) | Histogram of Oriented Gradient (HoG) | 15 - 40 weeks | Classification |
| (Wee et al., 2010) | To detect and recognize the fetal NT based on 2D ultrasound images by using artificial neural network techniques. | Artificial Neural Network (ANN) | Multilayer Perceptron (MLP) Network Bidirectional Iterations Forward Propagations Method (BIFP) |
N/A | Classification |
| (Liu et al., 2019) | To detect NT region | U-Net NT Segmentation PCA NT Thickness Measurement |
VGG16 NT Region Detection | 4 - 12 weeks | Segmentation |
| Growth disease | |||||
| (Bagi and Shreedhara, 2014) | To propose the biometric measurement and classification of IUGR, using OpenGL concepts for extracting the feature values and ANN model is designed for diagnosis and classification | ANN Radial Basis Function (RBF) |
OpenGL | 12–40 Weeks |
Classification |
| (Selvathi and Chandralekha, 2021) | To find the region of interest (ROI) of the fetal biometric and organ region in the US image | DCNN AlexNet | N/A | 16 -27 weeks | Classification |
| (Rawat et al., 2016) | To detect fetal abnormality in 2D US images | ANN + Multilayered perceptron neural networks (MLPNN) | Gradient vector flow (GVF) Median Filtering |
14 - 40 weeks | Classification segmentation |
| (Gadagkar and Shreedhara, 2014) | To develop a computer-aided diagnosis and classification tool for extracting ultrasound sonographic features and classify IUGR fetuses | ANN | Two-Step Splitting Method (TSSM) for Reaction-Diffusion (RD) | 12–40 Weeks |
Classification segmentation |
| (Andriani and Mardhiyah, 2019) | To develop an automatic classification algorithm on the US examination result using Convolutional Neural Network in Blighted Ovum detection | CNN | N/A | N/A | Classification |
| (Yekdast, 2019) | To propose an intelligent system based on combination of ConvNet and PSO for Down syndrome diagnosis. | CNN | Particle Swarm Optimization (PSO) | N/A | Classification |
| (Maraci et al., 2020) | To automatically detect and measure the transcerebellar diameter (TCD) in the fetal brain, which enables the estimation of fetal gestational age (GA) | CNN FCN | N/A | 16- - 26 weeks | Classification segmentation |
| (Chen et al., 2020a) | To accurately estimate the gestational age from the fetal lung region of US images. | U-NET | N/A | 24 - 40 weeks | Classification segmentation |
| (Prieto et al., 2021) | To classify, segment, and measure several fetal structures for the purpose of GA estimation | U-NET RESTNET |
Residual UNET (RUNET) | 16th weeks | Classification segmentation |
| (Maysanjaya et al., 2014) | To measure the accuracy of Learning Vector Quantization (LVQ) to classify the gender of the fetus in the US image" | ANN | Learning Vector Quantization (LVQ) Moment invariants |
N/A | Classification |