Table 7.

Articles published using AI to improve fetus abdomen monitoring: objective, backbone methods, optimization, fetal age, and AI tasks

Study	Objective	Backbone Methods/Framework	Optimization/Extractor methods	Fetal age	AI tasks
Abdominal anatomical landmarks
(Rahmatullah et al., 2011b)	To automatically detect two anatomical landmarks in an abdominal image plane stomach bubble (SB) and the umbilical vein (UV).	AdaBoost	Haar-like feature	14 - 19 weeks	Classification
(Yang et al., 2014)	To localize fetal abdominal standard plane (FASP) from US including SB, UV, and spine (SP)	Random Forests Classifier+ SVM	Haar-like feature Radial Component-Based Model (RCM)	18 - 40 weeks	Classification
(Kim et al., 2018)	To classify ultrasound images (SB, amniotic fluid (AF), and UV) and to obtain an initial estimate of the AC."	Initial Estimation CNN + U-Net	Hough transform	N/A	Classification segmentation
(Jang et al., 2017)	To classify ultrasound images (SB, AF, and UV) and measure AC	CNN	Hough transform	20 - 34 weeks	Classification segmentation
(Wu et al., 2017)	To find the region of interest (ROI) of the fetal abdominal region in the US image.	Fetal US Image Quality Assessment (FUIQA)	L-CNN is able to localize the fetal abdominal ROI AlexNet C-CNN then further analyzes the identified ROI DCNN to duplicate the US images for the RGB channels rotating"	16 - 40 weeks	Classification
(Ni et al., 2014)	To localize the fetal abdominal standard plane from ultrasound	Random forest classifier+ SVM classifier	Radial Component-based Model (RCM) Vessel Probability Map (VPM) Haar-like features	18 - 40 weeks	Classification
(Deepika et al., 2021)	To diagnose the (prenatal) US images by design and implement a novel framework	Defending Against Child Death (DACD)	CNN U-Net Hough-man transformation	N/A	Classification segmentation
(Rahmatnllah et al., 2012)	To detect important landmarks employed in manual scoring of ultrasoundimages.	AdaBoost	Haar-like feature	18 - 37 weeks	Classification
(Rahmatullah et al., 2011a)	To automatically select the standard plane from the fetal US volume for the application of fetal biometry measurement.	AdaBoost	One Combined Trained Classifier (1CTC) Two Separately Trained Classifiers (2STC) Haar-like feature	20 - 28 weeks	Classification
(Chen et al., 2014)	To localize the FASP from US images.	DCNN	Fine-Tuning with Knowledge Transfer Barnes-Hut Stochastic Neighbor Embedding (BH-SNE)	18 - 40 weeks)	Classification