  Developing innovative informatics approaches aimed to enhance fetal
monitoring is a burgeoning field of study in reproductive medicine. Several
reviews have been conducted regarding Artificial intelligence (AI) techniques
to improve pregnancy outcomes. They are limited by focusing on specific data
such as mother's care during pregnancy. This systematic survey aims to explore
how artificial intelligence (AI) can assist with fetal growth monitoring via
Ultrasound (US) image. We used eight medical and computer science bibliographic
databases, including PubMed, Embase, PsycINFO, ScienceDirect, IEEE explore, ACM
Library, Google Scholar, and the Web of Science. We retrieved studies published
between 2010 to 2021. Data extracted from studies were synthesized using a
narrative approach. Out of 1269 retrieved studies, we included 107 distinct
studies from queries that were relevant to the topic in the survey. We found
that 2D ultrasound images were more popular (n=88) than 3D and 4D ultrasound
images (n=19). Classification is the most used method (n=42), followed by
segmentation (n=31), classification integrated with segmentation (n=16) and
other miscellaneous such as object-detection, regression and reinforcement
learning (n=18). The most common areas within the pregnancy domain were the
fetus head (n=43), then fetus body (n=31), fetus heart (n=13), fetus abdomen
(n=10), and lastly the fetus face (n=10). In the most recent studies, deep
learning techniques were primarily used (n=81), followed by machine learning
(n=16), artificial neural network (n=7), and reinforcement learning (n=2). AI
techniques played a crucial role in predicting fetal diseases and identifying
fetus anatomy structures during pregnancy. More research is required to
validate this technology from a physician's perspective, such as pilot studies
and randomized controlled trials on AI and its applications in a hospital
setting.
