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Developing innovative informatics approaches aimed to enhance fetalmonitoring is a burgeoning field of study in reproductive medicine. Severalreviews have been conducted regarding Artificial intelligence (AI) techniquesto improve pregnancy outcomes. They are limited by focusing on specific datasuch as mother's care during pregnancy. This systematic survey aims to explorehow artificial intelligence (AI) can assist with fetal growth monitoring viaUltrasound (US) image. We used eight medical and computer science bibliographicdatabases, including PubMed, Embase, PsycINFO, ScienceDirect, IEEE explore, ACMLibrary, Google Scholar, and the Web of Science. We retrieved studies publishedbetween 2010 to 2021. Data extracted from studies were synthesized using anarrative approach. Out of 1269 retrieved studies, we included 107 distinctstudies from queries that were relevant to the topic in the survey. We foundthat 2D ultrasound images were more popular (n=88) than 3D and 4D ultrasoundimages (n=19). Classification is the most used method (n=42), followed bysegmentation (n=31), classification integrated with segmentation (n=16) andother miscellaneous such as object-detection, regression and reinforcementlearning (n=18). The most common areas within the pregnancy domain were thefetus 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, deeplearning techniques were primarily used (n=81), followed by machine learning(n=16), artificial neural network (n=7), and reinforcement learning (n=2). AItechniques played a crucial role in predicting fetal diseases and identifyingfetus anatomy structures during pregnancy. More research is required tovalidate this technology from a physician's perspective, such as pilot studiesand randomized controlled trials on AI and its applications in a hospitalsetting.

Towards deep observation: A systematic survey on artificial intelligence techniques to monitor fetus via Ultrasound Images