摘要：SummarySeveral reviews have been conducted regarding artificial intelligence (AI) techniques to improve pregnancy outcomes. But they are not focusing on ultrasound images. This survey aims to explore how AI can assist with fetal growth monitoring via ultrasound image. We reported our findings using the guidelines for PRISMA. We conducted a comprehensive search of eight bibliographic databases. Out of 1269 studies 107 are included. We found that 2D ultrasound images were more popular (88) than 3D and 4D ultrasound images (19). Classification is the most used method (42), followed by segmentation (31), classification integrated with segmentation (16) and other miscellaneous methods such as object-detection, regression, and reinforcement learning (18). The most common areas that gained traction within the pregnancy domain were the fetus head (43), fetus body (31), fetus heart (13), fetus abdomen (10), and the fetus face (10). This survey will promote the development of improved AI models for fetal clinical applications.Graphical abstractDisplay OmittedHighlights•Artificial intelligence studies to monitor fetal development via ultrasound images•Fetal issues categorized based on four categories — general, head, heart, face, abdomen•The most used AI techniques are classification, segmentation, object detection, and RL•The research and practical implications are included.Health informatics; Diagnostic technique in health technology; Medical imaging; Artificial intelligence
