Department of Computer Science, College of Computer Science and Information Technology, Imam Abdulrahman Bin Faisal University, P.O. Box 1982, Dammam 31441, Saudi Arabia.
Sensors (Basel). 2022 Jun 17;22(12):4570. doi: 10.3390/s22124570.
A fetal ultrasound (US) is a technique to examine a baby's maturity and development. US examinations have varying purposes throughout pregnancy. Consequently, in the second and third trimester, US tests are performed for the assessment of Amniotic Fluid Volume (AFV), a key indicator of fetal health. Disorders resulting from abnormal AFV levels, commonly referred to as oligohydramnios or polyhydramnios, may pose a serious threat to a mother's or child's health. This paper attempts to accumulate and compare the most recent advancements in Artificial Intelligence (AI)-based techniques for the diagnosis and classification of AFV levels. Additionally, we provide a thorough and highly inclusive breakdown of other relevant factors that may cause abnormal AFV levels, including, but not limited to, abnormalities in the placenta, kidneys, or central nervous system, as well as other contributors, such as preterm birth or twin-to-twin transfusion syndrome. Furthermore, we bring forth a concise overview of all the Machine Learning (ML) and Deep Learning (DL) techniques, along with the datasets supplied by various researchers. This study also provides a brief rundown of the challenges and opportunities encountered in this field, along with prospective research directions and promising angles to further explore.
胎儿超声（US）是一种检查婴儿成熟度和发育情况的技术。US 检查在整个怀孕期间有不同的目的。因此，在妊娠的第二和第三个三个月，US 测试用于评估羊水指数（AFV），这是胎儿健康的关键指标。AFV 水平异常导致的疾病，通常称为羊水过少或羊水过多，可能对母亲或孩子的健康构成严重威胁。本文试图收集和比较基于人工智能（AI）的技术在诊断和分类 AFV 水平方面的最新进展。此外，我们还全面深入地分析了可能导致 AFV 水平异常的其他相关因素，包括但不限于胎盘、肾脏或中枢神经系统的异常，以及其他因素，如早产或双胎输血综合征。此外，我们还简要概述了所有机器学习（ML）和深度学习（DL）技术，以及不同研究人员提供的数据集。本研究还介绍了该领域遇到的挑战和机遇，以及未来的研究方向和有前途的探索角度。
