Amniotic Fluid Classification and Artificial Intelligence: Challenges and Opportunities

Abstract

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.

Chinese Population Reference Curves for Ultrasound-Measured Amniotic Fluid Deepest Vertical Pocket in Dichorionic Twin Pregnancies, and Their Associations With Pregnancy Outcomes

“…However, Khalil 24 went to great lengths to modify the diagnostic criteria for twin-to-twin transfusion syndrome before 18 weeks of gestation, reconsidering the DVP cutoff in the recipient twin to 6 cm, which could be used as a reference in addition to our study. Modern sensors, data analysis techniques, and even artificial intelligence algorithms 25 can now be used to automate amniotic fluid analysis and improve its accuracy. Likewise, more research will be needed to improve the quality of amniotic fluid and fetal monitoring in twin pregnancies.…”

Development of Artificial Intelligence Image Classification Models for Determination of Umbilical Cord Vascular Anomalies

“…1,2 First, there has been segmentation or "coloring" of the different anatomies to aid in identification and quantification. 7,9 Next, AI and ML have been applied to measurement and fetal risk assessments. 6,9,37 Finally, there was an AI model looking at umbilical cord coiling but when analyzing the figures included both 2v and 3v cords to classify coiling index.…”

“…Current applications of AI/ML include: predicting fetal distress in pregnancy-induced hypertension, classification of amniotic fluid levels, fetal anatomy colorization, and the umbilical coiling index. 2,[6][7][8][9] A practical application of AI image classification should involve increased maternal-fetal risk and be of added benefit to sonographers and obstetricians. Therefore, a clinical exemplar using standard ultrasound images that correlates to increased maternal-fetal risks should be pursued.…”

“…Some of these models are simple mathematical equations while others use automated feedback loop mechanisms originally theorized several decades ago. Current applications of AI/ML include: predicting fetal distress in pregnancy‐induced hypertension, classification of amniotic fluid levels, fetal anatomy colorization, and the umbilical coiling index 2,6–9 . A practical application of AI image classification should involve increased maternal‐fetal risk and be of added benefit to sonographers and obstetricians.…”

A Deep Learning Fusion Approach to Diagnosis the Polycystic Ovary Syndrome (PCOS)

“…Tis imaging technique ofers a great opportunity to develop a deep learning model for automatic analysis; it makes this examination more objective and accurate in diagnosis. Deep learning is a powerful approach utilized in imaging analysis and computer vision [8][9][10]. Automatic classifcation of PCOS in the earliest stage based on the ultrasound images and clinical data helps in disease recognition.…”