Predictive value of fetal hepatic biometry for birth weight and cord blood markers in gestational diabetes

Fetal growth analysis from ultrasound videos based on different biometrics using optimal segmentation and hybrid classifier

Birth defects and their associated deaths, high health and financial costs of maternal care and associated morbidity are major contributors to infant mortality. If permitted by law, prenatal diagnosis allows for intrauterine care, more complicated hospital deliveries, and termination of pregnancy. During pregnancy, a set of measurements is commonly used to monitor the fetal health, including fetal head circumference, crown-rump length, abdominal circumference, and femur length. Because of the intricate interactions between the biological tissues and the US waves mother and fetus, analyzing fetal US images from a specialized perspective is difficult. Artifacts include acoustic shadows, speckle noise, motion blur, and missing borders. The fetus moves quickly, body structures close, and the weeks of pregnancy vary greatly. In this work, we propose a fetal growth analysis through US image of head circumference biometry using optimal segmentation and hybrid classifier. First, we introduce a hybrid whale with oppositional fruit fly optimization (WOFF) algorithm for optimal segmentation of segment fetal head which improves the detection accuracy. Next, an improved U-Net design is utilized for the hidden feature (head circumference biometry) extraction which extracts features from the segmented extraction. Then, we design a modified Boosting arithmetic optimization (MBAO) algorithm for feature optimization to selects optimal best features among multiple features for the reduction of data dimensionality issues. Furthermore, a hybrid deep learning technique called bi-directional LSTM with convolutional neural network (B-LSTM-CNN) for fetal growth analysis to compute the fetus growth and health. Finally, we validate our proposed method through the open benchmark datasets are HC18 (Ultrasound image) and oxford university research archive (ORA-data) (Ultrasound video frames). We compared the simulation results of our proposed algorithm with the existing state-of-art techniques in terms of various metrics.

Association between fetal liver diameter and glycemic control in pregnant women with gestational diabetes: A pilot study

BACKGROUND

This study aimed to investigate the relationship between fetal liver length (FLL) and maternal glycemic status in pregnant women with gestational diabetes mellitus (GDM), as well as to determine whether FLL measurement in the third trimester is associated with neonatal outcomes.

METHOD

A total of 51 singleton GDM pregnancies were included in this pilot study, and transabdominal ultrasound biometry and FLL measurements were performed between 34 and 36 weeks of gestation. Maternal indicators of glycemic control, including hemoglobin A1C (HbA1C), fasting blood sugar (FBS), and 2-h postprandial blood sugar were also evaluated during this period. The cases were followed up until delivery and maternal and neonatal outcomes were assessed to determine any correlation with FLL.

RESULT

The results showed a significant positive correlation between FLL and HbA1C (r = 0.464, P = 0.001), FBS (r = 0.574, P < 0.001), 2-h postprandial blood sugar (r = 0.405, P = 0.002), and AC (r = 0.515, P < 0.001). Additionally, FLL was significantly associated with fetal birth weight (r = 0.408, P = 0.003) and birth weight (r = 0.460, P = 0.001). The FLL≥95th percentile group demonstrated a higher number of polyhydramnios (p = 0.007), macrosomia (p < 0.001), and maternal intensive care unit (ICU) admissions (p = 0.006).

CONCLUSION

In conclusion, FLL measurement during third trimester of pregnancy is an indicator of maternal glycemic regulation and can be used as a predictor of macrosomia and neonatal birth weight in GDM pregnancies.

Temporal Leptin to Determine Cardiovascular and Metabolic Fate throughout the Life

Leptin links peripheral adiposity and the central nervous system (CNS) to regulate cardiometabolic physiology. Within the CNS, leptin receptor-expressing cells are a counterpart to circulating leptin, and leptin receptor-mediated neural networks modulate the output of neuroendocrine and sympathetic nervous activity to balance cardiometabolic homeostasis. Therefore, disrupted CNS leptin signaling is directly implicated in the development of metabolic diseases, such as hypertension, obesity, and type 2 diabetes. Independently, maternal leptin also plays a central role in the development and growth of the infant during gestation. Accumulating evidence points to the dynamic maternal leptin environment as a predictor of cardiometabolic fate in their offspring as it is directly associated with infant metabolic parameters at birth. In postnatal life, the degree of serum leptin is representative of the level of body adiposity/weight, a driving factor for cardiometabolic alterations, and therefore, the levels of blood leptin through the CNS mechanism, in a large part, are a strong determinant for future cardiometabolic fate. The current review focuses on highlighting and discussing recent updates for temporal dissection of leptin-associated programing of future cardiometabolic fate throughout the entire life.