Purpose: The goal of this study was to introduce PFCnet (placental features classification network), an multimodel model for evaluating and classifying placental features in gestational diabetes mellitus (GDM) and normal late pregnancy. Deep learning algorithms could be utilized to fully automate the examination of alterations in the placenta caused by hyperglycemia.Methods: A total of 718 placental ultrasound images, including 139 cases of GDM, were collected, including gray-scale images (GSIs) and microflow images (MFIs). Ultrasonic assessment parameters and perinatal features were recorded. We divided gestational age into two categories for analysis (37 weeks and 37 weeks) based on the cut-off value level of placental maturity. The PFCnet model was introduced for identifying placental characteristics from normal and GDM pregnancies after extensive training and optimization. The model was scored using metrics such as sensitivity, specificity, accuracy, and the area under the curve (AUC).Results: In view of multimodal fusion (GSIs and MFIs) and deep network optimization
AIM: The aim of this study was to investigate the role of cerebroplacental ratio (CPR) in the final prenatal care for neonatal respiratory diseases and to analyze the risk of relevant factors associated with neonatal respiratory disorders. METHODS: A prospective cohort study of 795 singleton pregnancies was conducted. The pulsatility indices (PI) of the umbilical artery (UA) and the middle cerebral artery (MCA) were measured, and the MCA to UA ratio (CPR) was determined. The severity of the case is determined by whether or not the newborn has respiratory problems. Compare the CPR correlation between the two groups and examine the illness prediction factors through a binary logistic regression method. RESULTS: Of the 801 participants, 114 had neonatal respiratory disorders. The mean values of CPR between neonatal respiratory diseases group and control group were 1.78±0.6, 1.97±0.9, respectively (P < 0.001). Maternal age, abortion history, cesarean section history, placental thickness, placental maturity, and amniotic fluid index (AFI) were determined to have no significant link between the two groups after comparison analysis (P > 0.05). It could be found that compared with the control group, CPR MoM indicators of neonatal respiratory distress syndrome, neonatal pneumonia and wet lung disease all show significant decreases. In binary logistic regression analysis, among the variables included in the model, CPR (OR:2.90, P = 0.015), fetal heart monitoring (OR:5.26, P < 0.001), delivery mode (OR:2.86, P < 0.001) and gestational age of delivery (OR:0.92, P < 0.001) were statistically significant in both groups. CONCLUSION: The findings of this study showed that infant respiratory problems were substantially related to CPR value. The correlation indicates that CPR was a powerful reference marker for respiratory disorders.
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