Noninvasive Fetal Lung Maturity Prediction Based on Amniotic Fluid Turbidity Using Ultrasonic Histogram Measurement Function

Background: Amniotic fluid turbidity increases with fetal lung maturation due to vernix and lung surfactant micelles suspended in the amniotic fluid. This study focused on this phenomenon and evaluated the presence or absence of respiratory distress syndrome (RDS)/transient tachypnea of the newborn (TTN) by quantitatively assessing the brightness of the amniotic fluid turbidity using a noninvasive ultrasound histogram measurement function. Methods: We included cases of singleton pregnancies managed at the Niigata University Medical and Dental Hospital between November 2020 and March 2022. Histograms of amniotic fluid turbidity were measured at the center of the amniotic fluid depth, avoiding the fetus, placenta, and umbilical cord, with the gain setting set to 0, and the average value was obtained after three measurements. Histograms of fetal urine in the bladder were measured similarly. The value obtained by subtracting the fetal bladder brightness value from the amniotic brightness value based on histogram measurements was used as the final amniotic fluid brightness value. Results: We included 118 cases (16 of RDS/TTN and 102 of control). The gestational age of delivery weeks was correlated with amniotic fluid brightness (Spearman’s rank correlation coefficient was 0.344;p = 0.00014). Amniotic fluid brightness values were significantly lower in the RDS/TTN group than in the control group (RDS/TTN: 16.2 ± 13.5, control: 26.3 ± 16.3;p = 0.020). The optimal cutoff value of amniotic fluid brightness to predict RDS/TTN was 20.3. For predicting RDS/TTN, the sensitivity, specificity, positive predictive value, and negative predictive value were 91.7%, 69.6%, 26.2%, and 94.1%, respectively. Conclusions: The quantitative value of the amniotic fluid brightness by histogram measurements may provide an easy and objective index for evaluating the presence or absence of RDS/TTN.

Prenatal assessment of normal fetal pulmonary grey-scale and lung volume by three-dimensional ultrasonography

Objective To quantitatively analyze the fetal lung echo and right lung volume in the third trimester by real-time three-dimensional ultrasound(3-D US)and evaluate the feasibility of fetal lung maturity.Methods A total of 732 women with normal singleton pregnancies between 28 and 42 weeks of gestation underwent ultrasound examination.The 3-D US equipment with a 3.5-5 MHz transabdominal transducer was used for the fetal biometric measurement.The echogenicity ratio between fetal lung and liver was compared.The fetal lung volume was calculated by the rotational multiplanar technique for volume measurement(VOCAL).Results The right fetal lung volume increased with the increase of gestational age with a linear positive correlation(r=0.884,P<0.01).After 34 weeks,the echogenicity ratio of fetal lung to liver was more than 1.1.Conclusion The echogenicity of lung/liver and fetal lung volume could be used as normal fetal predictable indicators for fetal lung maturity.

Lamellar Bodies Count (LBC) as a Predictor of Fetal Lung Maturity in Preterm Premature Rupture of Membranes Compared to Neonatal Assessment

Background: Respiratory distress syndrome (RDS) is a major cause of neonatal morbidity and mortality, affecting approximately 1% of all live births and 10% of all preterm infants. Lamellar bodies represent a storage form of pulmonary surfactant within Type II pneumocytes, secretion of which increases with advancing gestational age, thus enabling prediction of the degree of FLM. Preterm premature rupture of membranes (PPROM) complicates approximately 1/3 of all preterm births. Birth within 1 week is the most likely outcome for any patient with PPROM in the absence of adjunctive treatments. Respiratory distress has been reported to be the most common complication of preterm birth. Sepsis, intraventricular haemorrhage, and necrotizing enterocolitis also are associated with prematurity, but these are less common near to term. Objective: To assess the efficacy of the amniotic fluid lamellar body counting from a vaginal pool in predicting fetal lung maturity in women with preterm premature rupture of membranes. Methods: This study was conducted at Ain Shams University Maternity Hospital in the emergency ward from January 2019 to September 2019. It included 106 women with singleton pregnancies, gestational age from 28 - 36 weeks with preterm premature rupture of membranes. This study is designed to assess the efficacy of the amniotic fluid lamellar body counting (LBC) from a vaginal pool in predicting fetal lung maturity in women with preterm premature rupture of membranes. Results: The current study revealed a highly significant increase in the lamellar body count in cases giving birth to neonates without RDS compared to that cases giving birth to neonates with RDS. Also, no statistically significant difference between LBC and age, parity and number of previous miscarriages in the mother was found. Gestational age at delivery was significantly lower among cases with respiratory distress. Steroid administration was significantly less frequent among cases with respiratory distress. However, lamellar bodies had high diagnostic performance in the prediction of respiratory distress. Conclusion: Lamellar body count (LBC) is an effective, safe, easy, and cost-effective method to assess fetal lung maturity (FLM). It does not need a highly equipped laboratory or specially trained personnel, it just needs the conventional blood count analyzer. Measurement of LBC is now replacing the conventional Lecithin/Sphyngomyelin L/S ratio. LBC cut-off value of ≤42.5 × 103/μL can be used safely to decide fetal lung maturity with sensitivity of 95.7% and specificity of 97.6%.

Ultrasonographic Segmentation of Fetal Lung with Deep Learning

The morbidity and mortality of the fetus is related closely with the neonatal respiratory morbidity, which was caused by the immaturity of the fetal lung primarily. The amniocentesis has been used in clinics to evaluate the maturity of the fetal lung, which is invasive, expensive and time-consuming. Ultrasonography has been developed to examine the fetal lung quantitatively in the past decades as a non-invasive method. However, the contour of the fetal lung required by existing studies was delineated in manual. An automated segmentation approach could not only improve the objectiveness of those studies, but also offer a quantitative way to monitor the development of the fetal lung in terms of morphological parameters based on the segmentation. In view of this, we proposed a deep learning model for automated fetal lung segmentation and measurement. The model was constructed based on the U-Net. It was trained by 3500 data sets augmented from 250 ultrasound images with both the fetal lung and heart manually delineated, and then tested on 50 ultrasound data sets. With the proposed method, the fetal lung and cardiac area were automatically segmented with the accuracy, average IoU, sensitivity and precision being 0.98, 0.79, 0.881 and 0.886, respectively.

Effect of epidermal growth factor and dexamethasone on fetal rat lung development

Background Epidermal growth factor （EGF）, a mitogenic polypeptide that binds to cell surface receptors, is an important regulator of cell differentiation and fetal lung surfactant synthesis. We investigated the preventive and therapeutic effects of EGF in respiratory distress syndrome, by administering EGF and dexamethasone （Dex） to mother rat before delivery. Methods Six female Sprague-Dawley （SD） rats were assigned to three groups （2 rats each）; EGF or Dex was given to pregnant rats （EGF group and Dex group, respectively） from gestational day 16 to day 18 by intraperitoneal injection, while the group with normal saline injection was used as negative controls. Fetal rats were taken out of womb by hysterotomy on day 19 of pregnancy, then 24 fetal rats were randomly chosen from each group. Their body weights were measured, and pulmonary surfactant protein-A and -B （SP-A and SP-B） antigens were determined by immunohistochemical staining in each group. The histologic structure was examined under a light microscope, a light microscopic image system or an electron microscope. Results The expressions of SP-A and SP-B could be detected in each group. A significant difference was observed for SP-A and SP-B in the EGF and Dex groups compared with the control group （P 〈0.01）. Image analysis showed that the relative values of air space area and interalveolar septa area in the EGF and Dex groups were significantly greater than those in the control group （P 〈0.01）, while no significant difference was found between the two groups （P 〉0.05）. The ultrastructural features of fetal lungs showed that the number of alveolar type [I cells containing lamellar bodies in the EGF and Dex groups was apparently increased compared with that in the control group. The mean body weight of fetus from the Dex group was smaller than that from the control group （（1.3192＋0.0533） g, （1.3863_＋0.0373） g）, but there was no significant difference between the EGF group and the control group （（1.3986_＋0.0730） g, （1.3863_＋0.0373） g）. Conclusions Maternal treatment with EGF and Dex on days 16-18 of gestation could promote morphogenesis and increase the surfactant levels in premature fetal lung. However, maternal treatment with Dex, not EGF, decreased the body weight. Chin Med J 2009; 122（17）：2013-2016

Establish a normal fetal lung gestational age grading model and explore the potential value of deep learning algorithms in fetal lung maturity evaluation

Background:Prenatal evaluation of fetal lung maturity(FLM)is a challenge,and an effective non-invasive method for prenatal assessment of FLM is needed.The study aimed to establish a normal fetal lung gestational age(GA)grading model based on deep learning(DL)algorithms,validate the effectiveness of the model,and explore the potential value of DL algorithms in assessing FLM.Methods:A total of 7013 ultrasound images obtained from 1023 normal pregnancies between 20 and 41+6 weeks were analyzed in this study.There were no pregnancy-related complications that affected fetal lung development,and all infants were born without neonatal respiratory diseases.The images were divided into three classes based on the gestational week:class I:20 to 29+6 weeks,class II:30 to 36+6 weeks,and class III:37 to 41+6 weeks.There were 3323,2142,and 1548 images in each class,respectively.First,we performed a pre-processing algorithm to remove irrelevant information from each image.Then,a convolutional neural network was designed to identify different categories of fetal lung ultrasound images.Finally,we used ten-fold cross-validation to validate the performance of our model.This new machine learning algorithm automatically extracted and classified lung ultrasound image information related to GA.This was used to establish a grading model.The performance of the grading model was assessed using accuracy,sensitivity,specificity,and receiver operating characteristic curves.Results:A normal fetal lung GA grading model was established and validated.The sensitivity of each class in the independent test set was 91.7%,69.8%,and 86.4%,respectively.The specificity of each class in the independent test set was 76.8%,90.0%,and 83.1%,respectively.The total accuracy was 83.8%.The area under the curve(AUC)of each class was 0.982,0.907,and 0.960,respectively.The micro-average AUC was 0.957,and the macro-average AUC was 0.949.Conclusions:The normal fetal lung GA grading model could accurately identify ultrasound images of the fetal lung at different GAs,which can be used to identify cases of abnormal lung development due to gestational diseases and evaluate lung maturity after antenatal corticosteroid therapy.The results indicate that DL algorithms can be used as a non-invasive method to predict FLM.

Bisphenol A exposure alters release of immune and developmental modulators and expression of estrogen receptors in human fetal lung fibroblasts

Bisphenol A （BPA） has been shown to exert biological effects through estrogen receptor （ER）-dependent and ER-independent mechanisms. Recent studies suggest that prenatal exposure to BPA may increase the risk of childhood asthma. To investigate the underlying mechanisms in the actions of BPA, human fetal lung fibroblasts {hFLFs） were exposed to varying doses of BPA in culture for 24 hr. Effects of BPA on localization and uptake of BPA, cell viability, release of immune and developmental modulators, cellular localization and expression of ERa, ERβ and G-protein coupled estrogen receptor 30 （GPR30）, and effects of ERs antagonists on BPA-induced changes in endothelin-1 （ET-1） release were examined. BPA at 0.01-100 μmol/L caused no changes in cell viability after 24 hr of exposure, hFLFs expresses all three ERs. BPA had no effects on either cellular distribution or protein expression of ERa, however, at 100 μmol/L （or 23 μmol/L intracellular BPA） increased ERβ protein levels in the cytoplasmic fractions and GPR30 protein levels in the nuclear fractions. These paralleled with increased release of growth differentiation factor-15, decreased phosphorylation of nuclear factor kappa B p65 at serine 536, and decreased release of ET-1, interleukin-6, and interferon gamma-induced protein 10. ERs antagonists had no effects on BPA-induced decrease in ET-1 release. These data suggest that BPA at 100 μmol/L altered the release of immune and developmental modulators in hFLFs, which may negatively influence fetal lung development, maturation, and susceptibility to environmental stressors, although the role of BPA in childhood asthma remains to be confirmed in in viuo studies.