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%.

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.