Estimation Of Gestational Age Research Articles

Fetal Gestational Age Prediction in Brain Magnetic Resonance Imaging Using Artificial Intelligence: A Comparative Study of Three Biometric Techniques

Accurately predicting a fetus’s gestational age (GA) is crucial in prenatal care. This study aimed to develop an artificial intelligence (AI) model to predict GA using biometric measurements from fetal brain magnetic resonance imaging (MRI). We assessed the significance of using different reference standards for interpreting GA predictions. Measurements of biparietal diameter (BPD), fronto-occipital diameter (FOD), and head circumference (HC) were obtained from 52 normal fetal MRI cases from Rush University. Both manual and AI-based methods were utilized, and comparisons were made using three reference standards (Garel, Freq, and Bio). The AI model showed a strong correlation with manual measurements, particularly for HC, which exhibited the highest correlation with actual values. Differences between GA predictions and picture archiving and communication system (PACS) records varied by reference, ranging from 0.47 to 2.17 weeks for BPD, 0.46 to 2.26 weeks for FOD, and 0.75 to 1.74 weeks for HC. Pearson correlation coefficients between PACS records and GA predictions exceeded 0.97 across all references. In conclusion, the AI model demonstrated high accuracy in predicting GA from fetal brain MRI measurements. This approach offers improved accuracy and convenience over manual methods, highlighting the potential of AI in enhancing prenatal care through precise GA estimation.

Post-natal gestational age assessment using targeted metabolites of neonatal heel prick and umbilical cord blood: A GARBH-Ini cohort study from North India.

Accurate assessment of gestational age (GA) and identification of preterm birth (PTB) at delivery is essential to guide appropriate post-natal clinical care. Undoubtedly, dating ultrasound sonography (USG) is the gold standard to ascertain GA, but is not accessible to the majority of pregnant women in low- and middle-income countries (LMICs), particularly in rural areas and small secondary care hospitals. Conventional methods of post-natal GA assessment are not reliable at delivery and are further compounded by a lack of trained personnel to conduct them. We aimed to develop a population-specific GA model using integrated clinical and biochemical variables measured at delivery. We acquired metabolic profiles on paired neonatal heel prick (nHP) and umbilical cord blood (uCB) dried blood spot (DBS) samples (n = 1278). The master data set consists of 31 predictors from nHP and 24 from uCB after feature selection. These selected predictors including biochemical analytes, birth weight, and placental weight were considered for the development of population-specific GA estimation and birth outcome classification models using eXtreme Gradient Boosting (XGBoost) algorithm. The nHP and uCB full model revealed root mean square error (RMSE) of 1.14 (95% confidence interval (CI) = 0.82-1.18) and of 1.26 (95% CI = 0.88-1.32) to estimate the GA as compared to actual GA, respectively. In addition, these models correctly estimated 87.9 to 92.5% of the infants within ±2 weeks of the actual GA. The classification models also performed as the best fit to discriminate the PTB from term birth (TB) infants with an area under curve (AUC) of 0.89 (95% CI = 0.84-0.94) for nHP and an AUC of 0.89 (95% CI = 0.85-0.95) for uCB. The biochemical analytes along with clinical variables in the nHP and uCB data sets provide higher accuracy in predicting GA. These models also performed as the best fit to identify PTB infants at delivery.

Accuracy of portable ultrasound machines for obstetric biometry.

To evaluate the accuracy of two portable ultrasound machines (PUM) in assessing fetal biometry and estimated gestational age (EGA). This was a secondary analysis of data from the Fetal Age Machine Learning Initiative, an observational study of pregnant women in the USA and Zambia. Each participant underwent ultrasound assessment by an experienced sonographer using both a high-specification ultrasound machine (HSUM) and a PUM (Butterfly iQ or Clarius C3) to measure fetal biometry and calculate EGA at each visit. By comparing paired PUM and HSUM scans, we estimated agreement between individual biometry measurements and aggregate gestational age estimates by reporting mean difference, intraclass correlation coefficient (ICC) and Bland-Altman plots, adjusting for trend. Between April and December 2021, 818 participants contributed 1386 paired PUM-HSUM ultrasound investigations, of which 991 PUM scans were obtained using the Butterfly iQ device and 395 using the Clarius C3 device. Gestational age at scan ranged from 7 to 38 weeks. Compared with HSUM, the Butterfly iQ PUM had a mean difference of -0.20 (95% CI, -0.60 to 0.20) days in the first trimester and -0.68 (95% CI, -0.93 to -0.44) days in the second/third trimesters. Compared with HSUM, the Clarius C3 PUM had a mean difference of -0.47 (95% CI, -1.11 to 0.18) days in the first trimester and -1.67 (95% CI, -2.10 to -1.25) days in the second/third trimesters. ICCs were 0.989 or greater throughout. Increasing gestational age was associated with increasing error and absolute error in EGA and fetal biometry. Both PUM devices demonstrated a modest trend toward underestimation of EGA with advancing gestational age in second/third-trimester scans, compared with HSUM. The Butterfly iQ and Clarius C3 PUM devices were highly accurate in performing fetal biometry in a diverse population from the USA and Zambia. © 2023 International Society of Ultrasound in Obstetrics and Gynecology.

Systematic review and meta-analysis of birth weight and perfluorohexane sulfonate exposures: examination of sample timing and study confidence

We examined the association between mean birth weight (BW) differences and perfluorohexane sulfonate (PFHxS) exposure biomarkers.We fit a random effects model to estimate the overall pooled effect and for different...

A study of correlation between foot length and gestational maturity and anthropometric measurements in neonates

Background: This pinnacle’s the importance for alternative measurements which predicts birth weight and gestational age (GA). Foot length is a simple measure and does not require expertise. The aim of this study was to study correlation of foot length and GA among preterm, term and post-term neonates. Methods: This study was a prospective observational study done in 155 babies. Anthropometric measurements were taken within the first 24 hours of life. GA estimation was done using modified Ballard score. Foot length, head circumference and chest circumference were measured and noted. Weight of the baby was recorded using electronic weighing scale. Results: Foot length statistically correlated (p<0.05) with GA assessment using NBS, weight, length, head circumference and chest circumference. The highest correlation of foot length in term SGA and term AGA babies for foot length was with head circumference (r=0.74 and 0.64 respectively). In pre-terms, foot length correlated well with head circumference and birth weight (r=0.92, 0.84 and 0.92 respectively). There were no babies in preterm LGA group and post term SGA and LGA group. Conclusions: Foot length also statistically correlated with other parameters like birth weight, length, head circumference and chest circumference.

Internal validation of gestational age estimation algorithms in healthcare databases using pregnancies conceived with fertility procedures.

Fertility procedures recorded in healthcare databases can be used to estimate the start of pregnancy, which can serve as a reference standard to validate gestational age estimates based on International Classification of Diseases (ICD) codes. In a cohort of 17,398 pregnancies conceived by fertility procedures in MarketScan (2011-2020), we estimated gestational age at the end of pregnancy using algorithms based on (1) days since fertility procedure (the reference); (2) ICD-9/ICD-10 (before/after October 2015) codes indicating gestational length recorded at the end of pregnancy (method A); and (3) ICD-10 enhanced with Z3A codes denoting specific gestation weeks recorded at prenatal visits (method B). We calculated the proportion of pregnancies with an estimated gestational age within 14 days of the reference. Method A accuracy was similar for ICD-9 and ICD-10 codes. After 2015, method B was more accurate than method A: For term births, within-14-day-agreements were 90.8% for method A and 98.7% for method B. Corresponding estimates were 70.1% and 95.6% for preterm births; 35.3% and 92.6% for stillbirths; 54.3% and 64.2% for spontaneous abortions; and 16.7% and 84.6% for elective terminations. ICD-10-based algorithms that incorporate Z3A codes improve the accuracy of gestational age estimation in healthcare databases, especially for preterm and non-live births.

Accuracy of prenatal and postnatal biomarkers for estimating gestational age: a systematic review and meta-analysis

Knowledge of gestational age (GA) is key in clinical management of individual obstetric patients, and critical to be able to calculate rates of preterm birth and small for GA at a population level. Currently, the gold standard for pregnancy dating is measurement of the fetal crown rump length at 11-14 weeks of gestation. However, this is not possible for women first presenting in later pregnancy, or in settings where routine ultrasound is not available. A reliable, cheap and easy to measure GA-dependent biomarker would provide an important breakthrough in estimating the age of pregnancy. Therefore, the aim of this study was to determine the accuracy of prenatal and postnatal biomarkers for estimating gestational age (GA). Systematic review prospectively registered with PROSPERO (CRD42020167727) and reported in accordance with the PRISMA-DTA. Medline, Embase, CINAHL, LILACS, and other databases were searched from inception until September 2023 for cohort or cross-sectional studies that reported on the accuracy of prenatal and postnatal biomarkers for estimating GA. In addition, we searched Google Scholar and screened proceedings of relevant conferences and reference lists of identified studies and relevant reviews. There were no language or date restrictions. Pooled coefficients of correlation and root mean square error (RMSE, average deviation in weeks between the GA estimated by the biomarker and that estimated by the gold standard method) were calculated. The risk of bias in each included study was also assessed. Thirty-nine studies fulfilled the inclusion criteria: 20 studies (2,050 women) assessed prenatal biomarkers (placental hormones, metabolomic profiles, proteomics, cell-free RNA transcripts, and exon-level gene expression), and 19 (1,738,652 newborns) assessed postnatal biomarkers (metabolomic profiles, DNA methylation profiles, and fetal haematological components). Among the prenatal biomarkers assessed, human chorionic gonadotrophin measured in maternal serum between 4 and 9 weeks of gestation showed the highest correlation with the reference standard GA, with a pooled coefficient of correlation of 0.88. Among the postnatal biomarkers assessed, metabolomic profiling from newborn blood spots provided the most accurate estimate of GA, with a pooled RMSE of 1.03 weeks across all GAs. It performed best for term infants with a slightly reduced accuracy for preterm or small for GA infants. The pooled RMSEs for metabolomic profiling and DNA methylation profile from cord blood samples were 1.57 and 1.60 weeks, respectively. We identified no antenatal biomarkers that accurately predict GA over a wide window of pregnancy. Postnatally, metabolomic profiling from newborn blood spot provides an accurate estimate of GA, however, as this is known only after birth it is not useful to guide antenatal care. Further prenatal studies are needed to identify biomarkers that can be used in isolation, as part of a biomarker panel, or in combination with other clinical methods to narrow prediction intervals of GA estimation. The research was funded by the Bill and Melinda Gates Foundation (INV-000368). ATP is supported by the Oxford Partnership Comprehensive Biomedical Research Centre with funding from the NIHR Biomedical Research Centre funding scheme. The views expressed are those of the authors and not necessarily those of the UK National Health Service, the NIHR, the Department of Health, or the Department of Biotechnology. The funders of this study had no role in study design, data collection, analysis or interpretation of the data, in writing the paper or the decision to submit for publication.

Estimation of Gestational Age by Using Fetal Kidney Length and Transcerebellar Diameter in Comparison with Other Biometric Indices

Estimation of Gestational Age by Using Fetal Kidney Length and Transcerebellar Diameter in Comparison with Other Biometric Indices

Development and external validation of Indian population-specific Garbhini-GA2 model for estimating gestational age in second and third trimesters

A large proportion of pregnant women in lower and middle-income countries (LMIC) seek their first antenatal care after 14 weeks of gestation. While the last menstrual period (LMP) is still the most prevalent method of determining gestational age (GA), ultrasound-based foetal biometry is considered more accurate in the second and third trimesters. In LMIC settings, the Hadlock formula, originally developed using data from a small Caucasian population, is widely used for estimating GA and foetal weight worldwide as the pre-programmed formula in ultrasound machines. This approach can lead to inaccuracies when estimating GA in a diverse population. Therefore, this study aimed to develop a population-specific model for estimating GA in the late trimesters that was as accurate as the GA estimation in the first trimester, using data from GARBH-Ini, a pregnancy cohort in a North Indian district hospital, and subsequently validate the model in an independent cohort in South India. Data obtained by longitudinal ultrasonography across all trimesters of pregnancy was used to develop and validate GA models for the second and third trimesters. The gold standard for GA estimation in the first trimester was determined using ultrasonography. The Garbhini-GA2, a polynomial regression model, was developed using the genetic algorithm-based method, showcasing the best performance among the models considered. This model incorporated three of the five routinely measured ultrasonographic parameters during the second and third trimesters. To assess its performance, the Garbhini-GA2 model was compared against the Hadlock and INTERGROWTH-21st models using both the TEST set (N=1493) from the GARBH-Ini cohort and an independent VALIDATION dataset (N=948) from the Christian Medical College (CMC), Vellore cohort. Evaluation metrics, including root-mean-squared error, bias, and preterm birth (PTB) rates, were utilised to comprehensively assess the model's accuracy and reliability. With first trimester GA dating as the baseline, Garbhini-GA2 reduced the GA estimation median error by more than three times compared to the Hadlock formula. Further, the PTB rate estimated using Garbhini-GA2 was more accurate when compared to the INTERGROWTH-21st and Hadlock formulae, which overestimated the rate by 22.47% and 58.91%, respectively. The Garbhini-GA2 is the first late-trimester GA estimation model to be developed and validated using Indian population data. Its higher accuracy in GA estimation, comparable to GA estimation in the first trimester and PTB classification, underscores the significance of deploying population-specific GA formulae to enhance antenatal care. The GARBH-Ini cohort study was funded by the Department of Biotechnology, Government of India (BT/PR9983/MED/97/194/2013). The ultrasound repository was partly supported by the Grand Challenges India-All Children Thriving Program, Biotechnology Industry Research Assistance Council, Department of Biotechnology, Government of India (BIRAC/GCI/0115/03/14-ACT). The research reported in this publication was made possible by a grant (BT/kiData0394/06/18) from the Grand Challenges India at Biotechnology Industry Research Assistance Council (BIRAC), an operating division jointly supported by DBT-BMGF-BIRAC. The external validation study at CMC Vellore was partly supported by a grant (BT/kiData0394/06/18) from the Grand Challenges India at Biotechnology Industry Research Assistance Council (BIRAC), an operating division jointly supported by DBT-BMGF-BIRAC and by Exploratory Research Grant (SB/20-21/0602/BT/RBCX/008481) from Robert Bosch Centre for Data Science and Artificial Intelligence (RBCDSAI), IIT Madras. An alum endowment from Prakash Arunachalam (BIO/18-19/304/ALUM/KARH) partly funded this study at the Centre for Integrative Biology and Systems Medicine, IIT Madras.

Evaluation of fetal kidney length as a marker for fetal biometry.

The precise determination of gestational age is essential for effectively managing and prognosis of all pregnancies. Through careful biometry, timely interventions can be implemented, leading to positive outcomes for both the mother and fetus. In routine fetal biometry, parameters such as biparietal diameter (BPD), femur length (FL), head circumference (HC), and abdominal circumference (AC) have been traditionally used. This study aims to evaluate the usefulness of fetal kidney length (FKL) as a marker for fetal biometry. This prospective, observational, and cross-sectional study was conducted in the Radiodiagnosis and Obstetrics and Gynaecology departments, including a diverse group of pregnant women from various socio-economic backgrounds, with adherence to ethical standards. Women with singleton pregnancies between 22 and 40weeks of gestation who met the inclusion and exclusion criteria were examined through ultrasound. The data collected were subsequently analyzed. In the current study, 280 participants with an average age of 26.71 ± 3.6years were included. The agreement between the mean fetal kidney length and standard biometry parameters was almost perfect, with a strength of agreement exceeding 0.99. A strong and statistically significant positive correlation existed between fetal kidney length and the estimated gestational period calculated using DLMP/standard biometric measurements. Fetal kidney length is a reliable indicator of gestational age and can supplement standard biometric measurements to provide a more precise estimation of gestational age, especially in the later stages when obtaining such standard measurements may be challenging.

Prenatal cocaine exposure and its influence on pediatric epigenetic clocks and epigenetic scores in humans

The investigation of the effects of prenatal cocaine exposure (PCE) on offspring has been inconsistent, with few studies investigating biological outcomes in humans. We profiled genome-wide DNA methylation (DNAm) of umbilical cord blood (UCB) from newborns with (n = 35) and without (n = 47) PCE. We used DNAm data to (1) assess pediatric epigenetic clocks at birth and (2) to estimate epigenetic scores (ES) for lifetime disorders. We generated gestational epigenetic age estimates (DNAmGA) based on Knight and Bohlin epigenetic clocks. We also investigated the association between DNAmGA and UCB serum brain-derived neurotrophic factor (BDNF) levels. Considering the large-scale DNAm data availability and existing evidence regarding PCE as a risk for health problems later in life, we generated ES for tobacco smoking, psychosis, autism, diabetes, and obesity. A gene ontology (GO) analysis on the CpGs included in the ES with group differences was performed. PCE was associated with lower DNAmGA in newborns, and this effect remained significant when controlling for potential confounders, such as blood cell type composition predicted by DNAm and obstetric data. DNAmGA was negatively correlated with BDNF levels in the serum of UCB. Higher tobacco smoking, psychosis, and diabetes ES were found in the PCE group. The GO analysis revealed GABAergic synapses as a potential pathway altered by PCE. Our findings of decelerated DNAmGA and ES for adverse phenotypes associated with PCE, suggest that the effects of gestational cocaine exposure on the epigenetic landscape of human newborns are detectable at birth.

Problems with gestational age estimation by last menstrual period and ultrasound among late antenatal care attendant women in a low-resource setting in Africa, Sudan.

Accurate estimation of gestational age is essential to interpret and manage several maternal and perinatal indicators. Last menstrual period (LMP) and ultrasound are the two most common methods used for estimating gestational age. There are few published studies comparing the use of LMP and ultrasound in Sub-Saharan Africa to estimate gestational age and no studies on this topic in Sudan. A cross-sectional study was conducted in Gadarif Maternity Hospital in Sudanduring November through December 2022. Sociodemographic information was collected, and the date of the first day of each participant's LMP was recorded. Ultrasound examinations were performed (measuring crown-rump length in early pregnancy and biparietal diameter and femur length in late pregnancy) using a 3.5-MHz electronic convex sector probe. Bland-Altman analysis was performed. Four-hundred seventy-six pregnant women were enrolled. The median (interquartile range [IQR]) age and gravidity was 24.0 (20.0‒29.0) years and 2 (1‒4), respectively. There was a strong positive correlation between gestational age determined by LMP and ultrasound (r = 0.921, P < 0.001). The mean gestational age estimate according to LMP was higher than that determined by ultrasound, with a difference, on average, of 0.01 week (95% confidence interval [CI]: - 0.05, 0.07). Bland-Altman analysis showed the limits of agreement varied from - 1.36 to 1.38 weeks. A linear regression analysis showed proportional bias. The coefficient of difference of the mean was equal to 0.26 (95% CI: 0.01, 0.03, P < 0.001). Based on our results, there was a bias in LMP-based gestational age estimates when compared with the reproducible method (ultrasound).

Utilization of third-trimester fetal transcerebellar diameter measurement for gestational age estimation: a comparative study using Bland-Altman analysis

BackgroundSeveral studies show that gestational estimation during third trimester using fetal transcerbellar diameter (TCD) is superior to that measured by fetal biometry (BPD, HC, AC, and FL). However, the conclusions of studies stem from findings of correlation and regression statistical tests, which is not the recommended statistical analysis method for comparing the values of one variable as measured by two different methods. ObjectiveTo compare the accuracy of gestational age estimation by TCD to that by fetal biometry during the third trimester using Bland-Altman statistical analysis. MethodsThis was a cross-sectional study on pregnant women who presented for routine antenatal care follow-up in the third trimester (28-41 weeks) at St. Paul's Hospital Millennium Medical College (Ethiopia) between November 1, 2020, and February 28, 2021. Data were collected prospectively using a structured questionnaire on Open Data Kit (ODK). The primary outcome of our study was mean bias of gestational estimation (error in estimating gestational age) using TCD and fetal biometry (composite GA). Data were analyzed using STATA 15.p.l.c. Simple descriptive analysis, Bland-Altman(B&A) analysis, and Kendall's τa discordance measurement were performed as appropriate. Mean bias(error) and limits of agreement were used to present the finding significance. ResultsA total of 104 pregnant women in the third trimester were included in the study. The mean error(bias) when TCD was used to estimate the gestational age was 0.65 weeks versus a bias of 1.1 weeks by composite biometry, compared to gold standard method from crown-lump length (CRL) (in both cases). The calculated estimated limits of agreement was narrower in the case of TCD than by composite-fetal biometry (-3.56 to 2.25 vs -4.73 to 2.53). Kendall's τa discordance measurement revealed that gestational age estimation using composite biometry and that calculated from CRL were 51% to 70% more likely to agree than disagree whereas gestational age calculated from TCD and CRL were 62% to 77% more likely to agree than to disagree (P-value= <0.001). ConclusionGestational age calculation using TCD is more accurate than using composite gestational age (BPD, HC, FL, and AC) and it should be utilized to date third trimester pregnancies with unknown gestational age (unknown LNMP with no early ultrasound milestone).

Deep learning to estimate gestational age from fly-to cineloop videos: A novel approach to ultrasound quality control.

Low-cost devices have made obstetric sonography possible in settings where it was previously unfeasible, but ensuring quality and consistency at scale remains a challenge. In the present study, we sought to create a tool to reduce substandard fetal biometry measurement while minimizing care disruption. We developed a deep learning artificial intelligence (AI) model to estimate gestational age (GA) in the second and third trimester from fly-to cineloops-brief videos acquired during routine ultrasound biometry-and evaluated its performance in comparison to expert sonographer measurement. We then introduced random error into fetal biometry measurements and analyzed the ability of the AI model to flag grossly inaccurate measurements such as those that might be obtained by a novice. The mean absolute error (MAE) of our model (±standard error) was 3.87 ± 0.07 days, compared to 4.80 ± 0.10 days for expert biometry (difference -0.92 days; 95% CI: -1.10 to -0.76). Based on simulated novice biometry with average absolute error of 7.5%, our model reliably detected cases where novice biometry differed from expert biometry by 10 days or more, with an area under the receiver operating characteristics curve of 0.93 (95% CI: 0.92, 0.95), sensitivity of 81.0% (95% CI: 77.9, 83.8), and specificity of 89.9% (95% CI: 88.1, 91.5). These results held across a range of sensitivity analyses, including where the model was provided suboptimal truncated fly-to cineloops. Our AI model estimated GA more accurately than expert biometry. Because fly-to cineloop videos can be obtained without any change to sonographer workflow, the model represents a no-cost guardrail that could be incorporated into both low-cost and commercial ultrasound devices to prevent reporting of most gross GA estimation errors.

Tissue Contamination Challenges the Credibility of Machine Learning Models in Real World Digital Pathology

Machine learning (ML) models are poised to transform surgical pathology practice. The most successful use attention mechanisms to examine whole slides, identify which areas of tissue are diagnostic, and use them to guide diagnosis. Tissue contaminants, such as floaters, represent unexpected tissue. While human pathologists are extensively trained to consider and detect tissue contaminants, we examined their impact on ML models.We trained 4 whole slide models. Three operate in placenta for 1) detection of decidual arteriopathy (DA), 2) estimation of gestational age (GA), and 3) classification of macroscopic placental lesions. We also developed a model to detect prostate cancer in needle biopsies. We designed experiments wherein patches of contaminant tissue are randomly sampled from known slides and digitally added to patient slides and measured model performance. We measured the proportion of attention given to contaminants and examined the impact of contaminants in T-distributed Stochastic Neighbor Embedding (tSNE) feature space.Every model showed performance degradation in response to one or more tissue contaminants. DA detection balanced accuracy decreased from 0.74 to 0.69 +/- 0.01 with addition of 1 patch of prostate tissue for every 100 patches of placenta (1% contaminant). Bladder, added at 10% contaminant raised the mean absolute error in estimating gestation age from 1.626 weeks to 2.371 +/ 0.003 weeks. Blood, incorporated into placental sections, induced false negative diagnoses of intervillous thrombi. Addition of bladder to prostate cancer needle biopsies induced false positives, a selection of high-attention patches, representing 0.033mm2, resulted in a 97% false positive rate when added to needle biopsies. Contaminant patches received attention at or above the rate of the average patch of patient tissue.Tissue contaminants induce errors in modern ML models. The high level of attention given to contaminants indicates a failure to encode biological phenomena. Practitioners should move to quantify and ameliorate this problem.

Estimation of gestational age in buffaloes (Bubalus bubalis) by transabdominal and transrectal ultrasonography

The aims of the presented study were (1) to examine the relationship between foetal measurements and gestational age (GA), (2) to generate GA formulas, and (3) to investigate the estimation of GA by transabdominal ultrasonography in buffaloes. Thirteen pregnant buffaloes were used in the study. Transrectal ultrasonography was performed between Day (D) 28 and 112 of gestational age, whereas transabdominal ultrasonography was between 126 and 294. The diameters of embryonic (EVD) and amniotic (AVD) vesicles, crown-rump length (CRL), occipito-nasal length (ONL), biparietal diameter (BPD), orbit diameter (OD), cervical, thoracic, lumbar and coccygeal vertebrae lengths (CVL, TVL, LVL, CcVL), abdominal diameter (AD), chest diameter (CD), umbilical cord diameter (UCD), scapula, humerus, radius-ulna, metacarpus, femur, tibia, and metatarsus lengths (SL, HL, RUL, McL, FL, TL, MtL), diameters of transversal heart (THD), stomach (SD), kidney (KD), and outer diameter, circumference and area of placentomas (OPD, OPC, OPA) were measured by ultrasonography. All 26 parameters were highly correlated with GA (r = 0.968 – 0.999). The observation of the foetus was evident in all animals via transabdominal ultrasonography, and all parameters except EVD, AVD, and CRL could be measured on D 126. In addition, heartbeats, the sign of foetal vitality, could be observed in 11 of 13 living foetuses. This study is the first to demonstrate that transabdominal ultrasonography can be used to estimate GA in buffaloes. In addition, GA formulas related to ONL, CVL, TVL, LVL, CcVL, extremity bone lengths, THD, UCD, PC, and PA measurements were created for the first time in buffaloes.

Unveiling sex bias and adverse neonatal outcomes in ultrasound estimation of gestational age: A population-based cohort study.

Gestational age estimation by second-trimester ultrasound biometry introduces systematic errors due to sex differences in early foetal growth, consequently increasing the risk of adverse neonatal outcomes. Ultrasound estimation earlier in pregnancy may reduce this bias. To investigate the distribution of sex ratio by gestational age and estimate the risk of adverse outcomes in male foetuses born early-term and female foetuses born post-term by first- and second-trimester ultrasound estimations. This population-based study compared two cohorts of births with gestational age based on first- and second-trimester ultrasound in the Medical Birth Registry of Norway between 2016 and 2020. We used a log-binomial regression model to estimate adjusted relative risk (RR) with 95% confidence interval (CI) for Apgar score <7 at 5 min, umbilical artery pH <7.05, neonatal intensive care unit (NICU) admission and respiratory morbidity in relation to foetal sex. The sex ratio at birth in gestational weeks 36-43 showed less male predominance in pregnancies estimated in first compared to second trimester. Any adverse outcome was registered in 627 of 4470 male infants born in gestational weeks 37-38 and 618 of 6406 females born ≥41 weeks. Male infants born in weeks 37-38 had lower risk of NICU admission (RR 0.76, 95% CI 0.58, 0.99), Apgar score <7 at 5 min (RR 0.63, 95% CI 0.28, 1.41) and respiratory morbidity (RR 0.68, 95% CI 0.37, 1.25) in first- compared to second-trimester estimations. Female infants estimated in first trimester born ≥41 weeks had lower risk of umbilical artery pH <7.05, NICU admissions and respiratory morbidity; however, CIs were wide. Early ultrasound estimation of gestational age may reduce the excess risk of adverse neonatal outcomes and highlight the role of foetal sex and the timing of ultrasound assessment in the clinical evaluation of preterm and post-term pregnancies.

Estimating gestational age using the anthropometric measurements of newborns in North Shewa Zone public hospitals, Oromia, Ethiopia.

The accurate estimation of gestational age is crucial in identifying prematurity and other health problems in newborns and in providing appropriate perinatal care. Although there are numerous methods for measuring gestational age, they are not always applicable. During these situations, it becomes challenging to ascertain whether a baby has been born prematurely or not. Therefore, this study aims to estimate gestational age by utilizing newborn anthropometric parameters. The objective of this study is to estimate the gestational age of newborns in public hospitals located in the North Shewa Zone of the Oromia Region in Ethiopia, by using anthropometric parameters. A cross-sectional study was conducted at a facility from February 2022 to April 2022, using an interview-based questionnaire and anthropometric measurements. The anthropometric parameters that were measured include foot length (FL), mid-upper arm circumference (MUAC), and chest and head circumference (CHC). The study's sample size had a total of 420 participants. The data were cleaned, edited, manually checked for completeness, and entered into Epi-data version 3.1. Subsequently, the data were transferred into SPSS for analysis. The data were analyzed using descriptive analysis, simple linear regression, and multiple linear regressions. Finally, the data were presented using statements and tables. There is a significant and positive correlation between anthropometric parameters, including head circumference (r: 0.483), MUAC (r: 0.481), foot length (r: 0.457), and chest circumference (r: 0.482) with gestational age. All anthropometric parameters demonstrated positive and significant estimates of gestational age. The combination of the four measurements yielded the strongest estimate of gestational age. Gestational age can be calculated by the formula: Gestational age (Weeks) = 9.78 + 0.209*CHC + 0.607*MUAC + 0.727*FL + 0.322*HC. Gestational age can be measured using head circumference, mid-upper arm circumference, foot length, and chest circumference. Utilizing the four anthropometric parameters in combination exhibits greater efficacy in estimating gestational age than using them individually. Therefore, it is recommended to use these alternative approaches when standard methods are not applicable.

The correlation between iron deficiency anemia and hypothyroidism during pregnancy

Abstract Background: Pregnant women are often iron deficient, and this has adverse effects on thyroid metabolism. Impaired maternal thyroid function in pregnancy may cause neurodevelopmental delay in the offspring. Objectives: To investigate if maternal iron status is a determinant of thyroid stimulating hormone (TSH) and/or free T4 (FT4) concentrations during pregnancy. Materials and Methods: A case–control study was conducted in the Department of Obstetrics and Gynecology at Azadi Teaching Hospital, Kirkuk, Iraq over a period of 7 months from February 1st to September 1st, 2020. It included 80 pregnant women with singleton pregnancy in the obstetric outpatient clinic and labor room. Verbal consent was obtained. They were divided into: (1) case group: it included 40 cases of iron deficiency (ID) anemic pregnant women with hemoglobin level of 105 g/L and less in the second and third trimesters. (2) Control group: It included 40 cases of healthy pregnant women. Estimation of gestational age was done depending on the date of last menstrual cycle, and/or early ultrasound scan. Results: In this study, 80 pregnant women in their second and third trimesters participated. Fifty percent were anemic, had elevated serum soluble transferrin receptor and negative body iron stores, 35% had a free T4 less than 7.5 pmol/L, and 47.5% had a TSH more than 4.0 mIU/L. 47.5% who were hypothyroid had negative body iron store. Serum ferritin, soluble transferrin receptor, and body iron stores were highly significant predictors of thyroid status. Conclusions: Poor maternal iron status predicts both higher TSH and lower free T4 concentrations during pregnancy. On the basis of results, we had there may be a correlation between ID anemia of pregnant women and their thyroid status.

Comparative Study between Crown-Rump Length and Fetal Volume in First Trimester for Accurate Estimation of Gestational Age

Comparative Study between Crown-Rump Length and Fetal Volume in First Trimester for Accurate Estimation of Gestational Age