Artificial intelligence (AI) is transforming fetal brain imaging by addressing key challenges in diagnostic accuracy, efficiency, and data integration in prenatal care. This review explores AI's application in enhancing fetal brain imaging through ultrasound (US) and magnetic resonance imaging (MRI), with a particular focus on multimodal integration to leverage their complementary strengths. By critically analyzing state-of-the-art AI methodologies, including deep learning frameworks and attention-based architectures, this study highlights significant advancements alongside persistent challenges. Notable barriers include the scarcity of diverse and high-quality datasets, computational inefficiencies, and ethical concerns surrounding data privacy and security. Special attention is given to multimodal approaches that integrate US and MRI, combining the accessibility and real-time imaging of US with the superior soft tissue contrast of MRI to improve diagnostic precision. Furthermore, this review emphasizes the transformative potential of AI in fostering clinical adoption through innovations such as real-time diagnostic tools and human-AI collaboration frameworks. By providing a comprehensive roadmap for future research and implementation, this study underscores AI's potential to redefine fetal imaging practices, enhance diagnostic accuracy, and ultimately improve perinatal care outcomes.

Background: Failure to accurately estimate gestational age remains an important dilemma for optimal evidence-based antenatal care. Currently, when the last menstrual period (LMP) is unknown, ultrasonography measurement is the best method for estimating gestational age (GA). This study aims to assess the feasibility and accuracy of ultrasonography measurement of the transverse cerebellar diameter (TCD) to deduce fetal GA after 13 weeks of gestation. Methods: A prospective study was conducted on 384 normal singleton pregnancies. Demographic information and biometric measurements, including TCD, were collected using a data sheet. The data were then analyzed using SPSS version 27, DATAtab, and the R program. Results: The study found a strong significant association between GA based on TCD and the LMP, GA based on femur length (FL), GA based on biparietal diameter (BPD), GA based on abdominal circumference (AC), and GA based on the average gestational age (AVG) (r = 0.976, 0.970, 0.966, 0.968, and 0.984, respectively, p < 0.001). Furthermore, there was perfect agreement between GA estimated using TCD and GA based on LMP, with a mean difference of 0.41 weeks and upper and lower limits of agreement of -1.43 to 2.26 weeks. Conclusions: Ultrasonography measurements of the TCD accurately predict gestational age with excellent concordance with GA based on the LMP, FL, AC, and BPD. TCD can be used as a reliable estimator of GA in the second and third trimesters of pregnancy with the benefit of its brain-sparing effect in fetuses of fetal intrauterine growth restriction pregnancies. Combining TCD with FL, BPD, and AC provides the most accurate method of GA prediction.

The third-trimester scan allows not only the assessment of foetal growth but also its presentation and anatomy, and placental, amniotic fluid, and umbilical cord anomalies. Although there is a great disparity when considering its recommendation, most recent studies raise the question for its usefulness considering its impact in a potential reduction of perinatal morbidity and mortality. For this to be a reality in a population-wide setting, a systematic approach should be made considering performing it between 35 + 0 and 36 + 6 weeks', including the assessment of estimated foetal weight, foetal Doppler (umbilical and middle cerebral artery), placenta, amniotic fluid, foetal anatomy, and presentation. In high-risk cases, additional evaluation of the placenta, umbilical cord, or advanced foetal anatomy assessment can be warranted. Furthermore, pre-defined and evidence-based protocols should be followed after anomalies are detected in order to improve maternal and perinatal outcomes.

To examine the relationship of ophthalmic artery (OA) Doppler indices with uterine artery (UtA) Doppler indices, selected maternal hemodynamic parameters and gestational age, and to evaluate the intraobserver reproducibility of OA Doppler indices.

This was a prospective cohort study of women recruited between 11 + 0 and 23 + 6 weeks' gestation using a stratified and random sampling approach to ensure adequate distribution across the gestational-age range. OA pulsatility index (PI), first peak systolic velocity (PSV1), second peak systolic velocity (PSV2) and peak systolic velocity ratio (PSV ratio), calculated as PSV2/PSV1, were measured twice in each eye by the same observer. UtA-PI was also measured twice on each side by the same observer. Maternal hemodynamic assessment was undertaken using an ultrasonic cardiac output monitor (USCOM 1A). Pearson's and Spearman's rank correlation coefficients were used to assess the correlations between variables, and Bland-Altman plots were used to evaluate the intraobserver reproducibility of OA Doppler indices.

Of 194 women invited to participate in the study, 169 were eligible for inclusion, of whom 16 were excluded following an obstetric ultrasound scan and a further three owing to inadequate or incomplete OA or UtA Doppler assessment, leaving 150 women in the final analysis. Log UtA-PI had a weak correlation with both OA-PI (r = -0.19 (95% CI, -0.34 to -0.03), P = 0.021) and OA-PSV ratio (r = 0.31 (95% CI, 0.15-0.45), P < 0.001). The correlation between gestational age and OA-PI was non-significant (r = 0.14 (95% CI, -0.03 to 0.29), P = 0.097), and that between gestational age and OA-PSV ratio was weak (r = -0.23 (95% CI, -0.38 to -0.07), P = 0.004), as opposed to the strong correlation between gestational age and UtA-PI (r = -0.68 (95% CI, -0.76 to -0.58), P < 0.001). No strong correlations were observed between OA-PI or OA-PSV ratio and maternal hemodynamic indices. The correlations were unaltered by adjustment for maternal age and body mass index. The intraobserver reproducibility of OA-PI and OA-PSV ratio in the same eye was high. The correlation between the right and left eyes was moderate for OA-PI (r = 0.63 (95% CI, 0.53-0.72), P < 0.001) and strong for OA-PSV ratio (r = 0.81 (95% CI, 0.75-0.86), P < 0.001).

OA-PI and OA-PSV ratio had a weak or no correlation with UtA-PI and maternal hemodynamic parameters, meaning that they can be used as independent predictors for pre-eclampsia. Gestational age had no clinically relevant effect on OA-PI and OA-PSV ratio, suggesting that these indices could be measured without adjustment at any time between 11 and 23 weeks' gestation. OA Doppler indices had high intraobserver reproducibility and were strongly correlated between the right and left eyes. © 2025 The Author(s). Ultrasound in Obstetrics & Gynecology published by John Wiley & Sons Ltd on behalf of International Society of Ultrasound in Obstetrics and Gynecology.

Estimating a reliable gestational age (GA) is essential in providing appropriate care during pregnancy. With advancements in data science, there are several publications on the use of artificial intelligence (AI) models to estimate GA using ultrasound (US) images. The aim of this meta-analysis is to assess the accuracy of AI models in assessing GA against US as the gold standard.

A literature search was performed in PubMed, CINAHL, Wiley Cochrane Library, Scopus, and Web of Science databases. Studies that reported use of AI models for GA estimation with US as the reference standard were included. Risk of bias assessment was performed using Quality Assessment for Diagnostic Accuracy Studies-2 (QUADAS-2) tool. Mean error in GA was estimated using STATA version-17 and subgroup analysis on trimester of GA assessment, AI models, study design, and external validation was performed.

Out of the 1,039 studies screened, 17 were included in the review, and of these 10 studies were included in the meta-analysis. Five (29%) studies were from high-income countries (HICs), four (24%) from upper-middle-income countries (UMICs), one (6%) from low-and middle-income countries (LMIC), and the remaining seven studies (41%) used data across different income regions. The pooled mean error in GA estimation based on 2D images (n = 6) and blind sweep videos (n = 4) was 4.32 days (95% CI: 2.82, 5.83; l 2: 97.95%) and 2.55 days (95% CI: -0.13, 5.23; l 2: 100%), respectively. On subgroup analysis based on 2D images, the mean error in GA estimation in the first trimester was 7.00 days (95% CI: 6.08, 7.92), 2.35 days (95% CI: 1.03, 3.67) in the second, and 4.30 days (95% CI: 4.10, 4.50) in the third trimester. In studies using deep learning for 2D images, those employing CNN reported a mean error of 5.11 days (95% CI: 1.85, 8.37) in gestational age estimation, while one using DNN indicated a mean error of 5.39 days (95% CI: 5.10, 5.68). Most studies exhibited an unclear or low risk of bias in various domains, including patient selection, index test, reference standard, flow and timings and applicability domain.

Preliminary experience with AI models shows good accuracy in estimating GA. This holds tremendous potential for pregnancy dating, especially in resource-poor settings where trained interpreters may be limited.

PROSPERO, identifier (CRD42022319966).

Optimal maternal nutrition, including adequate intake and status of essential micronutrients, is important for the health of women and developing infants. Currently, the World Health Organization (WHO) Antenatal care recommendations for a positive pregnancy experience recommend daily iron and folic acid (IFA) supplementation as the standard of care. The use of multiple micronutrient supplements (MMS) is recommended in the context of rigorous research as more evidence was needed regarding the impact of switching from IFA supplements to MMS, including evaluation of critical clinical maternal and perinatal outcomes, acceptability, feasibility, sustainability, equity and cost-effectiveness. WHO convened a technical consultation of key stakeholders to discuss research priorities with the objective of providing guidance and clarity to donors, implementers and researchers about this recommendation. The overarching principles of the research agenda include the use of clinical indicators and impact measures that are applicable across studies and settings and the inclusion of outcomes that are important to women. Future studies should consider using standardized protocols based on current best practices to measure critical outcomes such as gestational age (GA) and birthweight (BW) in studies. As GA and BW are influenced by multiple factors, more research is needed to understand the biological impact pathways, and how initiation and considerations for timing of MMS influence these outcomes. A set of core clinical indicators was agreed upon during the technical consultation. For implementation research, the Evidence-to-Decision framework was used as a resource for discussing components of implementation research. The implementation research questions, key indicators and performance measures will depend on country-specific context and bottlenecks that require further research and improved solutions to enable the successful implementation of iron-containing supplements.

Accurate assessment of gestational age (GA) is important at both individual and population levels. The most accurate way to estimate GA in women who book late in pregnancy is unknown. The aim of this study was to externally validate the accuracy of equations for GA estimation in late pregnancy and to identify the best equation for estimating GA in women who do not receive an ultrasound scan until the second or third trimester.

This was a prospective, observational cross-sectional study.

57 prenatal care centres, France.

Women with a singleton pregnancy and a previous 11-14-week dating scan that gave the observed GA were recruited over an 8-week period. They underwent a standardised ultrasound examination at one time point during the pregnancy (15-43 weeks), measuring 12 foetal biometric parameters that have previously been identified as useful for GA estimation.

A total of 189 equations that estimate GA based on foetal biometry were examined and compared with GA estimation based on foetal CRL. Comparisons between the observed GA and the estimated GA were made using R2, calibration slope and intercept. RMSE, mean difference and 95% range of error were also calculated.

A total of 2741 pregnant women were examined. After exclusions, 2339 participants were included. In the 20 best performing equations, the intercept ranged from -0.22 to 0.30, the calibration slope from 0.96 to 1.03 and the RSME from 0.67 to 0.87. Overall, multiparameter models outperformed single-parameter models. Both the 95% range of error and mean difference increased with gestation. Commonly used models based on measurement of the head circumference alone were not amongst the best performing models and were associated with higher 95% error and mean difference.

We provide strong evidence that GA-specific equations based on multiparameter models should be used to estimate GA in late pregnancy. However, as all methods of GA assessment in late pregnancy are associated with large prediction intervals, efforts to improve access to early antenatal ultrasound must remain a priority.

The proposal for this study and the corresponding methodological review was registered on PROSPERO international register of systematic reviews (registration number: CRD4201913776).

The WHO's recommendations on antenatal care underscore the need for ultrasound assessment during pregnancy. Given that maternal and perinatal mortality remains unacceptably high in underserved regions, these guidelines are imperative for achieving better outcomes. In recent years, portable ultrasound devices have become increasingly popular in resource-constrained environments due to their cost-effectiveness, useability, and adoptability in resource-constrained settings. This desk review presents the capabilities and costs of currently available portable ultrasound devices, and is meant to serve as a resource for clinicians and researchers in the imaging community.

A list of ideal technical features for portable ultrasound devices was developed in consultation with subject matter experts (SMEs). Features included image acquisition modes, cost, portability, compatibility, connectivity, data storage and security, and regulatory certification status. Information on each of the devices was collected from publicly available information, input from SMEs and/or discussions with company representatives.

14 devices were identified and included in this review. The output is meant to provide objective information on ideal technical features for available ultrasound systems to researchers and clinicians working in obstetric ultrasound in low-resource settings. No product endorsements are provided.

This desk review provides an overview of the landscape of low-cost portable ultrasound probes for use in obstetrics in resource-constrained environments, and provides a description of key capabilities and costs for each. Methods could be applied to mapping the landscape of portable ultrasound devices for other clinical applications, or may be extended to reviewing other types of healthcare technologies. Further studies are recommended to evaluate portable ultrasound devices for usability and durability in global field settings.

Complications of prematurity are the leading cause of under-5 mortality globally and 80% of newborn deaths are of low birth weight (LBW) babies. Early identification of LBW and preterm infants is crucial to initiate timely interventions.

To evaluate the feasibility and diagnostic accuracy of alternative neonatal anthropometric measurements in identifying LBW and preterm infants in Africa.

In this systematic review and meta-analysis, we evaluated the diagnostic performance of infant foot length, mid-upper arm circumference (MUAC), head and chest circumferences against birth weight and gestational age. Pooled correlation between the index and the reference methods was estimated. Multiple anthropometric thresholds were considered in estimating the pooled sensitivity, specificity and area under receiver operating characteristic curve (AUC).

21 studies from 8 African countries met the inclusion criteria. Correlation coefficients with birth weight were 0.79 (95% CI 0.70 to 0.85) for chest circumference, 0.71 (95% CI 0.62 to 0.78) for MUAC and 0.66 (95% CI 0.59 to 0.73) for foot length. Foot length measured by rigid ruler showed a higher correlation than tape measurement. Chest circumference with 28.8 cm cut-off detects LBW babies with AUC value of 0.92 (95% CI 0.71 to 0.97). Foot length identified preterm infants, with 82% sensitivity, 89% specificity and AUC of 0.91 (95% CI 0.69 to 0.98) at a 7.2 cm optimal cut-off point. MUAC had an AUC of 0.83 (95% CI 0.47 to 0.95) for preterm detection. In identifying LBW babies, foot length and MUAC have AUC values of 0.89 (95% CI 0.70 to 0.96) and 0.91 (95% CI 0.73 to 0.97) at 7.3 cm and 9.8 cm optimal cut-off points, respectively. Foot length and MUAC are relatively simple and minimise the risk of exposing infants to cold.

Newborn foot length, MUAC, head and chest circumferences have comparable diagnostic accuracy in identifying LBW and preterm babies. Using foot length and MUAC in low-resource settings are the most feasible proxy measures for screening where weighing scales are not available.

CRD42023454497.

The integration of artificial intelligence (AI) in obstetrics and gynecology (OB/GYN) is revolutionizing the landscape of women's healthcare. This review article explores the transformative impact of AI technologies on the diagnosis, treatment, and management of obstetric and gynecological conditions. We examine key advancements in AI-driven imaging techniques, predictive analytics, and personalized medicine, highlighting their roles in enhancing prenatal care, improving maternal and fetal outcomes, and optimizing gynecological interventions. The article also addresses the challenges and ethical considerations associated with the implementation of AI in clinical practice. This paper highlights the potential of AI to greatly improve the standard of care in OB/GYN, ultimately leading to better health outcomes for women, by offering a thorough overview of present AI uses and future prospects.

Preterm birth (PTB) is the leading cause of morbidity and mortality in children globally, yet its prevalence has been difficult to accurately estimate due to unreliable methods of gestational age dating, heterogeneity in counting, and insufficient data. The estimated global PTB rate in 2020 was 9.9% (95% confidence interval: 9.1, 11.2), which reflects no significant change from 2010, and 81% of prematurity-related deaths occurred in Africa and Asia. PTB prevalence in the United States in 2021 was 10.5%, yet with concerning racial disparities. Few effective solutions for prematurity prevention have been identified, highlighting the importance of further research.

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.

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.

Preterm birth (PTB) is a complex syndrome traditionally defined by a single parameter, namely, gestational age at birth (ie, ˂37 weeks). This approach has limitations for clinical usefulness and may explain the lack of progress in identifying cause-specific effective interventions. The authors offer a framework for a functional taxonomy of PTB based on (1) conceptual principles established a priori; (2) known etiologic factors; (3) specific, prospectively identified obstetric and neonatal clinical phenotypes; and (4) postnatal follow-up of growth and development up to 2 years of age. This taxonomy includes maternal, placental, and fetal conditions routinely recorded in data collection systems.

Household air pollution might lead to fetal growth restriction during pregnancy. We aimed to investigate whether a liquefied petroleum gas (LPG) intervention to reduce personal exposures to household air pollution during pregnancy would alter fetal growth.

The Household Air Pollution Intervention Network (HAPIN) trial was an open-label randomised controlled trial conducted in ten resource-limited settings across Guatemala, India, Peru, and Rwanda. Pregnant women aged 18-34 years (9-19 weeks of gestation) were randomly assigned in a 1:1 ratio to receive an LPG stove, continuous fuel delivery, and behavioural messaging or to continue usual cooking with biomass for 18 months. We conducted ultrasound assessments at baseline, 24-28 weeks of gestation (the first pregnancy visit), and 32-36 weeks of gestation (the second pregnancy visit), to measure fetal size; we monitored 24 h personal exposures to household air pollutants during these visits; and we weighed children at birth. We conducted intention-to-treat analyses to estimate differences in fetal size between the intervention and control group, and exposure-response analyses to identify associations between household air pollutants and fetal size. This trial is registered with ClinicalTrials.gov (NCT02944682).

Between May 7, 2018, and Feb 29, 2020, we randomly assigned 3200 pregnant women (1593 to the intervention group and 1607 to the control group). The mean gestational age was 14·5 (SD 3·0) weeks and mean maternal age was 25·6 (4·5) years. We obtained ultrasound assessments in 3147 (98·3%) women at baseline, 3052 (95·4%) women at the first pregnancy visit, and 2962 (92·6%) at the second pregnancy visit, through to Aug 25, 2020. Intervention adherence was high (the median proportion of days with biomass stove use was 0·0%, IQR 0·0-1·6) and pregnant women in the intervention group had lower mean exposures to particulate matter with a diameter less than 2·5 μm (PM2·5; 35·0 [SD 37·2] μg/m3vs 103·3 [97·9] μg/m3) than did women in the control group. We did not find differences in averaged post-randomisation Z scores for head circumference (0·30 vs 0·39; p=0·04), abdominal circumference (0·38 vs 0·39; p=0·99), femur length (0·44 vs 0·45; p=0·73), and estimated fetal weight or birthweight (-0·13 vs -0·12; p=0·70) between the intervention and control groups. Personal exposures to household air pollutants were not associated with fetal size.

Although an LPG cooking intervention successfully reduced personal exposure to air pollution during pregnancy, it did not affect fetal size. Our findings do not support the use of unvented liquefied petroleum gas stoves as a strategy to increase fetal growth in settings were biomass fuels are used predominantly for cooking.

US National Institutes of Health and Bill & Melinda Gates Foundation.

For the Kinyarwanda, Spanish and Tamil translations of the abstract see Supplementary Materials section.

The inaccuracy of late pregnancy dating is often discussed, and the impact on diagnosis of fetal growth restriction is a concern. However, the magnitude and direction of this effect has not previously been demonstrated. In this study, we aimed to investigate the effect of late pregnancy dating by head circumference on the detection of late onset growth restriction, compared to first trimester crown-rump length dating.

This was a cohort study of 14 013 pregnancies receiving obstetric care at a tertiary center over a three-year period. Universal scans were performed at 12 weeks, including crown-rump length; at 20 weeks including fetal biometry; and at 36 weeks, where biometry, umbilical artery doppler and cerebroplacental ratio were used to determine the incidence of fetal growth restriction according to the Delphi consensus. For the entire cohort, the gestational age was first calculated using T1 dating; and was then recalculated using head circumference at 20 weeks (T2 dating); and at 36 weeks (T3 dating). The incidence of fetal growth restriction following T2 and T3 dating was compared to T1 dating using four-by-four sensitivity tables.

When the cohort was redated from T1 to T2, the median gestation at delivery changed from 40 + 0 to 40 + 2 weeks (p < 0.001). When the cohort was redated from T1 to T3, the median gestation at delivery changed from 40 + 0 to 40 + 3 weeks (p < 0.001). T2 dating resulted in fetal growth restriction sensitivity of 80.2% with positive predictive value of 78.8% compared to T1 dating. T3 dating resulted in sensitivity of 8.6% and positive predictive value of 27.7%, respectively. The sensitivity of abnormal CPR remained high despite T2 and T3 redating; 98.0% and 89.4%, respectively.

Although dating at 11-14 weeks is recommended, late pregnancy dating is sometimes inevitable, and this can prolong the estimated due date by an average of two to three days. One in five pregnancies which would be classified as growth restricted if the pregnancy was dated in the first trimester, will be reclassified as nongrowth restricted following dating at 20 weeks, whereas nine out of 10 pregnancies will be reclassified as non-growth restricted with 36-week dating.

Preterm birth complications are the leading cause of newborn and under-5 mortality. Over 85% of all preterm births occur in the late preterm period, i.e. between 34 and < 37 weeks of gestation. Antenatal corticosteroids (ACS) prevent mortality and respiratory morbidity when administered to women at high risk of an early preterm birth, i.e. < 34 weeks' gestation. However, the benefits and risks of ACS in the late preterm period are less clear; both guidelines and practices vary between settings. Emerging evidence suggests that the benefits of ACS may be achievable at lower doses than presently used. This trial aims to determine the efficacy and safety of two ACS regimens compared to placebo, when given to women with a high probability of late preterm birth, in hospitals in low-resource countries.

WHO ACTION III trial is a parallel-group, three-arm, individually randomized, double-blind, placebo-controlled trial of two ACS regimens: dexamethasone phosphate 4 × 6 mg q12h or betamethasone phosphate 4 × 2 mg q 12 h. The trial is being conducted across seven sites in five countries-Bangladesh, India, Kenya, Nigeria, and Pakistan. Eligible women are those with a gestational age between 34 weeks 0 days and 36 weeks 5 days, who have a high probability of preterm birth between 12 h and 7 days (up to 36 weeks 6 days gestation). The primary outcome is a composite of stillbirth or neonatal death within 72 h of birth or use of newborn respiratory support within 72 h of birth or prior to discharge from hospital, whichever is earlier. Secondary outcomes include safety and health utilization measures for both women and newborns. The sample size is 13,500 women.

This trial will evaluate the benefits and possible harms of ACS when used in women likely to have a late preterm birth. It will also evaluate a lower-dose ACS regimen based on literature from pharmacokinetic studies. The results of this trial will provide robust critical evidence on the safe and appropriate use of ACS in the late preterm period internationally.

ISRCTN11434567 . Registered on 7 June 2021.

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.

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 Sudan during 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).

Approaches to address measurement error frequently rely on validation data to estimate measurement error parameters (e.g., sensitivity and specificity). Acquisition of validation data can be costly, thus secondary use of existing data for validation is attractive. To use these external validation data, however, we may need to address systematic differences between these data and the main study sample. Here, we derive estimators of the risk and the risk difference that leverage external validation data to account for outcome misclassification. If misclassification is differential with respect to covariates that themselves are differentially distributed in the validation and study samples, the misclassification parameters are not immediately transportable. We introduce two ways to account for such covariates: (1) standardize by these covariates or (2) iteratively model the outcome. If conditioning on a covariate for transporting the misclassification parameters induces bias of the causal effect (e.g., M-bias), the former but not the latter approach is biased. We provide proof of identification, describe estimation using parametric models, and assess performance in simulations. We also illustrate implementation to estimate the risk of preterm birth and the effect of maternal HIV infection on preterm birth. Measurement error should not be ignored and it can be addressed using external validation data via transportability methods.

Preterm and term small for gestational age (SGA) babies are at high risk of experiencing malnutrition and impaired neurodevelopment. Standalone interventions have modest and sometimes inconsistent effects on growth and neurodevelopment in these babies. For greater impact, intervention may be needed in multiple domains-health, nutrition, and psychosocial care and support. Therefore, the combined effects of an integrated intervention package for preterm and term SGA on growth and neurodevelopment are worth investigating.

An individually randomized controlled trial is being conducted in urban and peri-urban low to middle-socioeconomic neighborhoods in South Delhi, India. Infants are randomized (1:1) into two strata of 1300 preterm and 1300 term SGA infants each to receive the intervention package or routine care. Infants will be followed until 12 months of age. Outcome data will be collected by an independent outcome ascertainment team at infant ages 1, 3, 6, 9, and 12 months and at 2, 6, and 12 months after delivery for mothers.

The findings of this study will indicate whether providing an intervention that addresses factors known to limit growth and neurodevelopment can offer substantial benefits to preterm or term SGA infants. The results from this study will increase our understanding of growth and development and guide the design of public health programs in low- and middle-income settings for vulnerable infants.

The trial has been registered prospectively in Clinical Trial Registry - India # CTRI/2021/11/037881, Registered on 08 November 2021.

Fetal biometry and amniotic fluid volume assessments are two essential yet repetitive tasks in fetal ultrasound screening scans, aiding in the detection of potentially life-threatening conditions. However, these assessment methods can occasionally yield unreliable results. Advances in deep learning have opened up new avenues for automated measurements in fetal ultrasound, demonstrating human-level performance in various fetal ultrasound tasks. Nevertheless, the majority of these studies are retrospective in silico studies, with a limited number including African patients in their datasets. In this study we developed and prospectively assessed the performance of deep learning models for end-to-end automation of fetal biometry and amniotic fluid volume measurements. These models were trained using a newly constructed database of 172,293 de-identified Moroccan fetal ultrasound images, supplemented with publicly available datasets. the models were then tested on prospectively acquired video clips from 172 pregnant people forming a consecutive series gathered at four healthcare centers in Morocco. Our results demonstrate that the 95% limits of agreement between the models and practitioners for the studied measurements were narrower than the reported intra- and inter-observer variability among expert human sonographers for all the parameters under study. This means that these models could be deployed in clinical conditions, to alleviate time-consuming, repetitive tasks, and make fetal ultrasound more accessible in limited-resource environments.

Many women in low and middle-income countries enter pregnancy with low nutritional reserves with increased risk of fetal growth restriction and poor birth outcomes, including small-for-gestational-age (SGA) and preterm birth. Balanced energy-protein (BEP) supplements have shown reductions in risk of stillbirth and SGA, yet variations in intervention format and composition and limited evidence on the impact of BEP during lactation on growth outcomes warrant further study. This paper describes the protocol of the Maternal Infant Nutrition Trial (MINT) Study, which aims to evaluate the impact of a fortified BEP supplement during pregnancy and lactation on birth outcomes and infant growth in rural Nepal.

MINT is a 2×2 factorial, household randomised, unblinded, efficacy trial conducted in a subarea of Sarlahi District, Nepal. The study area covers six rural municipalities with about 27 000 households and a population of approximately 100 000. Married women (15-30 years) who become pregnant are eligible for participation in the trial and are randomly assigned at enrolment to supplementation with fortified BEP or not and at birth to fortified BEP supplementation or not until 6 months post partum. The primary pregnancy outcome is incidence of SGA, using the INTERGROWTH-21st standard, among live born infants with birth weight measured within 72 hours of delivery. The primary infant growth outcome is mean length-for-age z-score at 6 months using the WHO international growth reference.

The study was approved by the Institutional Review Board (IRB) at Johns Hopkins Bloomberg School of Public Health, Baltimore, USA (IRB00009714), the Committee on Human Research IRB at The George Washington University, Washington, DC, USA (081739), and the Ethical Review Board of the Nepal Health Research Council, Kathmandu, Nepal (174/2018).

NCT03668977.

Malaria and sexually transmitted and reproductive tract infections (STIs/RTIs) are highly prevalent in sub-Saharan Africa and associated with poor pregnancy outcomes. We investigated the individual and combined effects of malaria and curable STIs/RTIs on fetal growth in Kenya, Tanzania, and Malawi.

This study was nested within a randomized trial comparing monthly intermittent preventive treatment for malaria in pregnancy with sulfadoxine-pyrimethamine vs dihydroartemisinin-piperaquine, alone or combined with azithromycin. Fetal weight gain was assessed by serial prenatal ultrasound. Malaria was assessed monthly, and Treponema pallidum, Neisseria gonorrhoeae, Trichomonas vaginalis, Chlamydia trachomatis, and bacterial vaginosis at enrollment and in the third trimester. The effect of malaria and STIs/RTIs on fetal weight/birthweight Z-scores was evaluated using mixed-effects linear regression.

In total, 1435 pregnant women had fetal/birth weight assessed 3950 times. Compared to women without malaria or STIs/RTIs (n = 399), malaria-only (n = 267), STIs/RTIs only (n = 410) or both (n = 353) were associated with reduced fetal growth (adjusted mean difference in fetal/birth weight Z-score [95% confidence interval]: malaria = -0.18 [-0.31,-0.04], P = 0.01; STIs/RTIs = -0.14 [-0.26,-0.03], P = 0.01; both = -0.20 [-0.33,-0.07], P = 0.003). Paucigravidae experienced the greatest impact.

Malaria and STIs/RTIs are associated with poor fetal growth especially among paucigravidae women with dual infections. Integrated antenatal interventions are needed to reduce the burden of both malaria and STIs/RTIs.

Blood proteins are frequently measured in serum or plasma, because they provide a wealth of information. Differences in the ex vivo processing of serum and plasma raise concerns that proteomic health and disease signatures derived from serum or plasma differ in content and quality. However, little is known about their respective power to predict feto-maternal health outcomes. Predictive power is a sentinel characteristic to determine the clinical use of biosignatures.

This study aimed to compare the power of serum and plasma proteomic signatures to predict a physiological pregnancy outcome.

Paired serum and plasma samples from 73 women were obtained from biorepositories of a multinational prospective cohort study on pregnancy outcomes. Gestational age at the time of sampling was the predicted outcome, because the proteomic signatures have been validated for such a prediction. Multivariate and cross-validated models were independently derived for serum and plasma proteins.

A total of 1116 proteins were measured in 88 paired samples from 73 women with a highly multiplexed platform using proximity extension technology (Olink Proteomics Inc, Watertown, MA). The plasma proteomic signature showed a higher predictive power (R=0.64; confidence interval, 0.42-0.79; P=3.5×10^-6) than the serum signature (R=0.45; confidence interval, 0.18-0.66; P=2.2×10^-3). The serum signature was validated in plasma with a similar predictive power (R=0.58; confidence interval, 0.34-0.75; P=4.8×10^-5), whereas the plasma signature was validated in serum with reduced predictive power (R=0.53; confidence interval, 0.27-0.72; P=2.6×10^-4). Signature proteins largely overlapped in the serum and plasma, but the strength of association with gestational age was weaker for serum proteins.

Findings suggest that serum proteomics are less informative than plasma proteomics. They are compatible with the view that the partial ex-vivo degradation and modification of serum proteins during sample processing are an underlying reason. The rationale for collecting and analyzing serum and plasma samples should be carefully considered when deriving proteomic biosignatures to ascertain that specimens of the highest scientific and clinical yield are processed. Findings suggest that plasma is the preferred matrix.

Small newborns are vulnerable to mortality and lifelong loss of human capital. Measures of vulnerability previously focused on liveborn low-birthweight (LBW) babies, yet LBW reduction targets are off-track. There are two pathways to LBW, preterm birth and fetal growth restriction (FGR), with the FGR pathway resulting in the baby being small for gestational age (SGA). Data on LBW babies are available from 158 (81%) of 194 WHO member states and the occupied Palestinian territory, including east Jerusalem, with 113 (58%) having national administrative data, whereas data on preterm births are available from 103 (53%) of 195 countries and areas, with only 64 (33%) providing national administrative data. National administrative data on SGA are available for only eight countries. Global estimates for 2020 suggest 13·4 million livebirths were preterm, with rates over the past decade remaining static, and 23·4 million were SGA. In this Series paper, we estimated prevalence in 2020 for three mutually exclusive types of small vulnerable newborns (SVNs; preterm non-SGA, term SGA, and preterm SGA) using individual-level data (2010-20) from 23 national datasets (∼110 million livebirths) and 31 studies in 18 countries (∼0·4 million livebirths). We found 11·9 million (50% credible interval [Crl] 9·1-12·2 million; 8·8%, 50% Crl 6·8-9·0%) of global livebirths were preterm non-SGA, 21·9 million (50% Crl 20·1-25·5 million; 16·3%, 14·9-18·9%) were term SGA, and 1·5 million (50% Crl 1·2-4·2 million; 1·1%, 50% Crl 0·9-3·1%) were preterm SGA. Over half (55·3%) of the 2·4 million neonatal deaths worldwide in 2020 were attributed to one of the SVN types, of which 73·4% were preterm and the remainder were term SGA. Analyses from 12 of the 23 countries with national data (0·6 million stillbirths at ≥22 weeks gestation) showed around 74% of stillbirths were preterm, including 16·0% preterm SGA and approximately one-fifth of term stillbirths were SGA. There are an estimated 1·9 million stillbirths per year associated with similar vulnerability pathways; hence integrating stillbirths to burden assessments and relevant indicators is crucial. Data can be improved by counting, weighing, and assessing the gestational age of every newborn, whether liveborn or stillborn, and classifying small newborns by the three vulnerability types. The use of these more specific types could accelerate prevention and help target care for the most vulnerable babies.

Prenatal ultrasonography is the most crucial imaging modality during pregnancy. However, problems such as high fetal mobility, excessive maternal abdominal wall thickness, and inter-observer variability limit the development of traditional ultrasound in clinical applications. The combination of artificial intelligence (AI) and obstetric ultrasound may help optimize fetal ultrasound examination by shortening the examination time, reducing the physician's workload, and improving diagnostic accuracy. AI has been successfully applied to automatic fetal ultrasound standard plane detection, biometric parameter measurement, and disease diagnosis to facilitate conventional imaging approaches. In this review, we attempt to thoroughly review the applications and advantages of AI in prenatal fetal ultrasound and discuss the challenges and promises of this new field.

Assessing fetal development is essential to the provision of healthcare for both mothers and fetuses. In low- and middle-income countries, conditions that increase the risk of fetal growth restriction (FGR) are often more prevalent. In these regions, barriers to accessing healthcare and social services exacerbate fetal maternal health problems. One of these barriers is the lack of affordable diagnostic technologies. To address this issue, this work introduces an end-to-end algorithm applied to a low-cost, hand-held Doppler ultrasound device for estimating gestational age (GA), and by inference, FGR. The Doppler ultrasound signals used in this study were collected from 226 pregnancies (45 low birth weight at delivery) between 5 and 9 months GA by lay midwives in highland Guatemala. We designed a hierarchical deep sequence learning model with an attention mechanism to learn the normative dynamics of fetal cardiac activity in different stages of development. This resulted in a state-of-the-art GA estimation performance, with an average error of 0.79 months. This is close to the theoretical minimum for the given quantization level of one month. The model was then tested on Doppler recordings of the fetuses with low birth weight and the estimated GA was shown to be lower than the GA calculated from last menstruation. Thus, this could be interpreted as a potential sign of developmental retardation (or FGR) associated with low birth weight, and referral and intervention may be necessary.

To compare the performance of mid upper arm circumference (MUAC) and body mass index (BMI) for prediction of small for gestational age (SGA) in Zambia.

This is a secondary analysis of an ongoing clinical cohort that included women with a single gestation and MUAC measured before 24 weeks of pregnancy. We assessed relationships between maternal MUAC and birth weight centile using regression. The performance of MUAC and BMI to predict SGA was compared using receiver operating characteristic curves and the effect of maternal HIV was investigated in sub-group analyses.

Of 1117 participants, 847 (75%) were HIV-negative (HIV-) and 270 (24%) were HIV-positive (HIV+). Seventy-four (7%) delivered severe SGA infants (<3rd centile), of whom 56 (76%) were HIV- and 18 (24%) were HIV+ (odds ratio [OR] 1.01, 95% confidence interval [CI] 0.58-1.75). MUAC was associated with higher birth weight centile (+1.2 centile points, 95% CI 0.7-1.6; P < 0.001); this relationship was stronger among HIV+ women (+1.7 centile points, 95% CI 0.8-2.6; P < 0.001) than HIV- women (+0.9 centile points, 95% CI 0.4-1.4; P = 0.001). The discriminatory power was similar, albeit poor (area under the curve [AUC] < 0.7), between MUAC and BMI for the prediction of SGA. In stratified analysis, MUAC and BMI showed excellent discrimination predicting severe SGA among HIV+ (AUC 0.83 and 0.81, respectively) but not among HIV- women (AUC 0.64 and 0.63, respectively).

Maternal HIV infection increased the discrimination of both early pregnancy MUAC and BMI for prediction of severe SGA in Zambia.

ClinicalTrials.gov (NCT02738892).

Accurate estimation of gestational age is an essential component of good obstetric care and informs clinical decision-making throughout pregnancy. As the date of the last menstrual period is often unknown or uncertain, ultrasound measurement of fetal size is currently the best method for estimating gestational age. The calculation assumes an average fetal size at each gestational age. The method is accurate in the first trimester, but less so in the second and third trimesters as growth deviates from the average and variation in fetal size increases. Consequently, fetal ultrasound late in pregnancy has a wide margin of error of at least ±2 weeks' gestation. Here, we utilise state-of-the-art machine learning methods to estimate gestational age using only image analysis of standard ultrasound planes, without any measurement information. The machine learning model is based on ultrasound images from two independent datasets: one for training and internal validation, and another for external validation. During validation, the model was blinded to the ground truth of gestational age (based on a reliable last menstrual period date and confirmatory first-trimester fetal crown rump length). We show that this approach compensates for increases in size variation and is even accurate in cases of intrauterine growth restriction. Our best machine-learning based model estimates gestational age with a mean absolute error of 3.0 (95% CI, 2.9-3.2) and 4.3 (95% CI, 4.1-4.5) days in the second and third trimesters, respectively, which outperforms current ultrasound-based clinical biometry at these gestational ages. Our method for dating the pregnancy in the second and third trimesters is, therefore, more accurate than published methods.

To illustrate the difference between exposure effects and population attributable effects.

We examined the effect of mid-pregnancy short cervical length (<25 mm) on preterm birth using data from a prospective cohort of pregnant women in Lusaka, Zambia. Preterm birth was live birth or stillbirth before 37 weeks of pregnancy. For estimation, we used multivariable regression and parametric g-computation.

Among 1409 women included in the analysis, short cervix was rare (2.4%); 13.6% of births were preterm. Exposure effect estimates were large (marginal risk ratio 2.86, 95% confidence interval [CI] 1.80-4.54), indicating that the preterm birth risk was substantially higher among women with a short cervix compared with women without a short cervix. However, the population attributable effect estimates were close to the null (risk ratio 1.06, 95% CI 1.02-1.10), indicating that an intervention to counteract the impact of short cervix on preterm birth would have minimal effect on the population risk of preterm birth.

Although authors often refer to "the" effect, there are actually different types of effects, as we have illustrated here. In planning research, it is important to consider which effect to estimate to ensure that the estimate aligns with the research objective.

Maternal and neonatal health is one of the main global health challenges. Every day, approximately 800 women and 7,000 newborns die due to complications during pregnancy, delivery, and neonatal period. The leading causes of maternal death in sub-Saharan Africa are obstetric hemorrhage (28.8%), hypertensive disorders in pregnancy (22.1%), non-obstetric complications (18.8%), and pregnancy-related infections (11.5%). Diagnostic ultrasound examinations can be used in a variety of specific circumstances during pregnancy. Because adverse outcomes may also arise in low-risk pregnancies, it is assumed that routine ultrasound in all pregnancies will enable earlier detection and improved management of pregnancy complications. The World Health Organization (WHO) estimated in 1997 that 50% of developing countries had no access to ultrasound imaging, and available equipment was outdated or broken. Unfortunately, besides all the exceptional benefits of ultrasound in obstetrics, its inappropriate use and abuse are reported. Using ultrasound to view, take a picture, or determine the sex of a fetus without a medical indication can be considered ethically unjustifiable. Ultrasound assessment when indicated should be every woman's right in the new era. However, it is still only a privilege in some parts of the world. Investment in both equipment and human resources has been clearly shown to be cost-effective and should be an obligatory step in the improvement of health care. Well-developed health systems should guide developing countries, creating principles for the organization of the health system with an accent on the correct, legal, and ethical use of diagnostic ultrasound in pregnancy to avoid its misuse. The aim of the article is to present the importance of correct and appropriate use of ultrasound in obstetrics and gynecology with reference to developing countries.