Expected-value bias in routine third-trimester growth scans

Quantitative Approach to Quality Review of Prenatal Ultrasound Examinations: Fetal Biometry

Background/Objectives: To evaluate the quality of an ultrasound practice, both large-scale and focused audits are recommended by professional organizations, but such audits can be time-consuming, inefficient, and expensive. Our objective was to develop a time-efficient, quantitative, objective, large-scale method to evaluate fetal biometry measurements for an entire practice, combined with a process for focused image review for personnel whose measurements are outliers. Methods: Ultrasound exam data for a full year are exported from commercial ultrasound reporting software to a statistical package. Fetal biometry measurements are converted to z-scores to standardize across gestational ages. For a large-scale audit, sonographer mean z-scores are compared using analysis of variance (ANOVA) with Scheffe multiple comparisons test. A focused image review is performed on a random sample of exams for sonographers whose mean z-scores differ significantly from the practice mean. A similar large-scale audit is performed, comparing physician mean z-scores. Results: Using fetal abdominal circumference measurements as an example, significant differences between sonographer mean z-scores are readily identified by the ANOVA and Scheffe test. A method is described for the blinded image audit of sonographers with outlier mean z-scores. Examples are also given for the identification and interpretation of several types of systematic errors that are unlikely to be detectable by image review, including z-scores with large or small standard deviations and physicians with outlier mean z-scores. Conclusions: The large-scale quantitative analysis provides an overview of the biometry measurements of all the sonographers and physicians in a practice, so that image audits can be focused on those whose measurements are outliers. The analysis takes little time to perform after initial development and avoids the time, complexity, and expense of auditing providers whose measurements fall within the expected range. We encourage commercial software developers to include tools in their ultrasound reporting software to facilitate such quantitative reviews.

The Holy Grail of obstetric ultrasound: can artificial intelligence detect hard-to-identify fetal cardiac anomalies?

Linked article: This Editorial comments on articles by Day et al. and Taksøe‐Vester et al.

[A pragmatic comparison of fetal biometry curves]

INTRODUCTION

The fetal biometrics charts recommended in France for ultrasound screening include measurements of head circumference (HC), biparietal diameter (BIP), abdominal circumference (AC) and femur length (FL). New international growth standards have been recommended since 2022. The aim of this work is to quantitatively describe the differences between these biometric curves.

METHODS

The biometry curves from the French College for Fetal Ultrasound, OMS and INTERGROWTH-21 are pragmatically compared based on their original quantile regression equations (superposition and quantification of differences in millimeters and in proportion) for different percentiles of clinical interest.

RESULTS

Compared with the new charts, CFEF underestimates HC<-3DS and AC<10eP. The proportions of differences between the CFEF and INTERGROWTH-21 or WHO curves always remained <5%. The proportions of difference of the 3rd percentile of HC and FL, 10th and 90th percentile of AC were always lower than 2%, 2%, 5% and 4% respectively, between OMS and INTERGROWTH-21.

CONCLUSION

The switch to prescriptive standards suggests an improvement in the detection of fetuses with AC<10th percentile, an improvement in the detection of prenatal onset microcephaly, with no argument for a decrease in the detection rate of severe constitutional bone disease or modification of obstetrical guidelines.

Clinical workflow of sonographers performing fetal anomaly ultrasound scans: deep-learning-based analysis

OBJECTIVE

Despite decades of obstetric scanning, the field of sonographer workflow remains largely unexplored. In the second trimester, sonographers use scan guidelines to guide their acquisition of standard planes and structures; however, the scan-acquisition order is not prescribed. Using deep-learning-based video analysis, the aim of this study was to develop a deeper understanding of the clinical workflow undertaken by sonographers during second-trimester anomaly scans.

METHODS

We collected prospectively full-length video recordings of routine second-trimester anomaly scans. Important scan events in the videos were identified by detecting automatically image freeze and image/clip save. The video immediately preceding and following the important event was extracted and labeled as one of 11 commonly acquired anatomical structures. We developed and used a purposely trained and tested deep-learning annotation model to label automatically the large number of scan events. Thus, anomaly scans were partitioned as a sequence of anatomical planes or fetal structures obtained over time.

RESULTS

A total of 496 anomaly scans performed by 14 sonographers were available for analysis. UK guidelines specify that an image or videoclip of five different anatomical regions must be stored and these were detected in the majority of scans: head/brain was detected in 97.2% of scans, coronal face view (nose/lips) in 86.1%, abdomen in 93.1%, spine in 95.0% and femur in 92.3%. Analyzing the clinical workflow, we observed that sonographers were most likely to begin their scan by capturing the head/brain (in 24.4% of scans), spine (in 23.2%) or thorax/heart (in 22.8%). The most commonly identified two-structure transitions were: placenta/amniotic fluid to maternal anatomy, occurring in 44.5% of scans; head/brain to coronal face (nose/lips) in 42.7%; abdomen to thorax/heart in 26.1%; and three-dimensional/four-dimensional face to sagittal face (profile) in 23.7%. Transitions between three or more consecutive structures in sequence were uncommon (up to 13% of scans). None of the captured anomaly scans shared an entirely identical sequence.

CONCLUSIONS

We present a novel evaluation of the anomaly scan acquisition process using a deep-learning-based analysis of ultrasound video. We note wide variation in the number and sequence of structures obtained during routine second-trimester anomaly scans. Overall, each anomaly scan was found to be unique in its scanning sequence, suggesting that sonographers take advantage of the fetal position and acquire the standard planes according to their visibility rather than following a strict acquisition order. © 2022 The Authors. Ultrasound in Obstetrics & Gynecology published by John Wiley & Sons Ltd on behalf of International Society of Ultrasound in Obstetrics and Gynecology.

Second and third trimester estimation of gestational age using ultrasound or maternal symphysis-fundal height measurements: A systematic review

Many vulnerable women seek antenatal care late in pregnancy. How should gestational age be determined? We examine all available studies estimating GA >20 weeks. Ultrasound is much better than fundal height, and using cerebellar measurement appears to be the most accurate.

Linked article: This article is commented on by Philip J. Steer, pp. 1459 in this issue. To view this minicommentary visit https://doi.org/10.1111/1471‐0528.17127 .

Expected-value bias in mid-trimester preterm birth screening

OBJECTIVES

Cervical length (CL) measurement ≤ 25 mm on mid-trimester ultrasound scan is a known risk factor for preterm birth, for which vaginal progesterone is recommended. The aims of this study were to evaluate whether CL measurement is affected by observer bias and to assess the impact on short cervix prevalence of masking CL measurement during routine mid-trimester ultrasound scan.

METHODS

This was a flash study designed for a 2-month period (October and November 2018) at Cruces University Hospital (Bizkaia, Spain), in which all CL measurements from routine mid-trimester scans were masked. During the study period, there was no modification of the routine screening method, and women with a short cervix were prescribed 200 mg vaginal progesterone daily as per usual. The control group included women examined in a 2-month period (April and May 2018) prior to the study, in which CL measurements were taken as usual by a non-blinded operator. The primary outcome was the prevalence of short cervix in each group.

RESULTS

A total of 983 CL measurements were analyzed, including 457 in the blinded group and 526 in the control group. The prevalence of short cervix was 2.7% in the non-blinded group and 5.5% in the blinded group (P = 0.024). We identified a statistically significant difference in the incidence of CL of 24-25 mm between the two groups, with a lower prevalence in the non-blinded vs blinded group (0.6% vs 2.4%; P < 0.005). Moreover, the distribution of CL values was normal in the blinded group, in contrast to the non-blinded group, which was characterized by skewed distribution of CL values.

CONCLUSIONS

Expected-value bias exists and should be taken into account when measuring CL in mid-trimester preterm birth screening. Blinding has demonstrated to be an effective strategy to improve the performance of CL screening in clinical practice. © 2021 International Society of Ultrasound in Obstetrics and Gynecology.

Influence of maternal body mass index on interobserver variability of fetal ultrasound biometry and amniotic-fluid assessment in late pregnancy

OBJECTIVE

To determine the interobserver reproducibility of fetal ultrasound biometric and amniotic-fluid measurements in the third trimester of pregnancy, according to maternal body mass index (BMI) category.

METHODS

This was a prospective cohort study of women with a singleton gestation beyond 34 weeks, recruited into four groups according to BMI category: normal (18.0-24.9 kg/m² ), overweight (25.0-29.9 kg/m²⁾ , obese (30.0-39.9 kg/m² ) and morbidly obese (≥ 40 kg/m² ). Multiple pregnancies, women with diabetes and pregnancies with a fetal growth, structural or genetic abnormality were excluded. In each woman, fetal biometric (biparietal diameter (BPD), head circumference, abdominal circumference (AC), femur length (FL) and estimated fetal weight) and amniotic-fluid (amniotic-fluid index (AFI) and maximum vertical pocket (MVP)) measurements were obtained by two experienced sonographers or physicians, blinded to gestational age and each other's measurements. Differences in measurements between observers were expressed as gestational age-specific Z-scores. The interobserver intraclass correlation coefficient (ICC) and Cronbach's reliability coefficient (CRC) were calculated. Bland-Altman analysis was used to assess the degree of reproducibility.

RESULTS

In total, 110 women were enrolled prospectively (including 1320 measurements obtained by 17 sonographers or physicians). Twenty (18.2%) women had normal BMI, 30 (27.3%) women were overweight, 30 (27.3%) women were obese and 30 (27.3%) women were morbidly obese. Except for AFI (ICC, 0.65; CRC, 0.78) and MVP (ICC, 0.49; CRC, 0.66), all parameters had a very high level of interobserver reproducibility (ICC, 0.72-0.87; CRC, 0.84-0.93). When assessing reproducibility according to BMI category, BPD measurements had a very high level of reproducibility (ICC ≥ 0.85; CRC > 0.90) in all groups. The reproducibility of AC and FL measurements increased with increasing BMI, while the reproducibility of MVP measurements decreased. Among the biometric parameters, the difference between the BMI categories in measurement-difference Z-score was significant only for FL. Interobserver differences for biometric measurements fell within the 95% limits of agreement.

CONCLUSION

Obesity does not seem to impact negatively on the reproducibility of ultrasound measurements of fetal biometric parameters when undertaken by experienced sonographers or physicians who commonly assess overweight, obese and morbidly obese women. © 2021 International Society of Ultrasound in Obstetrics and Gynecology.

The Use of Artificial Intelligence in Automation in the Fields of Gynaecology and Obstetrics - an Assessment of the State of Play

The long-awaited progress in digitalisation is generating huge amounts of medical data every day, and manual analysis and targeted, patient-oriented evaluation of this data is becoming increasingly difficult or even infeasible. This state of affairs and the associated, increasingly complex requirements for individualised precision medicine underline the need for modern software solutions and algorithms across the entire healthcare system. The utilisation of state-of-the-art equipment and techniques in almost all areas of medicine over the past few years has now indeed enabled automation processes to enter - at least in part - into routine clinical practice. Such systems utilise a wide variety of artificial intelligence (AI) techniques, the majority of which have been developed to optimise medical image reconstruction, noise reduction, quality assurance, triage, segmentation, computer-aided detection and classification and, as an emerging field of research, radiogenomics. Tasks handled by AI are completed significantly faster and more precisely, clearly demonstrated by now in the annual findings of the ImageNet Large-Scale Visual Recognition Challenge (ILSVCR), first conducted in 2015, with error rates well below those of humans. This review article will discuss the potential capabilities and currently available applications of AI in gynaecological-obstetric diagnostics. The article will focus, in particular, on automated techniques in prenatal sonographic diagnostics.

Transforming obstetric ultrasound into data science using eye tracking, voice recording, transducer motion and ultrasound video

Ultrasound is the primary modality for obstetric imaging and is highly sonographer dependent. Long training period, insufficient recruitment and poor retention of sonographers are among the global challenges in the expansion of ultrasound use. For the past several decades, technical advancements in clinical obstetric ultrasound scanning have largely concerned improving image quality and processing speed. By contrast, sonographers have been acquiring ultrasound images in a similar fashion for several decades. The PULSE (Perception Ultrasound by Learning Sonographer Experience) project is an interdisciplinary multi-modal imaging study aiming to offer clinical sonography insights and transform the process of obstetric ultrasound acquisition and image analysis by applying deep learning to large-scale multi-modal clinical data. A key novelty of the study is that we record full-length ultrasound video with concurrent tracking of the sonographer's eyes, voice and the transducer while performing routine obstetric scans on pregnant women. We provide a detailed description of the novel acquisition system and illustrate how our data can be used to describe clinical ultrasound. Being able to measure different sonographer actions or model tasks will lead to a better understanding of several topics including how to effectively train new sonographers, monitor the learning progress, and enhance the scanning workflow of experts.

The impact of individual sonographer variation on the detection of small for gestational age fetuses using a third trimester growth scan

OBJECTIVES

Detection of small for gestational age (SGA) fetuses in a third trimester ultrasound could be affected by variation in sonographer performance.

METHODS

Retrospective analysis of all singleton, non-anomalous ultrasound examinations between 35⁺⁰ -36⁺⁶ weeks gestation, in a single institution where a universal 36-week scan is offered. Screen positive was defined as estimated fetal weight (EFW) <10th centile; SGA was birthweight <10th centile. Individual sonographers' distributions of head circumference (HC), abdominal circumference (AC) and femur length (FL) were used to assess sonographers' screen positive rate (SPR), detection rate (DR) and true positive rate (TPR). Univariate and multivariate regression analysis was performed to assess the association between the sonographers' mean and SD (SD) for HC, AC, FL and their SPR, DR and TPR.

RESULTS

There were 27 sonographers performing more than 50 examinations per year, a total of 5691 scans. The mean incidence of SGA was 10.0%. For an overall SPR of 9.4%, the overall DR was 43.8% (95% CI: 39.6% - 48.1%) and the overall TPR was 46.5% (95% CI: 42.9% - 50.2%). Higher AC scatter (SD difference up to 11.6 mm) was associated with higher SPR (P = 0.001). Lower mean FL (difference up to 3.6 mm) was associated with higher SPR (P = 0.003) and higher DR (P = 0.002). As a result, DR varied amongst different sonographers between 14.3% and 85.7% and TPR varied between 8.3% and 100.0%.

CONCLUSIONS

Monitoring of individual AC and FL distributions is a simple and effective tool for institutional quality assurance.

Artificial intelligence in ultrasound

Ultrasound (US), a flexible green imaging modality, is expanding globally as a first-line imaging technique in various clinical fields following with the continual emergence of advanced ultrasonic technologies and the well-established US-based digital health system. Actually, in US practice, qualified physicians should manually collect and visually evaluate images for the detection, identification and monitoring of diseases. The diagnostic performance is inevitably reduced due to the intrinsic property of high operator-dependence from US. In contrast, artificial intelligence (AI) excels at automatically recognizing complex patterns and providing quantitative assessment for imaging data, showing high potential to assist physicians in acquiring more accurate and reproducible results. In this article, we will provide a general understanding of AI, machine learning (ML) and deep learning (DL) technologies; We then review the rapidly growing applications of AI-especially DL technology in the field of US-based on the following anatomical regions: thyroid, breast, abdomen and pelvis, obstetrics heart and blood vessels, musculoskeletal system and other organs by covering image quality control, anatomy localization, object detection, lesion segmentation, and computer-aided diagnosis and prognosis evaluation; Finally, we offer our perspective on the challenges and opportunities for the clinical practice of biomedical AI systems in US.

The impact of optimal dating on the assessment of fetal growth

BACKGROUND

The impact of using the Intergrowth (IG) dating formulae in comparison to the commonly used Robinson dating on the evaluation of biometrics and estimated fetal weight (EFW) has not been evaluated.

METHODS

Nationwide cross-sectional study of routine fetal ultrasound biometry in low-risk pregnant women whose gestational age (GA) had been previously assessed by a first trimester CRL measurement. We compared the CRL-based GA according to the Robinson formula and the IG formula. We evaluated the fetal biometric measurements as well as the EFW taken later in pregnancy depending on the dating formula used. Mean and standard deviation of the Z scores as well as the number and percentage of cases classified as <3rd, < 10th, >90th and > 97th percentile were compared.

RESULTS

Three thousand five hundred twenty-two low-risk women with scans carried out after 18 weeks were included. There were differences of zero, one and 2 days in 642 (18.2%), 2700 (76.7%) and 180 (5%) when GA was estimated based on the Robinson or the IG formula, respectively. The biometry Z scores assessed later in pregnancy were all statistically significantly lower when the Intergrowth-based dating formula was used (p < 10- 4). Likewise, the number and percentage of foetuses classified as <3rd, < 10th, >90th and > 97th percentile demonstrated significant differences. As an example, the proportion of SGA foetuses varied from 3.46 to 4.57% (p = 0.02) and that of LGA foetuses from 17.86 to 13.4% (p < 10- 4).

CONCLUSION

The dating formula used has a quite significant impact on the subsequent evaluation of biometry and EFW. We suggest that the combined and homogeneous use of a recent dating standard, together with prescriptive growth standards established on the same low-risk pregnancies, allows an optimal assessment of fetal growth.

Introduction to artificial intelligence in ultrasound imaging in obstetrics and gynecology

Artificial intelligence (AI) uses data and algorithms to aim to draw conclusions that are as good as, or even better than, those drawn by humans. AI is already part of our daily life; it is behind face recognition technology, speech recognition in virtual assistants (such as Amazon Alexa, Apple's Siri, Google Assistant and Microsoft Cortana) and self-driving cars. AI software has been able to beat world champions in chess, Go and recently even Poker. Relevant to our community, it is a prominent source of innovation in healthcare, already helping to develop new drugs, support clinical decisions and provide quality assurance in radiology. The list of medical image-analysis AI applications with USA Food and Drug Administration or European Union (soon to fall under European Union Medical Device Regulation) approval is growing rapidly and covers diverse clinical needs, such as detection of arrhythmia using a smartwatch or automatic triage of critical imaging studies to the top of the radiologist's worklist. Deep learning, a leading tool of AI, performs particularly well in image pattern recognition and, therefore, can be of great benefit to doctors who rely heavily on images, such as sonologists, radiographers and pathologists. Although obstetric and gynecological ultrasound are two of the most commonly performed imaging studies, AI has had little impact on this field so far. Nevertheless, there is huge potential for AI to assist in repetitive ultrasound tasks, such as automatically identifying good-quality acquisitions and providing instant quality assurance. For this potential to thrive, interdisciplinary communication between AI developers and ultrasound professionals is necessary. In this article, we explore the fundamentals of medical imaging AI, from theory to applicability, and introduce some key terms to medical professionals in the field of ultrasound. We believe that wider knowledge of AI will help accelerate its integration into healthcare. © 2020 The Authors. Ultrasound in Obstetrics & Gynecology published by John Wiley & Sons Ltd on behalf of the International Society of Ultrasound in Obstetrics and Gynecology.

Peer review of third trimester abdominal circumference measurements

Background

Third trimester growth scans represent a significant proportion of the workload in obstetric ultrasound departments. The objective of these serial growth scans is to improve the antenatal detection of babies with fetal growth restriction. The aim of this paper is to describe a method of peer review for third trimester abdominal circumference measurements which is realistic within busy obstetric ultrasound departments in the UK.

Method

Twenty-two, third trimester, measured abdominal circumference images were randomly selected. Images were assessed subjectively by 12 sonographers using the image Criteria Achieved Score. For quantitative assessment, termed the Inter-operator Variability Score, three of the abdominal circumference (AC) images were blindly remeasured. Following this, a questionnaire was used to ascertain which image criteria sonographers considered most important and to reach an agreement on correct caliper placement.

Results

The least frequently met image criteria with the lowest Criteria Achieved Score related to an oblique abdominal circumference section. These included fetal kidney present (Criteria Achieved Score 24.6%), multiple oblique ribs (Criteria Achieved Score 39.4%) and oblique spine (Criteria Achieved Score 37.5%). Caliper placement was also identified as inconsistent.

Discussion

This study demonstrates that the perfect AC section is not always possible and sonographers use their professional judgement to determine whether an image is acceptable. Seventy-three percent of the images reviewed were of an acceptable standard. There can be inconsistencies in sonographer opinion regarding what is an acceptable third trimester abdominal circumference image. These differences need to be addressed to maximise the effectiveness of the third trimester ultrasound examination.

Conclusion

Peer review can be used to monitor scan quality and identify areas of inconsistency.

How often do we identify fetal abnormalities during routine third-trimester ultrasound? A systematic review and meta-analysis

BACKGROUND

Routine third-trimester ultrasound is frequently offered to pregnant women to identify fetuses with abnormal growth. Infrequently, a congenital anomaly is incidentally detected.

OBJECTIVE

To establish the prevalence and type of fetal anomalies detected during routine third-trimester scans using a systematic review and meta-analysis.

SEARCH STRATEGY

Electronic databases (MEDLINE, Embase and the Cochrane library) from inception until August 2019.

SELECTION CRITERIA

Population-based studies (randomised control trials, prospective and retrospective cohorts) reporting abnormalities detected at the routine third-trimester ultrasound performed in unselected populations with prior screening. Case reports, case series, case-control studies and reviews without original data were excluded.

DATA COLLECTION AND ANALYSIS

Prevalence and type of anomalies detected in the third trimester. We calculated pooled prevalence as the number of anomalies per 1000 scans with 95% confidence intervals. Publication bias was assessed.

MAIN RESULTS

The literature search identified 9594 citations: 13 studies were eligible representing 141 717 women; 643 were diagnosed with an unexpected abnormality. The pooled prevalence of a new abnormality diagnosed was 3.68 per 1000 women scanned (95% CI 2.72-4.78). The largest groups of abnormalities were urogenital (55%), central nervous system abnormalities (18%) and cardiac abnormalities (14%).

CONCLUSION

Combining data from 13 studies and over 140 000 women, we show that during routine third-trimester ultrasound, an incidental fetal anomaly will be found in about 1 in 300 scanned women. This information should be taken into account when taking consent from women for third-trimester ultrasound and when designing and assessing cost of third-trimester ultrasound screening programmes.

TWEETABLE ABSTRACT

One in 300 women attending a third-trimester scan will have a finding of a fetal abnormality.

International gestational age-specific centiles for umbilical artery Doppler indices: a longitudinal prospective cohort study of the INTERGROWTH-21st Project

BACKGROUND

Reference values for umbilical artery Doppler indices are used clinically to assess fetal well-being. However, many studies that have produced reference charts have important methodologic limitations, and these result in significant heterogeneity of reported reference ranges.

OBJECTIVES

To produce international gestational age-specific centiles for umbilical artery Doppler indices based on longitudinal data and the same rigorous methodology used in the original Fetal Growth Longitudinal Study of the INTERGROWTH-21st Project.

STUDY DESIGN

In Phase II of the INTERGROWTH-21st Project (the INTERBIO-21st Study), we prospectively continued enrolling pregnant women according to the same protocol from 3 of the original populations in Pelotas (Brazil), Nairobi (Kenya), and Oxford (United Kingdom) that had participated in the Fetal Growth Longitudinal Study. Women with a singleton pregnancy were recruited at <14 weeks' gestation, confirmed by ultrasound measurement of crown-rump length, and then underwent standardized ultrasound every 5±1 weeks until delivery. From 22 weeks of gestation umbilical artery indices (pulsatility index, resistance index, and systolic/diastolic ratio) were measured in a blinded fashion, using identical equipment and a rigorously standardized protocol. Newborn size at birth was assessed using the international INTERGROWTH-21st Standards, and infants had detailed assessment of growth, nutrition, morbidity, and motor development at 1 and 2 years of age. The appropriateness of pooling data from the 3 study sites was assessed using variance component analysis and standardized site differences. Umbilical artery indices were modeled as functions of the gestational age using an exponential, normal distribution with second-degree fractional polynomial smoothing; goodness of fit for the overall models was assessed.

RESULTS

Of the women enrolled at the 3 sites, 1629 were eligible for this study; 431 (27%) met the entry criteria for the construction of normative centiles, similar to the proportion seen in the original fetal growth longitudinal study. They contributed a total of 1243 Doppler measures to the analysis; 74% had 3 measures or more. The healthy low-risk status of the population was confirmed by the low rates of preterm birth (4.9%) and preeclampsia (0.7%). There were no neonatal deaths and satisfactory growth, health, and motor development of the infants at 1 and 2 years of age were documented. Only a very small proportion (2.8%-6.5%) of the variance of Doppler indices was due to between-site differences; in addition, standardized site difference estimates were marginally outside this threshold in only 1 of 27 comparisons, and this supported the decision to pool data from the 3 study sites. All 3 Doppler indices decreased with advancing gestational age. The 3rd, 5th 10th, 50th, 90th, 95th, and 97th centiles according to gestational age for each of the 3 indices are provided, as well as equations to allow calculation of any value as a centile and z scores. The mean pulsatility index according to gestational age = 1.02944 + 77.7456*(gestational age)-2 - 0.000004455*gestational age3.

CONCLUSION

We present here international gestational age-specific normative centiles for umbilical artery Doppler indices produced by studying healthy, low-risk pregnant women living in environments with minimal constraints on fetal growth. The centiles complement the existing INTERGROWTH-21st Standards for assessment of fetal well-being.

Expected-value bias: is it time to remove features from ultrasound machines?

Linked Comment: Ultrasound Obstet Gynecol 2020; 55:375-382.