Iron deficiency anemia at delivery is associated with an increased risk for cesarean section and adverse maternal and neonatal outcomes in otherwise healthy women. Monitoring/correction of hemoglobin concentrations even in late pregnancy may prevent these adverse events.

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.

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-21 st Project. STUDY DESIGN: In Phase II of the INTERGROWTH-21 st 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 crownerump length, and then underwent standardized ultrasound every 5AE1 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-21 st 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 lowrisk 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% e6.5%) 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 ...

Major obstetric hemorrhage is a rare event with potentially modifiable risk factors which represent a platform of interventions for lessening obstetric morbidity.

A negative sliding sign predicts severe intra-abdominal adhesions encountered during repeat cesarean delivery, longer time to delivery, and a higher chance of bleeding.

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We describe an automatic natural language processing (NLP)based image captioning method to describe fetal ultrasound video content by modelling the vocabulary commonly used by sonographers and sonologists. The generated captions are similar to the words spoken by a sonographer when describing the scan experience in terms of visual content and performed scanning actions. Using full-length second-trimester fetal ultrasound videos and text derived from accompanying expert voice-over audio recordings, we train deep learning models consisting of convolutional neural networks and recurrent neural networks in merged configurations to generate captions for ultrasound video frames. We evaluate different model architectures using established general metrics (BLEU, ROUGE-L) and application-specific metrics. Results show that the proposed models can learn joint representations of image and text to generate relevant and descriptive captions for anatomies, such as the spine, the abdomen, the heart, and the head, in clinical fetal ultrasound scans.

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.