Expected-value bias in routine third-trimester growth scans.

OBJECTIVES: Operators performing fetal growth scans are usually aware of the gestational age of the pregnancy, which may lead to expected-value bias when performing biometric measurements. We aimed to evaluate the incidence of expected-value bias in routine fetal growth scans and assess its impact on standard biometric measurements. METHODS: We collected prospectively full-length video recordings of routine ultrasound growth scans coupled with operator eye tracking. Expected value was defined as the gestational age at the time of the scan, based on the estimated due date that was established at the dating scan. Expected-value bias was defined as occurring when the operator looked at the measurement box on the screen during the process of caliper adjustment before saving a measurement. We studied the three standard biometric planes on which measurements of head circumference (HC), abdominal circumference (AC) and femur length (FL) are obtained. We evaluated the incidence of expected-value bias and quantified the impact of biased measurements. RESULTS: We analyzed 272 third-trimester growth scans, performed by 16 operators, during which a total of 1409 measurements (354 HC, 703 AC and 352 FL; including repeat measurements) were obtained. Expected-value bias occurred in 91.4% of the saved standard biometric plane measurements (85.0% for HC, 92.9% for AC and 94.9% for FL). The operators were more likely to adjust the measurements towards the expected value than away from it (47.7% vs 19.7% of measurements; P < 0.001). On average, measurements were corrected by 2.3 ± 5.6, 2.4 ± 10.4 and 3.2 ± 10.4 days of gestation towards the expected gestational age for the HC, AC, and FL measurements, respectively. Additionally, we noted a statistically significant reduction in measurement variance once the operator was biased (P = 0.026). Comparing the lowest and highest possible estimated fetal weight (using the smallest and largest biased HC, AC and FL measurements), we noted that the discordance, in percentage terms, was 10.1% ± 6.5%, and that in 17% (95% CI, 12-21%) of the scans, the fetus could be considered as small-for-gestational age or appropriate-for-gestational age if using the smallest or largest possible measurements, respectively. Similarly, in 13% (95% CI, 9-16%) of scans, the fetus could be considered as large-for-gestational age or appropriate-for-gestational age if using the largest or smallest possible measurements, respectively. CONCLUSIONS: During routine third-trimester growth scans, expected-value bias frequently occurs and significantly changes standard biometric measurements obtained. © 2019 the Authors. Ultrasound in Obstetrics & Gynecology published by John Wiley & Sons Ltd on behalf of the International Society of Ultrasound in Obstetrics and Gynecology

Efficient Ultrasound Image Analysis Models with Sonographer Gaze Assisted Distillation.

Recent automated medical image analysis methods have attained state-of-the-art performance but have relied on memory and compute-intensive deep learning models. Reducing model size without significant loss in performance metrics is crucial for time and memory-efficient automated image-based decision-making. Traditional deep learning based image analysis only uses expert knowledge in the form of manual annotations. Recently, there has been interest in introducing other forms of expert knowledge into deep learning architecture design. This is the approach considered in the paper where we propose to combine ultrasound video with point-of-gaze tracked for expert sonographers as they scan to train memory-efficient ultrasound image analysis models. Specifically we develop teacher-student knowledge transfer models for the exemplar task of frame classification for the fetal abdomen, head, and femur. The best performing memory-efficient models attain performance within 5% of conventional models that are 1000× larger in size

Small for Gestational Age Babies After 37 Weeks: An Impact Study of a Risk Stratification Protocol.

OBJECTIVES: Although no clear evidence exists, many international guidelines advocate early term delivery of small for gestational age (SGA) fetuses. The aim of this study was to determine whether a protocol that included monitoring SGA fetuses beyond 37 weeks affected perinatal and maternal outcomes. METHODS: The impact of the introduction in 2014 of a protocol for management of SGA, which included risk stratification with surveillance and expectant management after 37 weeks for lower risk babies (Group 2), was compared with the previous strategy, which recommended delivery at around 37 weeks (Group 1). Data from all referred SGA babies over a 39 month period were analyzed. RESULTS: In group 1 there were 138 SGA babies; in group 2 there were 143. The mean gestation at delivery was 37 + 4 and 38 + 2 weeks respectively (p = 0.04). The incidence of neonatal composite adverse outcomes was lower in Group 2 (9% v 22% v; p < 0.01) as was neonatal NNU admission (13% v 42%; p < 0.01). Induction of labour and caesarean section rates were lower, and vaginal delivery (83% v 60%; p < 0.01) was higher in group 2. Most of the differences were due to delayed delivery of SGA babies that were stratified as low risk. CONCLUSIONS: This study suggests that protocol-based management of SGA babies may improve outcomes and that identification of moderate SGA should not alone prompt delivery. Larger numbers are required to assess any impact on perinatal mortality

Spatio-Temporal Partitioning And Description Of Full-Length Routine Fetal Anomaly Ultrasound Scans

This paper considers automatic clinical workflow description of full-length routine fetal anomaly ultrasound scans using deep learning approaches for spatio-temporal video analysis. Multiple architectures consisting of 2D and 2D + t CNN, LSTM, and convolutional LSTM are investigated and compared. The contributions of short-term and long-term temporal changes are studied, and a multi-stream framework analysis is found to achieve the best top-l accuracy =0.77 and top-3 accuracy =0.94. Automated partitioning and characterisation on unlabelled full-length video scans show high correlation (ρ=0.95, p=0.0004) with workflow statistics of manually labelled videos, suggesting practicality of proposed methods

Self-Supervised Ultrasound to MRI Fetal Brain Image Synthesis

Fetal brain magnetic resonance imaging (MRI) offers exquisite images of the developing brain but is not suitable for second-trimester anomaly screening, for which ultrasound (US) is employed. Although expert sonographers are adept at reading US images, MR images which closely resemble anatomical images are much easier for non-experts to interpret. Thus in this paper we propose to generate MR-like images directly from clinical US images. In medical image analysis such a capability is potentially useful as well, for instance for automatic US-MRI registration and fusion. The proposed model is end-to-end trainable and self-supervised without any external annotations. Specifically, based on an assumption that the US and MRI data share a similar anatomical latent space, we first utilise a network to extract the shared latent features, which are then used for MRI synthesis. Since paired data is unavailable for our study (and rare in practice), pixel-level constraints are infeasible to apply. We instead propose to enforce the distributions to be statistically indistinguishable, by adversarial learning in both the image domain and feature space. To regularise the anatomical structures between US and MRI during synthesis, we further propose an adversarial structural constraint. A new cross-modal attention technique is proposed to utilise non-local spatial information, by encouraging multi-modal knowledge fusion and propagation. We extend the approach to consider the case where 3D auxiliary information (e.g., 3D neighbours and a 3D location index) from volumetric data is also available, and show that this improves image synthesis. The proposed approach is evaluated quantitatively and qualitatively with comparison to real fetal MR images and other approaches to synthesis, demonstrating its feasibility of synthesising realistic MR images.Comment: IEEE Transactions on Medical Imaging 202

International standards for early fetal size and pregnancy dating based on ultrasound measurement of crown-rump length in the first trimester of pregnancy.

OBJECTIVES: There are no international standards for relating fetal crown-rump length (CRL) to gestational age (GA), and most existing charts have considerable methodological limitations. The INTERGROWTH-21(st) Project aimed to produce the first international standards for early fetal size and ultrasound dating of pregnancy based on CRL measurement. METHODS: Urban areas in eight geographically diverse countries that met strict eligibility criteria were selected for the prospective, population-based recruitment, between 9 + 0 and 13 + 6 weeks' gestation, of healthy well-nourished women with singleton pregnancies at low risk of fetal growth impairment. GA was calculated on the basis of a certain last menstrual period, regular menstrual cycle and lack of hormonal medication or breastfeeding in the preceding 2 months. CRL was measured using strict protocols and quality-control measures. All women were followed up throughout pregnancy until delivery and hospital discharge. Cases of neonatal and fetal death, severe pregnancy complications and congenital abnormalities were excluded from the study. RESULTS: A total of 4607 women were enrolled in the Fetal Growth Longitudinal Study, one of the three main components of the INTERGROWTH-21(st) Project, of whom 4321 had a live singleton birth in the absence of severe maternal conditions or congenital abnormalities detected by ultrasound or at birth. The CRL was measured in 56 women at < 9 + 0 weeks' gestation; these were excluded, resulting in 4265 women who contributed data to the final analysis. The mean CRL and SD increased with GA almost linearly, and their relationship to GA is given by the following two equations (in which GA is in days and CRL in mm): mean CRL = -50.6562 + (0.815118 × GA) + (0.00535302 × GA(2) ); and SD of CRL = -2.21626 + (0.0984894 × GA). GA estimation is carried out according to the two equations: GA = 40.9041 + (3.21585 × CRL(0.5) ) + (0.348956 × CRL); and SD of GA = 2.39102 + (0.0193474 × CRL). CONCLUSIONS: We have produced international prescriptive standards for early fetal linear size and ultrasound dating of pregnancy in the first trimester that can be used throughout the world

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

Ultrasound methodology used to construct the fetal growth standards in the INTERGROWTH-21st Project

A unified protocol is essential to ensure that fetal ultrasound measurements taken in multicentre research studies are accurate and reproducible. This paper describes the methodology used to take two-dimensional, ultrasound measurements in the longitudinal, fetal growth component of the INTERGROWTH-21st Project. These standardised methods should minimise the systematic errors associated with pooling data from different study sites. They represent a model for carrying out similar research studies in the future

Machine learning-based analysis of operator pupillary response to assess cognitive workload in clinical ultrasound imaging.

INTRODUCTION: Pupillometry, the measurement of eye pupil diameter, is a well-established and objective modality correlated with cognitive workload. In this paper, we analyse the pupillary response of ultrasound imaging operators to assess their cognitive workload, captured while they undertake routine fetal ultrasound examinations. Our experiments and analysis are performed on real-world datasets obtained using remote eye-tracking under natural clinical environmental conditions. METHODS: Our analysis pipeline involves careful temporal sequence (time-series) extraction by retrospectively matching the pupil diameter data with tasks captured in the corresponding ultrasound scan video in a multi-modal data acquisition setup. This is followed by the pupil diameter pre-processing and the calculation of pupillary response sequences. Exploratory statistical analysis of the operator pupillary responses and comparisons of the distributions between ultrasonographic tasks (fetal heart versus fetal brain) and operator expertise (newly-qualified versus experienced operators) are performed. Machine learning is explored to automatically classify the temporal sequences into the corresponding ultrasonographic tasks and operator experience using temporal, spectral, and time-frequency features with classical (shallow) models, and convolutional neural networks as deep learning models. RESULTS: Preliminary statistical analysis of the extracted pupillary response shows a significant variation for different ultrasonographic tasks and operator expertise, suggesting different extents of cognitive workload in each case, as measured by pupillometry. The best-performing machine learning models achieve receiver operating characteristic (ROC) area under curve (AUC) values of 0.98 and 0.80, for ultrasonographic task classification and operator experience classification, respectively. CONCLUSION: We conclude that we can successfully assess cognitive workload from pupil diameter changes measured while ultrasound operators perform routine scans. The machine learning allows the discrimination of the undertaken ultrasonographic tasks and scanning expertise using the pupillary response sequences as an index of the operators' cognitive workload. A high cognitive workload can reduce operator efficiency and constrain their decision-making, hence, the ability to objectively assess cognitive workload is a first step towards understanding these effects on operator performance in biomedical applications such as medical imaging