3,318 research outputs found
International standards for fetal brain structures based on serial ultrasound measurements from the Fetal Growth Longitudinal Study of the INTERGROWTH-21st Project.
OBJECTIVE: To create prescriptive growth standards for five fetal brain structures, measured by ultrasound, from healthy, well-nourished women, at low risk of impaired fetal growth and poor perinatal outcomes, taking part in the Fetal Growth Longitudinal Study (FGLS) of the INTERGROWTH-21st Project. METHODS: This was a complementary analysis of a large, population-based, multicentre, longitudinal study. We measured, in planes reconstructed from 3-dimensional (3D) ultrasound volumes of the fetal head at different time points in pregnancy, the size of the parieto-occipital fissure (POF), Sylvian fissure (SF), anterior horn of the lateral ventricle (AV), atrium of the posterior ventricle (PV) and cisterna magna (CM). The sample analysed was randomly selected from the overall FGLS population, ensuring an equal distribution amongst the eight diverse participating sites and of 3D ultrasound volumes across pregnancy (range: 15 - 36âweeks' gestation). Fractional polynomials were used to the construct standards. Growth and development of the infants were assessed at 1 and 2âyears of age to confirm their adequacy for constructing international standards. RESULTS: From the entire FGLS cohort of 4321 women, 451 (10.4%) were randomly selected. After exclusions, 3D ultrasound volumes from 442 fetuses born without congenital malformations were used to create the charts. The fetal brain structures of interest were identified in 90% of cases. All structures showed increasing size with gestation and increasing variability for the POF, SF, PV and CM. The 3rd , 5th , 50th , 95th and 97th smoothed centile are presented. The 5th centile of POF and SF were 2.8 and 4.3 at 22âweeks and 4.2 and 9.4mm at 32âweeks respectively. The 95th centile of PV and CM were 8.5 and 7.4 at 22âweeks and 8.5 and 9.4mm at 32âweeks respectively. CONCLUSIONS: We have produced prescriptive size standards for fetal brain structures based on prospectively enrolled pregnancies at low risk of abnormal outcomes. We recommend these as international standards for the assessment of measurements obtained by ultrasound from fetal brain structures
Machine learning and disease prediction in obstetrics
Machine learning technologies and translation of artificial intelligence tools to enhance the patient experience are changing obstetric and maternity care. An increasing number of predictive tools have been developed with data sourced from electronic health records, diagnostic imaging and digital devices. In this review, we explore the latest tools of machine learning, the algorithms to establish prediction models and the challenges to assess fetal well-being, predict and diagnose obstetric diseases such as gestational diabetes, pre-eclampsia, preterm birth and fetal growth restriction. We discuss the rapid growth of machine learning approaches and intelligent tools for automated diagnostic imaging of fetal anomalies and to asses fetoplacental and cervix function using ultrasound and magnetic resonance imaging. In prenatal diagnosis, we discuss intelligent tools for magnetic resonance imaging sequencing of the fetus, placenta and cervix to reduce the risk of preterm birth. Finally, the use of machine learning to improve safety standards in intrapartum care and early detection of complications will be discussed. The demand for technologies to enhance diagnosis and treatment in obstetrics and maternity should improve frameworks for patient safety and enhance clinical practice
Real-time standard scan plane detection and localisation in fetal ultrasound using fully convolutional neural networks
Fetal mid-pregnancy scans are typically carried out according to fixed protocols. Accurate detection of abnormalities and correct biometric measurements hinge on the correct acquisition of clearly defined standard scan planes. Locating these standard planes requires a high level of expertise. However, there is a worldwide shortage of expert sonographers. In this paper, we consider a fully automated system based on convolutional neural networks which can detect twelve standard scan planes as defined by the UK fetal abnormality screening programme. The network design allows real-time inference and can be naturally extended to provide an approximate localisation of the fetal anatomy in the image. Such a framework can be used to automate or assist with scan plane selection, or for the retrospective retrieval of scan planes from recorded videos. The method is evaluated on a large database of 1003 volunteer mid-pregnancy scans. We show that standard planes acquired in a clinical scenario are robustly detected with a precision and recall of 69 % and 80 %, which is superior to the current state-of-the-art. Furthermore, we show that it can retrospectively retrieve correct scan planes with an accuracy of 71 % for cardiac views and 81 % for non-cardiac views
Analysis of maturation features in fetal brain ultrasound via artificial intelligence for the estimation of gestational age
Background:
Optimal prenatal care relies on accurate gestational age dating. After the first trimester, the accuracy of current gestational age estimation methods diminishes with increasing gestational age. Considering that, in many countries, access to first trimester crown rump length is still difficult owing to late booking, infrequent access to prenatal care, and unavailability of early ultrasound examination, the development of accurate methods for gestational age estimation in the second and third trimester of pregnancy remains an unsolved challenge in fetal medicine.
Objective.
This study aimed to evaluate the performance of an artificial intelligence method based on automated analysis of fetal brain morphology on standard cranial ultrasound sections to estimate the gestational age in second and third trimester fetuses compared with the current formulas using standard fetal biometry.
Study Design:
Standard transthalamic axial plane images from a total of 1394 patients undergoing routine fetal ultrasound were used to develop an artificial intelligence method to automatically estimate gestational age from the analysis of fetal brain information. We compared its performanceâas stand alone or in combination with fetal biometric parametersâagainst 4 currently used fetal biometry formulas on a series of 3065 scans from 1992 patients undergoing second (n=1761) or third trimester (n=1298) routine ultrasound, with known gestational age estimated from crown rump length in the first trimester.
Results:
Overall, 95% confidence interval of the error in gestational age estimation was 14.2 days for the artificial intelligence method alone and 11.0 when used in combination with fetal biometric parameters, compared with 12.9 days of the best method using standard biometrics alone. In the third trimester, the lower 95% confidence interval errors were 14.3 days for artificial intelligence in combination with biometric parameters and 17 days for fetal biometrics, whereas in the second trimester, the 95% confidence interval error was 6.7 and 7, respectively. The performance differences were even larger in the small-for-gestational-age fetuses group (14.8 and 18.5, respectively).
Conclusion:
An automated artificial intelligence method using standard sonographic fetal planes yielded similar or lower error in gestational age estimation compared with fetal biometric parameters, especially in the third trimester. These results support further research to improve the performance of these methods in larger studies.The research leading to these results was partially funded by Transmural Biotech S.L. In addition, the research has received funding from âla Caixaâ Foundation under grant agreements LCF/PR/GN14/10270005 and LCF/PR/GN18/10310003, the Instituto de Salud Carlos III (PI16/00861, PI17/00675) within the Plan Nacional de I+D+I and cofinanced by Instituto de Salud Carlos IIIâ SubdirecciĂłn General de EvaluaciĂłn together with the Fondo Europeo de Desarrollo Regional (FEDER) âUna manera de hacer Europa,â Cerebra Foundation for the Brain Injured Child (Carmarthen, Wales, United Kingdom), Cellex Foundation, ASISA Foundation, and Agency for Management of University and Research Grants under grant 2017 SGR number 1531. In addition, E.E. has received funding from the Departament de Salut under grant number SLT008/18/00156.Peer ReviewedPostprint (published version
Automatic quantitative MRI texture analysis in small-for-gestational-age fetuses discriminates abnormal neonatal neurobehavior
Background: We tested the hypothesis whether texture analysis (TA) from MR images could identify patterns associated with an abnormal neurobehavior in small for gestational age (SGA) neonates. Methods: Ultrasound and MRI were performed on 91 SGA fetuses at 37 weeks of GA. Frontal lobe, basal ganglia, mesencephalon and cerebellum were delineated from fetal MRIs. SGA neonates underwent NBAS test and were classified as abnormal if $1 area was ,5th centile and as normal if all areas were .5th centile. Textural features associated with neurodevelopment were selected and machine learning was used to model a predictive algorithm. Results: Of the 91 SGA neonates, 49 were classified as normal and 42 as abnormal. The accuracies to predict an abnormal neurobehavior based on TA were 95.12% for frontal lobe, 95.56% for basal ganglia, 93.18% for mesencephalon and 83.33% for cerebellum. Conclusions: Fetal brain MRI textural patterns were associate
Born too early and too small: higher order cognitive function and brain at risk at ages 8â16
Prematurity presents a risk for higher order cognitive functions. Some of these deficits
manifest later in development, when these functions are expected to mature. However,
the causes and consequences of prematurity are still unclear. We conducted a
longitudinal study to first identify clinical predictors of ultrasound brain abnormalities in
196 children born very preterm (VP; gestational age 32 weeks) and with very low birth
weight (VLBW; birth weight 1500 g). At ages 8â16, the subset of VP-VLBW children
without neurological findings (124) were invited for a neuropsychological assessment
and an MRI scan (41 accepted). Of these, 29 met a rigorous criterion for MRI quality
and an age, and gender-matched control group (n = 14) was included in this study.
The key findings in the VP-VLBW neonates were: (a) 37% of the VP-VLBW neonates
had ultrasound brain abnormalities; (b) gestational age and birth weight collectively with
hospital course (i.e., days in hospital, neonatal intensive care, mechanical ventilation and
with oxygen therapy, surgeries, and retinopathy of prematurity) predicted ultrasound
brain abnormalities. At ages 8â16, VP-VLBW children showed: a) lower intelligent
quotient (IQ) and executive function; b) decreased gray and white matter (WM) integrity;
(c) IQ correlated negatively with cortical thickness in higher order processing cortical
areas. In conclusion, our data indicate that facets of executive function and IQ are the
most affected in VP-VLBW children likely due to altered higher order cortical areas and
underlying WMThis study was supported by the Spanish Government Institute Carlos III (FIS Pl11/02860), Spanish Ministry of Health to MM-L, and the University of Castilla-La Mancha mobility Grant VA1381500149
Automated fetal brain extraction from clinical Ultrasound volumes using 3D Convolutional Neural Networks
To improve the performance of most neuroimiage analysis pipelines, brain
extraction is used as a fundamental first step in the image processing. But in
the case of fetal brain development, there is a need for a reliable US-specific
tool. In this work we propose a fully automated 3D CNN approach to fetal brain
extraction from 3D US clinical volumes with minimal preprocessing. Our method
accurately and reliably extracts the brain regardless of the large data
variation inherent in this imaging modality. It also performs consistently
throughout a gestational age range between 14 and 31 weeks, regardless of the
pose variation of the subject, the scale, and even partial feature-obstruction
in the image, outperforming all current alternatives.Comment: 13 pages, 7 figures, MIUA conferenc
FUSQA: Fetal Ultrasound Segmentation Quality Assessment
Deep learning models have been effective for various fetal ultrasound
segmentation tasks. However, generalization to new unseen data has raised
questions about their effectiveness for clinical adoption. Normally, a
transition to new unseen data requires time-consuming and costly quality
assurance processes to validate the segmentation performance post-transition.
Segmentation quality assessment efforts have focused on natural images, where
the problem has been typically formulated as a dice score regression task. In
this paper, we propose a simplified Fetal Ultrasound Segmentation Quality
Assessment (FUSQA) model to tackle the segmentation quality assessment when no
masks exist to compare with. We formulate the segmentation quality assessment
process as an automated classification task to distinguish between good and
poor-quality segmentation masks for more accurate gestational age estimation.
We validate the performance of our proposed approach on two datasets we collect
from two hospitals using different ultrasound machines. We compare different
architectures, with our best-performing architecture achieving over 90%
classification accuracy on distinguishing between good and poor-quality
segmentation masks from an unseen dataset. Additionally, there was only a
1.45-day difference between the gestational age reported by doctors and
estimated based on CRL measurements using well-segmented masks. On the other
hand, this difference increased and reached up to 7.73 days when we calculated
CRL from the poorly segmented masks. As a result, AI-based approaches can
potentially aid fetal ultrasound segmentation quality assessment and might
detect poor segmentation in real-time screening in the future.Comment: 13 pages, 3 figures, 3 table
Quantitative Analysis of the Cervical Texture by Ultrasound and Correlation with Gestational Age
Objectives: Quantitative texture analysis has been proposed to extract robust features from the ultrasound image to detect subtle changes in the textures of the images. The aim of this study was to evaluate the feasibility of quantitative cervical texture analysis to assess cervical tissue changes throughout pregnancy. Methods: This was a cross-sectional study including singleton pregnancies between 20.0 and 41.6 weeks of gestation from women who delivered at term. Cervical length was measured, and a selected region of interest in the cervix was delineated. A model to predict gestational age based on features extracted from cervical images was developed following three steps: data splitting, feature transformation, and regression model computation. Results: Seven hundred images, 30 per gestational week, were included for analysis. There was a strong correlation between the gestational age at which the images were obtained and the estimated gestational age by quantitative analysis of the cervical texture (R = 0.88). Discussion: This study provides evidence that quantitative analysis of cervical texture can extract features from cervical ultrasound images which correlate with gestational age. Further research is needed to evaluate its applicability as a biomarker of the risk of spontaneous preterm birth, as well as its role in cervical assessment in other clinical situations in which cervical evaluation might be relevant
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