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Bradburn E, Conde-Agudelo A, Roberts NW, Villar J, Papageorghiou AT. Accuracy of prenatal and postnatal biomarkers for estimating gestational age: a systematic review and meta-analysis. EClinicalMedicine 2024; 70:102498. [PMID: 38495518 PMCID: PMC10940947 DOI: 10.1016/j.eclinm.2024.102498] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/18/2023] [Revised: 01/21/2024] [Accepted: 02/02/2024] [Indexed: 03/19/2024] Open
Abstract
Background 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). Methods 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. Findings 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. Interpretation 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. Funding 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.
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Affiliation(s)
- Elizabeth Bradburn
- Nuffield Department of Women’s & Reproductive Health, University of Oxford, Oxford, UK
| | - Agustin Conde-Agudelo
- Oxford Maternal & Perinatal Health Institute, Green Templeton College, University of Oxford, Oxford, UK
| | - Nia W. Roberts
- Bodleian Health Care Libraries, University of Oxford, Oxford, UK
| | - Jose Villar
- Nuffield Department of Women’s & Reproductive Health, University of Oxford, Oxford, UK
- Oxford Maternal & Perinatal Health Institute, Green Templeton College, University of Oxford, Oxford, UK
| | - Aris T. Papageorghiou
- Nuffield Department of Women’s & Reproductive Health, University of Oxford, Oxford, UK
- Oxford Maternal & Perinatal Health Institute, Green Templeton College, University of Oxford, Oxford, UK
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Cutland CL, Sawry S, Fairlie L, Barnabas S, Frajzyngier V, Roux JL, Izu A, Kekane-Mochwari KE, Vika C, De Jager J, Munson S, Jongihlati B, Stark JH, Absalon J. Obstetric and neonatal outcomes in South Africa. Vaccine 2024; 42:1352-1362. [PMID: 38310014 DOI: 10.1016/j.vaccine.2024.01.054] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2023] [Revised: 11/14/2023] [Accepted: 01/18/2024] [Indexed: 02/05/2024]
Abstract
BACKGROUND Background epidemiologic population data from low- and middle-income countries (LMIC), on maternal, foetal and neonatal adverse outcomes are limited. We aimed to estimate the incidence of maternal, foetal and neonatal adverse outcomes at South African maternal vaccine trial sites as reported directly in the clinical notes as well as using the 'Global Alignment of Immunization Safety Assessment in Pregnancy' case definitions (GAIA-CDs). GAIA-CDs were utilized as a tool to standardise data collection and outcome assessment, and the applicability and utility of the GAIA-CDs was evaluated in a LMIC observational study. METHODS We conducted a retrospective record review of maternity and neonatal case records for births that occurred in Soweto, Inner City- Johannesburg and Metro-East Cape Town, South Africa, between 1st July 2017 and 30th June 2018. Study staff abstracted data from randomly selected medical charts onto standardized study-specific forms. Incidence (per 100,000 population) was calculated for adverse maternal, foetal and neonatal outcomes, which were identified as priority outcomes in vaccine safety studies by the Brighton Collaboration and World Health Organization. Outcomes reported directly in the clinical notes and outcomes which fulfilled GAIA-CDs were compared. Incidence of outcomes was calculated by combining cases which were either reported in clinical notes by attending physicians and/ or fulfilled GAIA-CDs. FINDINGS Of 9371 pregnant women enrolled, 27·6% were HIV-infected, 19·9% attended antenatal clinic in the 1st trimester of pregnancy and 55·3% had ≥1 ultrasound examination. Fourteen percent of women had hypertensive disease of pregnancy, 1·3% had gestational diabetes mellitus and 16% experienced preterm labour. There were 150 stillbirths (1·6%), 26·8% of infants were preterm and five percent had microcephaly. Data available in clinical notes for some adverse outcomes, including maternal- & neonatal death, severe pre-eclampsia/ eclampsia, were able to fulfil GAIA-CDs criteria for all of the clinically-reported cases, however, missing data required to fulfil other GAIA-CD criteria (including stillbirth, gestational diabetes mellitus and gestational hypertension) led to poor correlation between clinically-reported adverse outcomes and outcomes fulfilling GAIA-CDs. Challenges were also encountered in accurately ascertaining gestational age. INTERPRETATION This study contributes to the expanding body of data on background rates of adverse maternal and foetal/ neonatal outcomes in LMICs. Utilization of GAIA-CDs assists with alignment of data, however, some GAIA-CDs require amendment to improve the applicability in LMICs. FUNDING This study was funded by Pfizer (Inc).
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Affiliation(s)
- Clare L Cutland
- Wits African Leadership in Vaccinology Expertise (Wits-Alive), School of Pathology, Faculty of Health Science, University of the Witwatersrand, Johannesburg, South Africa; South African Medical Research Council Vaccines and Infectious Diseases Analytics Research Unit, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa; Department of Science/ National Research Foundation: Vaccine Preventable Diseases, University of the Witwatersrand, Faculty of Health Science, Johannesburg, South Africa.
| | - Shobna Sawry
- Wits RHI, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa.
| | - Lee Fairlie
- Wits RHI, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa.
| | - Shaun Barnabas
- Family Centre for Research with Ubuntu, Department of Paediatrics, University of Stellenbosch, Cape Town, South Africa.
| | | | - Jean Le Roux
- Wits RHI, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa.
| | - Alane Izu
- South African Medical Research Council Vaccines and Infectious Diseases Analytics Research Unit, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa; Department of Science/ National Research Foundation: Vaccine Preventable Diseases, University of the Witwatersrand, Faculty of Health Science, Johannesburg, South Africa.
| | - Kebonethebe Emmanuel Kekane-Mochwari
- South African Medical Research Council Vaccines and Infectious Diseases Analytics Research Unit, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa.
| | - Caroline Vika
- Wits RHI, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa.
| | - Jeanne De Jager
- Family Centre for Research with Ubuntu, Department of Paediatrics, University of Stellenbosch, Cape Town, South Africa.
| | - Samantha Munson
- Pfizer Vaccines Clinical Research & Development, Pfizer, Inc, Pearl River, New York, USA.
| | - Babalwa Jongihlati
- Pfizer Vaccines Clinical Research & Development, Pfizer, Inc, Pearl River, New York, USA.
| | - James H Stark
- Vaccines, Antivirals, and Evidence Generation, Pfizer Biopharma Group, 1 Portland St, Cambridge, MA, USA.
| | - Judith Absalon
- Pfizer Vaccines Clinical Research & Development, Pfizer, Inc, Pearl River, New York, USA.
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Bekele D, Gudu W, Wondafrash M, Abdosh AA, Sium AF. Utilization of third-trimester fetal transcerebellar diameter measurement for gestational age estimation: a comparative study using Bland-Altman analysis. AJOG GLOBAL REPORTS 2024; 4:100307. [PMID: 38304306 PMCID: PMC10832473 DOI: 10.1016/j.xagr.2024.100307] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/03/2024] Open
Abstract
BACKGROUND Several studies show that gestational age estimation during the third trimester of pregnancy using fetal transcerebellar diameter is superior to that measured using fetal biometry (biparietal diameter, head circumference, abdominal circumference, and femur diaphysis length). However, the conclusion of the studies stemmed from findings of correlation and regression statistical tests, which are not the recommended statistical analysis methods for comparing the values of 1 variable as measured by 2 different methods. OBJECTIVE This study aimed to compare the accuracy of gestational age estimation using transcerebellar diameter to that using fetal biometry during the third trimester of pregnancy using Bland-Altman statistical analysis. STUDY DESIGN This was a cross-sectional study on pregnant women who presented for routine antenatal care follow-up in the third trimester of pregnancy (28-41 weeks of gestation) at St. Paul's Hospital Millennium Medical College (Ethiopia) between November 1, 2020, and February 28, 2021. Data were collected prospectively using a structured questionnaire on the Open Data Kit. The primary outcome of our study was the mean bias of gestational age estimation (error in estimating gestational age) using transcerebellar diameter and composite fetal biometry (composite gestational age). Data were analyzed using Stata (version 15; StataCorp, College Station, TX). Simple descriptive analysis, Bland-Altman analysis, and the Kendall τa discordance measurement were performed as appropriate. The mean bias (error) and limits of agreement were used to present the significance of the finding. RESULTS A total of 104 pregnant women in the third trimester were included in the study. The mean error (bias) when transcerebellar diameter was used to estimate the gestational age was 0.65 weeks vs a bias of 1.1 weeks using composite biometry, compared with the gold standard method from crown-lump length (in both cases). The calculated estimated limit of agreement was narrower in the case of transcerebellar diameter than in the case of composite fetal biometry (-3.56 to 2.25 vs -4.73 to 2.53). The Kendall τa discordance measurement revealed that gestational age estimations using composite biometry and crown-lump length were 51% to 70%, respectively, more likely to agree than disagree and that gestational age estimations using transcerebellar diameter and crown-lump length were 62% to 77%, respectively, more likely to agree than to disagree (P≤.001). CONCLUSION Gestational age estimation using transcerebellar diameter is more accurate than gestational age estimation using composite gestational age (biparietal diameter, head circumference, femur diaphysis length, and abdominal circumference). Transcerebellar diameter should be used to date third-trimester pregnancies with unknown gestational age (unknown last normal menstrual period with no early ultrasound milestone).
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Affiliation(s)
- Delayehu Bekele
- Department of Obstetrics and Gynecology, St. Paul's Hospital Millennium Medical College, Addis Ababa, Ethiopia (Drs Bekele, Gudu, Abdosh, and Sium)
| | - Wondimu Gudu
- Department of Obstetrics and Gynecology, St. Paul's Hospital Millennium Medical College, Addis Ababa, Ethiopia (Drs Bekele, Gudu, Abdosh, and Sium)
| | - Mekitie Wondafrash
- St. Paul's Institute for Reproductive Health and Rights, Addis Ababa, Ethiopia (Dr Wondafrash)
| | - Abdulfetah Abdulkadir Abdosh
- Department of Obstetrics and Gynecology, St. Paul's Hospital Millennium Medical College, Addis Ababa, Ethiopia (Drs Bekele, Gudu, Abdosh, and Sium)
| | - Abraham Fessehaye Sium
- Department of Obstetrics and Gynecology, St. Paul's Hospital Millennium Medical College, Addis Ababa, Ethiopia (Drs Bekele, Gudu, Abdosh, and Sium)
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Khalil A, Sotiriadis A, D'Antonio F, Da Silva Costa F, Odibo A, Prefumo F, Papageorghiou AT, Salomon LJ. ISUOG Practice Guidelines: performance of third-trimester obstetric ultrasound scan. ULTRASOUND IN OBSTETRICS & GYNECOLOGY : THE OFFICIAL JOURNAL OF THE INTERNATIONAL SOCIETY OF ULTRASOUND IN OBSTETRICS AND GYNECOLOGY 2024; 63:131-147. [PMID: 38166001 DOI: 10.1002/uog.27538] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/03/2023] [Accepted: 11/03/2023] [Indexed: 01/04/2024]
Affiliation(s)
- A Khalil
- Fetal Medicine Unit, St George's Hospital, St George's University of London, London, UK
| | - A Sotiriadis
- Second Department of Obstetrics and Gynecology, Aristotle University of Thessaloniki, Faculty of Medicine, Thessaloniki, Greece
| | - F D'Antonio
- Centre for Fetal Care and High-Risk Pregnancy, University of Chieti, Chieti, Italy
| | - F Da Silva Costa
- Maternal Fetal Medicine Unit, Gold Coast University Hospital, and School of Medicine and Dentistry, Griffith University, Gold Coast, QLD, Australia
| | - A Odibo
- Obstetrics and Gynecology Department, Washington University School of Medicine in St. Louis, St. Louis, MO, USA
| | - F Prefumo
- Obstetrics and Gynecology Unit, IRCCS Istituto Giannina Gaslini, Genova, Italy
| | - A T Papageorghiou
- Fetal Medicine Unit, St George's Hospital, St George's University of London, London, UK; Nuffield Department for Women's and Reproductive Health, University of Oxford, Oxford, UK
| | - L J Salomon
- URP FETUS 7328 and LUMIERE platform, Maternité, Obstétrique, Médecine, Chirurgie et Imagerie Foetales, Hôpital Necker-Enfants Malades, Assistance Publique-Hôpitaux de Paris (AP-HP), Université de Paris, Paris, France
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Zhao H, Zheng Q, Teng C, Yasrab R, Drukker L, Papageorghiou AT, Noble JA. Memory-based unsupervised video clinical quality assessment with multi-modality data in fetal ultrasound. Med Image Anal 2023; 90:102977. [PMID: 37778101 DOI: 10.1016/j.media.2023.102977] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2022] [Revised: 08/03/2023] [Accepted: 09/18/2023] [Indexed: 10/03/2023]
Abstract
In obstetric sonography, the quality of acquisition of ultrasound scan video is crucial for accurate (manual or automated) biometric measurement and fetal health assessment. However, the nature of fetal ultrasound involves free-hand probe manipulation and this can make it challenging to capture high-quality videos for fetal biometry, especially for the less-experienced sonographer. Manually checking the quality of acquired videos would be time-consuming, subjective and requires a comprehensive understanding of fetal anatomy. Thus, it would be advantageous to develop an automatic quality assessment method to support video standardization and improve diagnostic accuracy of video-based analysis. In this paper, we propose a general and purely data-driven video-based quality assessment framework which directly learns a distinguishable feature representation from high-quality ultrasound videos alone, without anatomical annotations. Our solution effectively utilizes both spatial and temporal information of ultrasound videos. The spatio-temporal representation is learned by a bi-directional reconstruction between the video space and the feature space, enhanced by a key-query memory module proposed in the feature space. To further improve performance, two additional modalities are introduced in training which are the sonographer gaze and optical flow derived from the video. Two different clinical quality assessment tasks in fetal ultrasound are considered in our experiments, i.e., measurement of the fetal head circumference and cerebellar diameter; in both of these, low-quality videos are detected by the large reconstruction error in the feature space. Extensive experimental evaluation demonstrates the merits of our approach.
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Affiliation(s)
- He Zhao
- Institute of Biomedical Engineering, University of Oxford, United Kingdom.
| | - Qingqing Zheng
- Guangdong Provincial Key Laboratory of Computer Vision and Virtual Reality, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, China
| | - Clare Teng
- Institute of Biomedical Engineering, University of Oxford, United Kingdom
| | - Robail Yasrab
- Institute of Biomedical Engineering, University of Oxford, United Kingdom
| | - Lior Drukker
- Nuffield Department of Women's and Reproductive Health, University of Oxford, United Kingdom; Department of Obstetrics and Gynecology, Tel-Aviv University, Israel
| | - Aris T Papageorghiou
- Nuffield Department of Women's and Reproductive Health, University of Oxford, United Kingdom
| | - J Alison Noble
- Institute of Biomedical Engineering, University of Oxford, United Kingdom
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Nel S, Pattinson RC, Vannevel V, Feucht UD, Mulol H, Wenhold FAM. Integrated growth assessment in the first 1000 d of life: an interdisciplinary conceptual framework. Public Health Nutr 2023; 26:1523-1538. [PMID: 37170908 PMCID: PMC10410405 DOI: 10.1017/s1368980023000940] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2023] [Revised: 04/03/2023] [Accepted: 04/26/2023] [Indexed: 05/13/2023]
Abstract
OBJECTIVES Prenatal growth affects short- and long-term morbidity, mortality and growth, yet communication between prenatal and postnatal healthcare teams is often minimal. This paper aims to develop an integrated, interdisciplinary framework for foetal/infant growth assessment, contributing to the continuity of care across the first 1000 d of life. DESIGN A multidisciplinary think-tank met regularly over many months to share and debate their practice and research experience related to foetal/infant growth assessment. Participants’ personal practice and knowledge were verified against and supplemented by published research. SETTING Online and in-person brainstorming sessions of growth assessment practices that are feasible and valuable in resource-limited, low- and middle-income country (LMIC) settings. PARTICIPANTS A group of obstetricians, paediatricians, dietitians/nutritionists and a statistician. RESULTS Numerous measurements, indices and indicators were identified for growth assessment in the first 1000 d. Relationships between foetal, neonatal and infant measurements were elucidated and integrated into an interdisciplinary framework. Practices relevant to LMIC were then highlighted: antenatal Doppler screening, comprehensive and accurate birth anthropometry (including proportionality of weight, length and head circumference), placenta weighing and incorporation of length-for-age, weight-for-length and mid-upper arm circumference in routine growth monitoring. The need for appropriate, standardised clinical records and corresponding policies to guide clinical practice and facilitate interdisciplinary communication over time became apparent. CONCLUSIONS Clearer communication between prenatal, perinatal and postnatal health care providers, within the framework of a common understanding of growth assessment and a supportive policy environment, is a prerequisite to continuity of care and optimal health and development outcomes.
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Affiliation(s)
- Sanja Nel
- Department of Human Nutrition, University of Pretoria, Pretoria0002, South Africa
- Research Centre for Maternal, Fetal, Newborn & Child Health Care Strategies, University of Pretoria, Pretoria, South Africa
- Maternal and Infant Health Care Strategies Unit, South African Medical Research Council (SAMRC), Pretoria, South Africa
| | - Robert C Pattinson
- Research Centre for Maternal, Fetal, Newborn & Child Health Care Strategies, University of Pretoria, Pretoria, South Africa
- Maternal and Infant Health Care Strategies Unit, South African Medical Research Council (SAMRC), Pretoria, South Africa
- Department of Obstetrics and Gynaecology, University of Pretoria, Pretoria, South Africa
| | - Valerie Vannevel
- Research Centre for Maternal, Fetal, Newborn & Child Health Care Strategies, University of Pretoria, Pretoria, South Africa
- Maternal and Infant Health Care Strategies Unit, South African Medical Research Council (SAMRC), Pretoria, South Africa
- Department of Obstetrics and Gynaecology, University of Pretoria, Pretoria, South Africa
| | - Ute D Feucht
- Research Centre for Maternal, Fetal, Newborn & Child Health Care Strategies, University of Pretoria, Pretoria, South Africa
- Maternal and Infant Health Care Strategies Unit, South African Medical Research Council (SAMRC), Pretoria, South Africa
- Department of Paediatrics, University of Pretoria, Pretoria, South Africa
- Tshwane District Health Services, Gauteng Department of Health, Pretoria, South Africa
| | - Helen Mulol
- Research Centre for Maternal, Fetal, Newborn & Child Health Care Strategies, University of Pretoria, Pretoria, South Africa
- Maternal and Infant Health Care Strategies Unit, South African Medical Research Council (SAMRC), Pretoria, South Africa
- Department of Paediatrics, University of Pretoria, Pretoria, South Africa
| | - Friede AM Wenhold
- Department of Human Nutrition, University of Pretoria, Pretoria0002, South Africa
- Research Centre for Maternal, Fetal, Newborn & Child Health Care Strategies, University of Pretoria, Pretoria, South Africa
- Maternal and Infant Health Care Strategies Unit, South African Medical Research Council (SAMRC), Pretoria, South Africa
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Katebi N, Sameni R, Rohloff P, Clifford GD. Hierarchical Attentive Network for Gestational Age Estimation in Low-Resource Settings. IEEE J Biomed Health Inform 2023; 27:2501-2511. [PMID: 37027652 PMCID: PMC10482160 DOI: 10.1109/jbhi.2023.3246931] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/22/2023]
Abstract
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.
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Lee LH, Bradburn E, Craik R, Yaqub M, Norris SA, Ismail LC, Ohuma EO, Barros FC, Lambert A, Carvalho M, Jaffer YA, Gravett M, Purwar M, Wu Q, Bertino E, Munim S, Min AM, Bhutta Z, Villar J, Kennedy SH, Noble JA, Papageorghiou AT. Machine learning for accurate estimation of fetal gestational age based on ultrasound images. NPJ Digit Med 2023; 6:36. [PMID: 36894653 PMCID: PMC9998590 DOI: 10.1038/s41746-023-00774-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2022] [Accepted: 02/07/2023] [Indexed: 03/11/2023] Open
Abstract
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.
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Affiliation(s)
- Lok Hin Lee
- Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford, Oxford, UK
| | - Elizabeth Bradburn
- Nuffield Department of Women's & Reproductive Health, University of Oxford, Oxford, UK
| | - Rachel Craik
- Nuffield Department of Women's & Reproductive Health, University of Oxford, Oxford, UK
| | - Mohammad Yaqub
- Intelligent Ultrasound Ltd, Hodge House, Cardiff, CF10 1DY, UK
| | - Shane A Norris
- South African Medical Research Council Developmental Pathways for Health Research Unit, Department of Paediatrics & Child Health, University of the Witwatersrand, Johannesburg, South Africa
| | - Leila Cheikh Ismail
- College of Health Sciences, University of Sharjah, University City, United Arab Emirates
| | - Eric O Ohuma
- Nuffield Department of Women's & Reproductive Health, University of Oxford, Oxford, UK.,Maternal, Adolescent, Reproductive & Child Health (MARCH) Centre, London School of Hygiene & Tropical Medicine, London, UK
| | - Fernando C Barros
- Programa de Pós-Graduação em Epidemiologia, Universidade Federal de Pelotas, Pelotas, Brazil.,Programa de Pós-Graduação em Saúde e Comportamento, Universidade Católica de Pelotas, Pelotas, Brazil
| | - Ann Lambert
- Nuffield Department of Women's & Reproductive Health, University of Oxford, Oxford, UK
| | - Maria Carvalho
- Faculty of Health Sciences, Aga Khan University, Nairobi, Kenya
| | - Yasmin A Jaffer
- Department of Family & Community Health, Ministry of Health, Muscat, Oman
| | - Michael Gravett
- Departments of Obstetrics and Gynecology and of Global Health, University of Washington, Seattle, WA, USA
| | - Manorama Purwar
- Nagpur INTERGROWTH-21st Research Centre, Ketkar Hospital, Nagpur, India
| | - Qingqing Wu
- School of Public Health, Peking University, Beijing, China
| | - Enrico Bertino
- Dipartimento di Scienze Pediatriche e dell' Adolescenza, Struttura Complessa Direzione Universitaria Neonatologia, Università di Torino, Torino, Italy
| | - Shama Munim
- Department of Obstetrics & Gynaecology, Division of Women & Child Health, Aga Khan University, Karachi, Pakistan
| | - Aung Myat Min
- Shoklo Malaria Research Unit, Mahidol-Oxford Tropical Medicine Research Unit, Faculty of Tropical Medicine, Mahidol University, Mae Sot, Tak, Thailand
| | - Zulfiqar Bhutta
- Department of Obstetrics & Gynaecology, Division of Women & Child Health, Aga Khan University, Karachi, Pakistan.,Center for Global Child Health, Hospital for Sick Children, Toronto, Canada
| | - Jose Villar
- Nuffield Department of Women's & Reproductive Health, University of Oxford, Oxford, UK.,Oxford Maternal & Perinatal Health Institute, Green Templeton College, University of Oxford, Oxford, UK
| | - Stephen H Kennedy
- Nuffield Department of Women's & Reproductive Health, University of Oxford, Oxford, UK.,Oxford Maternal & Perinatal Health Institute, Green Templeton College, University of Oxford, Oxford, UK
| | - J Alison Noble
- Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford, Oxford, UK
| | - Aris T Papageorghiou
- Nuffield Department of Women's & Reproductive Health, University of Oxford, Oxford, UK. .,Oxford Maternal & Perinatal Health Institute, Green Templeton College, University of Oxford, Oxford, UK.
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