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Postnatal gestational age estimation of newborns using Small Sample Deep Learning

Mercedes Torres Torres, Michel Valstar, Caroline Henry, Carole Ward, Don Sharkey
2019 Image and Vision Computing  
Our main contribution is a novel system for automatic postnatal gestational age estimation using small sets of images of a newborn's face, foot and ear.  ...  This indicates that even with a very small set of data, our method is a viable candidate for postnatal gestational age estimation in areas were USS is not available.  ...  Small Sample Learning Our Small Sample Deep Learning method presented in this paper can be divided into two stages: 1.  ... 
doi:10.1016/j.imavis.2018.09.003 pmid:31762527 pmcid:PMC6859867 fatcat:3eqiak7b2bgw3g5srf52ftqzbe

Small Sample Deep Learning for Newborn Gestational Age Estimation

Mercedes Torres Torres, Michel F. Valstar, Caroline Henry, Carole Ward, Don Sharkey
2017 2017 12th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2017)  
In this paper, we present an automatic system for postnatal gestational age estimation aimed to be deployed on mobile phones, using small sets of images of a newborn's face, foot and ear.  ...  This indicates that even with a very small set of data, our method is a viable candidate for postnatal gestational age estimation in areas were USS is not available. 978-1-5090-4023-0/17/$31.00 c 2017  ...  Training of our Small Sample Deep Learning structure consists of two phases: 1.  ... 
doi:10.1109/fg.2017.19 dblp:conf/fgr/TorresVHWS17 fatcat:ljalfo533vebbajqm4fcyccptu

Postnatal gestational age estimation via newborn screening analysis: application and potential

Lindsay A. Wilson, Malia SQ. Murphy, Robin Ducharme, Kathryn Denize, Nafisa M. Jadavji, Beth Potter, Julian Little, Pranesh Chakraborty, Steven Hawken, Kumanan Wilson
2019 Espert Review of Proteomics  
Given the importance of accurately ascertaining estimates of preterm birth and in light of current limitations in postnatal gestational age (GA) estimation, novel methods of estimating GA postnatally in  ...  Areas covered: Circulating analytes found in newborn blood samples vary by GA.  ...  We are also exploring state-of-the-art artificial intelligence (AI) modeling approaches such as deep learning neural networks (DLNN).  ... 
doi:10.1080/14789450.2019.1654863 pmid:31422714 pmcid:PMC6816481 fatcat:pb5kq4heanbglpuckvotngbgky

External validation of machine learning models including newborn metabolomic markers for postnatal gestational age estimation in East and South-East Asian infants [version 2; peer review: 1 approved, 3 approved with reservations]

Steven Hawken, Malia S. Q. Murphy, Robin Ducharme, A. Brianne Bota, Lindsay A. Wilson, Wei Cheng, Ma-Am Joy Tumulak, Maria Melanie Liberty Alcausin, Ma Elouisa Reyes, Wenjuan Qiu, Beth K. Potter, Julian Little (+5 others)
2021 Gates Open Research  
Postnatal gestational age (GA) algorithms derived from newborn metabolic profiles have emerged as a novel method of acquiring population-level preterm birth estimates in low resource settings.  ...  ELASTICNET machine learning models were developed to estimate GA in a cohort of infants from Canada using sex, birth weight and metabolomic markers from newborn heel prick blood samples.  ...  Development and external validation of machine learning algorithms for postnatal gestational age estimation using clinical data and metabolomic markers.  ... 
doi:10.12688/gatesopenres.13131.2 doaj:8ac7beff887d4ca1a9b9042acee8afee fatcat:r4vqixotknfbha4fj4mc25vpua

Deep clinical and biological phenotyping of the preterm birth and small for gestational age syndromes: The INTERBIO-21st Newborn Case-Control Study protocol

Stephen H. Kennedy, Cesar G. Victora, Rachel Craik, Stephen Ash, Fernando C. Barros, Hellen C. Barsosio, James A. Berkley, Maria Carvalho, Michelle Fernandes, Leila Cheikh Ismail, Ann Lambert, Cecilia M. Lindgren (+19 others)
2018 Gates Open Research  
age and their interactions, using deep phenotyping of clinical, growth and epidemiological data and associated nutritional, biochemical, omic and histological profiles.  ...  Methods: In the INTERBIO-21st Newborn Case-Control Study, a major component of Phase II, our objective is to investigate the mechanisms potentially responsible for preterm birth and small for gestational  ...  We are indebted to GAPPS for the supply of sample processing kits.  ... 
doi:10.12688/gatesopenres.12869.1 fatcat:ome6wk7v5retdi5o3s4whnire4

Open-source, machine and deep learning-based automated algorithm for gestational age estimation through smartphone lens imaging

Arjun D. Desai, Chunlei Peng, Leyuan Fang, Dibyendu Mukherjee, Andrew Yeung, Stephanie J. Jaffe, Jennifer B. Griffin, Sina Farsiu
2018 Biomedical Optics Express  
Gestational age estimation at time of birth is critical for determining the degree of prematurity of the infant and for administering appropriate postnatal treatment.  ...  The algorithm is expected to be an influential tool for remote and point-of-care gestational age estimation of premature neonates in low-income countries.  ...  Parallel to our clinical trial, a recent deep learning-based method has utilized images of newborns' faces, feet, and ears to determine gestational age [41] .  ... 
doi:10.1364/boe.9.006038 pmid:31065411 pmcid:PMC6491013 fatcat:7rpjz35z4bgfvdwbwptk3ynyte

Survival and major neurodevelopmental impairment in extremely low gestational age newborns born 1990–2000: a retrospective cohort study

Lisa K Washburn, Robert G Dillard, Donald J Goldstein, Kurt L Klinepeter, Raye-Ann deRegnier, Thomas Michael O'Shea
2007 BMC Pediatrics  
It is important to determine if rates of survival and major neurodevelopmental impairment in extremely low gestational age newborns (ELGANs; infants born at 23-27 weeks gestation) are changing over time  ...  Outcomes at one year adjusted age were compared for two epochs of birth: epoch  ...  Acknowledgements This project was supported by the North Carolina Department of Health and Human Services and the Brenner Center for Child and Adolescent Health.  ... 
doi:10.1186/1471-2431-7-20 pmid:17477872 pmcid:PMC1876228 fatcat:eamvuv35qbcz3h7srlh535t77u

Estimation of Gestational Age via Image Analysis of Anterior Lens Capsule Vascularity in Preterm Infants: A Pilot Study

Monalisa Patel, Dibyendu Mukherjee, Sina Farsiu, Breda Munoz, Arlin B. Blood, Christopher G. Wilson, Jennifer B. Griffin
2019 Frontiers in Pediatrics  
ALCV estimates of gestational age were within 0.11 ± 1.3 weeks of ultrasound estimates.  ...  Conclusions: This novel application of smartphone ophthalmoscopy and ALCV image analysis may provide a safe, accurate and non-invasive technology to estimate postnatal gestational age, especially in low  ...  This study had a larger sample size, with greater statistical power, and will allow for machine learning approaches to improve the prediction of gestational age.  ... 
doi:10.3389/fped.2019.00043 pmid:30842940 pmcid:PMC6391335 fatcat:lzm4e4imfjbkzbhrm6vwtxxosq

Deep clinical and biological phenotyping of the preterm birth and small for gestational age syndromes: The INTERBIO-21st Newborn Case-Control Study protocol

Stephen H. Kennedy, Cesar G. Victora, Rachel Craik, Stephen Ash, Fernando C. Barros, Hellen C. Barsosio, James A. Berkley, Maria Carvalho, Michelle Fernandes, Leila Cheikh Ismail, Ann Lambert, Cecilia M. Lindgren (+19 others)
2019 Gates Open Research  
age and their interactions, using deep phenotyping of clinical, growth and epidemiological data and associated nutritional, biochemical, omic and histological profiles.  ...  Methods: In the INTERBIO-21st Newborn Case-Control Study, a major component of Phase II, our objective is to investigate the mechanisms potentially responsible for preterm birth and small for gestational  ...  We are indebted to GAPPS for the supply of sample processing kits.  ... 
doi:10.12688/gatesopenres.12869.2 pmid:31172050 pmcid:PMC6545521 fatcat:d4ydkaafnvfjzpaf76inn5eaim

A maChine and deep Learning Approach to predict pulmoNary hyperteNsIon in newbornS with congenital diaphragmatic Hernia (CLANNISH): Protocol for a retrospective study

Ilaria Amodeo, Giorgio De Nunzio, Genny Raffaeli, Irene Borzani, Alice Griggio, Luana Conte, Francesco Macchini, Valentina Condò, Nicola Persico, Isabella Fabietti, Stefano Ghirardello, Maria Pierro (+7 others)
2021 PLoS ONE  
We propose applying Machine Learning (ML), and Deep Learning (DL) approaches to fetuses and newborns with CDH to develop forecasting models in prenatal epoch, based on the integrated analysis of clinical  ...  Introduction Outcome predictions of patients with congenital diaphragmatic hernia (CDH) still have some limitations in the prenatal estimate of postnatal pulmonary hypertension (PH).  ...  of the Department of Pediatric Surgery, anesthesiologists of the Pediatric Anesthesiology and Intensive Care Unit, nurses of the operating room.  ... 
doi:10.1371/journal.pone.0259724 pmid:34752491 pmcid:PMC8577746 fatcat:jeidt5mt6rgcrhz7bs2notii6y

Brain age predicted using graph convolutional neural network explains developmental trajectory in preterm neonates [article]

Mengting Liu, Sharon Kim, Ben A. Duffy, Shiyu Yuan, James H. Cole, Arthur W. Toga, Neda Jahanshad, Anthony James Barkovich, Duan Xu, Hosung Kim
2021 bioRxiv   pre-print
Our findings demonstrate that GCN-based age prediction of preterm neonates (n=170; mean absolute error [MAE]: 1.06 weeks) outperformed conventional machine learning algorithms and deep learning methods  ...  Dramatic alterations in brain morphology, such as cortical thickness and sulcal folding, occur during the 3rd trimester of gestation which overlaps with the period of premature births.  ...  To study brain aging using PBA, studies to date particularly focused on using machine learning and deep learning (DL) methods (LeCun et al., 2015; Peng et al., 2019) that learn important features without  ... 
doi:10.1101/2021.05.15.444320 fatcat:p2hhvgtyp5fejgqnagphfukvzm

Prenatal depression effects on the fetus and newborn: a review

Tiffany Field, Miguel Diego, Maria Hernandez-Reif
2006 Infant Behavior and Development  
A review of research on prenatal depression effects on the fetus and newborn suggests that they experience prenatal, perinatal and postnatal complications.  ...  Newborns of depressed mothers then show a biochemical/physiological profile that mimics their mothers' prenatal biochemical/physiological profile including elevated cortisol, lower levels of dopamine and  ...  age at birth and gestational age when fetal weight was estimated.  ... 
doi:10.1016/j.infbeh.2006.03.003 pmid:17138297 fatcat:ezwf5ndf65buje24s2gjsbmsoy

Neonatal sepsis and simple minor neurological dysfunction

Nazan Kavas, Ayşe Engin Arısoy, Asuman Bayhan, Bülent Kara, Ayla Günlemez, Gülcan Türker, Meral Oruç, Ayşe Sevim Gökalp
2017 Pediatrics International  
of cerebral palsy (CP). 5 However, despite limited data, preliminary evidence suggests that premature newborns are at an increased risk of MND in comparison with age-matched term newborns. 6,7 An increased  ...  This study examined potential risk factors for and consequences of simple minor neurological dysfunction (SMND), in a group of very low birth weight newborns followed until preschool age.  ...  Acknowledgement We would like to kindly express our deep thanks to all participant children and their parents. Disclosure The authors declare no conflict of interest and have nothing to disclose.  ... 
doi:10.1111/ped.13217 pmid:27935218 fatcat:wds4uarvqrgnhbbia2t262bq54

Validity of Newborn Clinical Assessment to Determine Gestational Age in Bangladesh

A. C. Lee, L. C. Mullany, K. Ladhani, J. Uddin, D. Mitra, P. Ahmed, P. Christian, A. Labrique, S. K. DasGupta, R. P. Lokken, M. Quaiyum, A. H. Baqui
2016 Pediatrics  
BACKGROUND: Gestational age (GA) is frequently unknown or inaccurate in pregnancies in lowincome countries.  ...  CONCLUSIONS: Newborn clinical assessment of GA is challenging at the community level in low-resource settings.  ...  ACKNOWLEDGMENTS We thank all the staff of the Projahnmo team with special thanks to those who implemented this  ... 
doi:10.1542/peds.2015-3303 pmid:27313070 pmcid:PMC4925072 fatcat:j62ohdvedbeghmeny2biils6xu

Simplified models to assess newborn gestational age in low-middle income countries: findings from a multicountry, prospective cohort study

The Alliance for Maternal and Newborn Health Improvement (AMANHI) Gestational Age Study Group
2021 BMJ Global Health  
This study aimed to develop and validate programmatically feasible and accurate approaches to estimate newborn gestational age (GA) in low resource settings.MethodsThe WHO Alliance for Maternal and Newborn  ...  Machine-learning techniques were used to construct ensemble models.  ...  Acknowledgements We would like to thank all of the pregnant women and infants who participated in the study. We acknowledge all research field teams and communities at each site.  ... 
doi:10.1136/bmjgh-2021-005688 pmid:34518201 fatcat:hxxv32733ng7ninajsdzbvz2iy
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