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Precision Health Database


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Reproductive and Child Health

Last data update: Feb 05, 2023. (Total: 52479 Documents since 2012)
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Query Trace: Prematurity[original query]>>Original Studies[Product Type]
Assessment of Retinopathy of Prematurity Regression and Reactivation Using an Artificial Intelligence-Based Vascular Severity Score.
Eilts Sonja K et al. JAMA network open 2023 6(1) e2251512
Evaluation of an Artificial Intelligence System for Retinopathy of Prematurity Screening in Nepal and Mongolia.
Cole Emily et al. Ophthalmology science 2022 2(4) 100165
Clinical and economic evaluation of a proteomic biomarker preterm birth risk predictor: Cost-effectiveness modeling of prenatal interventions applied to predicted higher-risk pregnancies within a large and diverse cohort.
Burchard Julja et al. Journal of medical economics 2022 1-51
Improved Training Efficiency for Retinopathy of Prematurity Deep Learning Models Using Comparison versus Class Labels.
Hanif Adam et al. Ophthalmology science 2022 2(2) 100122
Disparities in first evaluation of infants with cystic fibrosis since implementation of newborn screening.
McColley Susanna A et al. Journal of cystic fibrosis : official journal of the European Cystic Fibrosis Society 2022
External Validation of a Retinopathy of Prematurity Screening Model Using Artificial Intelligence in 3 Low- and Middle-Income Populations.
Coyner Aaron S et al. JAMA ophthalmology 2022
Early Diagnosis and Quantitative Analysis of Stages in Retinopathy of Prematurity Based on Deep Convolutional Neural Networks.
Li Peng et al. Translational vision science & technology 2022 11(5) 17
Increased referrals for congenital hyperinsulinism genetic testing in children with trisomy 21 reflects the high burden of non-genetic risk factors in this group.
Hewat Thomas I et al. Pediatric diabetes 2022
Implementation of Universal Newborn Screening for Severe Combined Immunodeficiency in Singapore While Continuing Routine Bacille-Calmette-Guerin Vaccination Given at Birth.
Chan Su-Wan Bianca et al. Frontiers in immunology 2022 12794221
Comparison of Multivariable Logistic Regression and Machine Learning Models for Predicting Bronchopulmonary Dysplasia or Death in Very Preterm Infants.
Khurshid Faiza et al. Frontiers in pediatrics 2021 9759776
Developing practical clinical tools for predicting neonatal mortality at a neonatal intensive care unit in Tanzania.
Kovacs Dory et al. BMC pediatrics 2021 21(1) 537
Evaluation of artificial intelligence-based quantitative analysis to identify clinically significant severe retinopathy of prematurity.
Li Jie et al. Retina (Philadelphia, Pa.) 2021
Neonatal cystic fibrosis screening: Analysis and differences in immunoreactive trypsin levels in newborns with a positive screen.
Arrudi-Moreno Marta et al. Anales de pediatria 2021
Machine Learning Assessment of Early Life Factors Predicting Suicide Attempt in Adolescence or Young Adulthood.
Navarro Marie C et al. JAMA network open 2021 4(3) e211450
Applications of Artificial Intelligence for Retinopathy of Prematurity Screening.
Campbell J Peter et al. Pediatrics 2021
Follow-Up for an Abnormal Newborn Screen for Severe Combined Immunodeficiencies (NBS SCID): A Clinical Immunology Society (CIS) Survey of Current Practices.
Knight Vijaya et al. International journal of neonatal screening 2020 Jun 6(3)
Evaluation of a novel retinopathy of prematurity severity scale applied by clinicians and deep learning.
Campbell J Peter et al. Ophthalmology 2020 Oct
Initial Evaluation of Prospective and Parallel Assessments of Cystic Fibrosis Newborn Screening Protocols in Eastern Andalusia: IRT/IRT versus IRT/PAP/IRT.
Sadik Ilham et al. International journal of neonatal screening 2019 Sep 5(3) 32
[Neonatal cystic fibrosis screening: Analysis and differences in immunoreactive trypsin levels in newborns with a positive screen].
Arrudi-Moreno Marta et al. Anales de pediatria (Barcelona, Spain : 2003) 2020 Oct
Newborn Screening Protocols and Positive Predictive Value for Congenital Adrenal Hyperplasia Vary across the United States.
Speiser Phyllis W et al. International journal of neonatal screening 2020 Jun 6(2)
Evaluation of deep convolutional neural networks for automatic classification of common maternal fetal ultrasound planes.
Burgos-Artizzu Xavier P et al. Scientific reports 2020 Jun 10(1) 10200
Real world variability in plus disease identified using a deep learning-based retinopathy of prematurity severity scale.
Choi Rene Y et al. Ophthalmology. Retina 2020 May
Siamese neural networks for continuous disease severity evaluation and change detection in medical imaging.
Li Matthew D et al. NPJ digital medicine 2020 348
Automated Fundus Image Quality Assessment in Retinopathy of Prematurity Using Deep Convolutional Neural Networks.
Coyner Aaron S et al. Ophthalmology. Retina 2019 3(5) 444-450
Deep Learning Predicts Extreme Preterm Birth from Electronic Health Records.
Gao Cheng et al. Journal of biomedical informatics 2019 Oct 103334
Automated diagnosis and quantitative analysis of plus disease in retinopathy of prematurity based on deep convolutional neural networks.
Mao Jianbo et al. Acta ophthalmologica 2019 Sep
Exome sequencing identifies gene variants and networks associated with extreme respiratory outcomes following preterm birth.
Hamvas Aaron et al. BMC genetics 2018 19(1) 94
Evaluation of a Remote Telemedicine Screening System for Severe Retinopathy of Prematurity.
Begley Brett A et al. Journal of pediatric ophthalmology and strabismus 2019 May 56(3) 157-161
Using Mobile Health to Support Clinical Decision-Making to Improve Maternal and Neonatal Health Outcomes in Ghana: Insights of Frontline Health Worker Information Needs.
Amoakoh Hannah Brown et al. JMIR mHealth and uHealth 2019 May 7(5) e12879
Evaluation of the Dutch neonatal screening for congenital adrenal hyperplasia.
van der Linde Annelieke A A et al. Archives of disease in childhood 2019 Feb
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