Last Posted: Jan 24, 2023
- 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
- Artificial intelligence for the diagnosis of retinopathy of prematurity: A systematic review of current algorithms.
Ramanathan Ashwin et al. Eye (London, England) 2022
- 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
- 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
- Inborn Errors of Immunity With Fetal or Perinatal Clinical Manifestations.
Carneiro-Sampaio Magda et al. Frontiers in pediatrics 2022 10891343
- 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
- Ethical Considerations for Equitable Access to Genomic Sequencing for Critically Ill Neonates in the United States.
P Fishler Kristen et al. International journal of neonatal screening 2022 8(1)
- 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
- Ethics of Artificial Intelligence in Medicine and Ophthalmology.
Abdullah Yasser Ibraheem et al. Asia-Pacific journal of ophthalmology (Philadelphia, Pa.) 2021 10(3) 289-298
- 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
- Recommendations for neonatologists and pediatricians working in first level birthing centers on the first communication of genetic disease and malformation syndrome diagnosis: consensus issued by 6 Italian scientific societies and 4 parents' associations.
Serra Gregorio et al. Italian journal of pediatrics 2021 47(1) 94
- 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
- Current Application of Digital Diagnosing Systems for Retinopathy of Prematurity.
Bao Yuekun et al. Computer methods and programs in biomedicine 2020 Nov 105871
- 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)
- Diving Deep into Deep Learning: An Update on Artificial Intelligence in Retina.
Goldhagen Brian E et al. Current ophthalmology reports 2020 Sep 8(3) 121-128
- 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)
- Toward the Clinical Application of Therapeutic Angiogenesis Against Pediatric Ischemic Retinopathy.
Jo Dong Hyun et al. Journal of lipid and atherosclerosis 2020 May 9(2) 268-282
- Artificial intelligence for retinopathy of prematurity.
Gensure Rebekah H et al. Current opinion in ophthalmology 2020 Jul
- Application of Artificial Intelligence in Targeting Retinal Diseases.
Sorrentino Francesco Saverio et al. Current drug targets 2020 Jul
- 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
Reproductive and Child Health PHGKB is an online, continuously updated, searchable database of published scientific literature, CDC and NIH resources, and other materials that address the translation of genomic and other precision health discoveries into improved health care and prevention related to reproductive and child Health...more
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Disclaimer: Articles listed in the Public Health Knowledge Base are selected by the CDC Office of Public Health Genomics to provide current awareness of the literature and news. Inclusion in the update does not necessarily represent the views of the Centers for Disease Control and Prevention nor does it imply endorsement of the article's methods or findings. CDC and DHHS assume no responsibility for the factual accuracy of the items presented. The selection, omission, or content of items does not imply any endorsement or other position taken by CDC or DHHS. Opinion, findings and conclusions expressed by the original authors of items included in the update, or persons quoted therein, are strictly their own and are in no way meant to represent the opinion or views of CDC or DHHS. References to publications, news sources, and non-CDC Websites are provided solely for informational purposes and do not imply endorsement by CDC or DHHS.