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Public Health Genomics and Precision Health Knowledge Base (v9.0)
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Precision Health Database|Search|Public Health Genomics and Precision Health Knowledge Base (PHGKB)
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Last data update: Apr 25, 2024
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Image analysis-based machine learning for the diagnosis of retinopathy of prematurity: A meta-analysis and systematic review.
Yihang Chu et al. Ophthalmol Retina 2024
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The past, current, and future of neonatal intensive care units with artificial intelligence: a systematic review.
Elif Keles et al. NPJ Digit Med 2023 6(1) 220
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Epidemiologic evaluation of retinopathy of prematurity severity in a large telemedicine program in India using artificial intelligence.
Mallory A deCampos-Stairiker et al. Ophthalmology 2023
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Electronic medical records driven big data analytics in retinal diseases, report number 1: non-oncological retinal diseases in children and adolescents in India.
Komal Agarwal et al. International ophthalmology 2023
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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
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Artificial intelligence for the diagnosis of retinopathy of prematurity: A systematic review of current algorithms.
Ramanathan Ashwin et al. Eye (London, England) 2022
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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
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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
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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
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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
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Predicting clinical outcomes using artificial intelligence and machine learning in neonatal intensive care units: a systematic review.
McAdams Ryan M et al. Journal of perinatology : official journal of the California Perinatal Association 2022
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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
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Evaluation of artificial intelligence-based quantitative analysis to identify clinically significant severe retinopathy of prematurity.
Li Jie et al. Retina (Philadelphia, Pa.) 2021
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Applications of Artificial Intelligence for Retinopathy of Prematurity Screening.
Campbell J Peter et al. Pediatrics 2021
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Current Application of Digital Diagnosing Systems for Retinopathy of Prematurity.
Bao Yuekun et al. Computer methods and programs in biomedicine 2020 Nov 105871
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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
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Evaluation of a novel retinopathy of prematurity severity scale applied by clinicians and deep learning.
Campbell J Peter et al. Ophthalmology 2020 Oct
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Artificial Intelligence in Retinopathy of Prematurity Diagnosis.
Scruggs Brittni A et al. Translational vision science & technology 2020 Feb 9(2) 5
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Artificial intelligence for retinopathy of prematurity.
Gensure Rebekah H et al. Current opinion in ophthalmology 2020 Jul
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Application of Artificial Intelligence in Targeting Retinal Diseases.
Sorrentino Francesco Saverio et al. Current drug targets 2020 Jul
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Insights into the growing popularity of artificial intelligence in ophthalmology.
Dutt Sreetama et al. Indian journal of ophthalmology 2020 Jul 68(7) 1339-1346
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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
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Evaluation of artificial intelligence-based telemedicine screening for retinopathy of prematurity.
Greenwald Miles F et al. Journal of AAPOS : the official publication of the American Association for Pediatric Ophthalmology and Strabismus 2020 Apr
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Siamese neural networks for continuous disease severity evaluation and change detection in medical imaging.
Li Matthew D et al. NPJ digital medicine 2020 348
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Practice Guidelines for Ocular Telehealth-Diabetic Retinopathy, Third Edition.
Horton Mark B et al. Telemedicine journal and e-health : the official journal of the American Telemedicine Association 2020 Mar
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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
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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
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Automated Algorithms and Retinopathy of Prematurity Treatment.
Good William V, et al. JAMA ophthalmology 2019 7 0.
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The role of retinal photography and telemedicine in ROP screening.
Quinn Graham E et al. Seminars in perinatology 2019 May
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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
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Page last reviewed:
Feb 1, 2024
Page last updated:
Apr 25, 2024
Content source:
Public Health Genomics Branch in the Division of Blood Disorders and Public Health Genomics
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National Center on Birth Defects and Developmental Disabilities
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