Literature DB >> 32197913

Aggressive Posterior Retinopathy of Prematurity: Clinical and Quantitative Imaging Features in a Large North American Cohort.

Kellyn N Bellsmith1, James Brown2, Sang Jin Kim3, Isaac H Goldstein1, Aaron Coyner4, Susan Ostmo1, Kishan Gupta1, R V Paul Chan5, Jayashree Kalpathy-Cramer6, Michael F Chiang7, J Peter Campbell8.   

Abstract

PURPOSE: Aggressive posterior retinopathy of prematurity (AP-ROP) is a vision-threatening disease with a significant rate of progression to retinal detachment. The purpose of this study was to characterize AP-ROP quantitatively by demographics, rate of disease progression, and a deep learning-based vascular severity score.
DESIGN: Retrospective analysis. PARTICIPANTS: The Imaging and Informatics in ROP cohort from 8 North American centers, consisting of 947 patients and 5945 clinical eye examinations with fundus images, was used. Pretreatment eyes were categorized by disease severity: none, mild, type 2 or pre-plus, treatment-requiring (TR) without AP-ROP, TR with AP-ROP. Analyses compared TR with AP-ROP and TR without AP-ROP to investigate differences between AP-ROP and other TR disease.
METHODS: A reference standard diagnosis was generated for each eye examination using previously published methods combining 3 independent image-based gradings and 1 ophthalmoscopic grading. All fundus images were analyzed using a previously published deep learning system and were assigned a score from 1 through 9. MAIN OUTCOME MEASURES: Birth weight, gestational age, postmenstrual age, and vascular severity score.
RESULTS: Infants who demonstrated AP-ROP were more premature by birth weight (617 g vs. 679 g; P = 0.01) and gestational age (24.3 weeks vs. 25.0 weeks; P < 0.01) and reached peak severity at an earlier postmenstrual age (34.7 weeks vs. 36.9 weeks; P < 0.001) compared with infants with TR without AP-ROP. The mean vascular severity score was greatest in TR with AP-ROP infants compared with TR without AP-ROP infants (8.79 vs. 7.19; P < 0.001). Analyzing the severity score over time, the rate of progression was fastest in infants with AP-ROP (P < 0.002 at 30-32 weeks).
CONCLUSIONS: Premature infants in North America with AP-ROP are born younger and demonstrate disease earlier than infants with less severe ROP. Disease severity is quantifiable with a deep learning-based score, which correlates with clinically identified categories of disease, including AP-ROP. The rate of progression to peak disease is greatest in eyes that demonstrate AP-ROP compared with other treatment-requiring eyes. Analysis of quantitative characteristics of AP-ROP may help improve diagnosis and treatment of an aggressive, vision-threatening form of ROP.
Copyright © 2020 American Academy of Ophthalmology. Published by Elsevier Inc. All rights reserved.

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Year:  2020        PMID: 32197913      PMCID: PMC7384953          DOI: 10.1016/j.ophtha.2020.01.052

Source DB:  PubMed          Journal:  Ophthalmology        ISSN: 0161-6420            Impact factor:   12.079


  31 in total

Review 1.  The International Classification of Retinopathy of Prematurity revisited.

Authors: 
Journal:  Arch Ophthalmol       Date:  2005-07

2.  Retinopathy of prematurity: two distinct mechanisms that underlie zone 1 and zone 2 disease.

Authors:  John T Flynn; Tailoi Chan-Ling
Journal:  Am J Ophthalmol       Date:  2006-07       Impact factor: 5.258

3.  Expert Diagnosis of Plus Disease in Retinopathy of Prematurity From Computer-Based Image Analysis.

Authors:  J Peter Campbell; Esra Ataer-Cansizoglu; Veronica Bolon-Canedo; Alican Bozkurt; Deniz Erdogmus; Jayashree Kalpathy-Cramer; Samir N Patel; James D Reynolds; Jason Horowitz; Kelly Hutcheson; Michael Shapiro; Michael X Repka; Phillip Ferrone; Kimberly Drenser; Maria Ana Martinez-Castellanos; Susan Ostmo; Karyn Jonas; R V Paul Chan; Michael F Chiang
Journal:  JAMA Ophthalmol       Date:  2016-06-01       Impact factor: 7.389

4.  Aggressive posterior retinopathy of prematurity in large preterm babies in South India.

Authors:  Parag K Shah; Venkatapathy Narendran; Narendran Kalpana
Journal:  Arch Dis Child Fetal Neonatal Ed       Date:  2012-05-18       Impact factor: 5.747

5.  Diagnostic Discrepancies in Retinopathy of Prematurity Classification.

Authors:  J Peter Campbell; Michael C Ryan; Emily Lore; Peng Tian; Susan Ostmo; Karyn Jonas; R V Paul Chan; Michael F Chiang
Journal:  Ophthalmology       Date:  2016-05-27       Impact factor: 12.079

6.  Evidence-based screening criteria for retinopathy of prematurity: natural history data from the CRYO-ROP and LIGHT-ROP studies.

Authors:  James D Reynolds; Velma Dobson; Graham E Quinn; Alistair R Fielder; Earl A Palmer; Richard A Saunders; Robert J Hardy; Dale L Phelps; John D Baker; Michael T Trese; David Schaffer; Betty Tung
Journal:  Arch Ophthalmol       Date:  2002-11

7.  Plus Disease in Retinopathy of Prematurity: Improving Diagnosis by Ranking Disease Severity and Using Quantitative Image Analysis.

Authors:  Jayashree Kalpathy-Cramer; J Peter Campbell; Deniz Erdogmus; Peng Tian; Dharanish Kedarisetti; Chace Moleta; James D Reynolds; Kelly Hutcheson; Michael J Shapiro; Michael X Repka; Philip Ferrone; Kimberly Drenser; Jason Horowitz; Kemal Sonmez; Ryan Swan; Susan Ostmo; Karyn E Jonas; R V Paul Chan; Michael F Chiang
Journal:  Ophthalmology       Date:  2016-08-24       Impact factor: 12.079

Review 8.  Retinopathy of prematurity: a global perspective of the epidemics, population of babies at risk and implications for control.

Authors:  Clare Gilbert
Journal:  Early Hum Dev       Date:  2008-01-29       Impact factor: 2.079

9.  A Quantitative Severity Scale for Retinopathy of Prematurity Using Deep Learning to Monitor Disease Regression After Treatment.

Authors:  Kishan Gupta; J Peter Campbell; Stanford Taylor; James M Brown; Susan Ostmo; R V Paul Chan; Jennifer Dy; Deniz Erdogmus; Stratis Ioannidis; Jayashree Kalpathy-Cramer; Sang J Kim; Michael F Chiang
Journal:  JAMA Ophthalmol       Date:  2019-07-03       Impact factor: 7.389

10.  Outcomes and prognostic factors for aggressive posterior retinopathy of prematurity following initial treatment with intravitreal ranibizumab.

Authors:  Qizhe Tong; Hong Yin; Mingwei Zhao; Xiaoxin Li; Wenzhen Yu
Journal:  BMC Ophthalmol       Date:  2018-06-26       Impact factor: 2.209

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  9 in total

1.  Efficacy evaluation of intravitreal ranibizumab therapy for three types of retinopathy of prematurity.

Authors:  Qiong Zou; Yan-Qiong Zhu; Feng-Jun Zhang; Qiu-Ping Liu
Journal:  Int J Ophthalmol       Date:  2022-05-18       Impact factor: 1.779

2.  Federated Learning for Multicenter Collaboration in Ophthalmology: Implications for Clinical Diagnosis and Disease Epidemiology.

Authors:  Adam Hanif; Charles Lu; Ken Chang; Praveer Singh; Aaron S Coyner; James M Brown; Susan Ostmo; Robison V Paul Chan; Daniel Rubin; Michael F Chiang; Jayashree Kalpathy-Cramer; John Peter Campbell
Journal:  Ophthalmol Retina       Date:  2022-03-16

3.  Artificial Intelligence for Retinopathy of Prematurity: Validation of a Vascular Severity Scale against International Expert Diagnosis.

Authors:  J Peter Campbell; Michael F Chiang; Jimmy S Chen; Darius M Moshfeghi; Eric Nudleman; Paisan Ruambivoonsuk; Hunter Cherwek; Carol Y Cheung; Praveer Singh; Jayashree Kalpathy-Cramer; Susan Ostmo; Malvina Eydelman; R V Paul Chan; Antonio Capone
Journal:  Ophthalmology       Date:  2022-02-12       Impact factor: 14.277

4.  Single-Examination Risk Prediction of Severe Retinopathy of Prematurity.

Authors:  Aaron S Coyner; Jimmy S Chen; Praveer Singh; Robert L Schelonka; Brian K Jordan; Cindy T McEvoy; Jamie E Anderson; R V Paul Chan; Kemal Sonmez; Deniz Erdogmus; Michael F Chiang; Jayashree Kalpathy-Cramer; J Peter Campbell
Journal:  Pediatrics       Date:  2021-12-01       Impact factor: 9.703

Review 5.  Artificial intelligence for retinopathy of prematurity.

Authors:  Rebekah H Gensure; Michael F Chiang; John P Campbell
Journal:  Curr Opin Ophthalmol       Date:  2020-09       Impact factor: 3.761

6.  Applications of Artificial Intelligence for Retinopathy of Prematurity Screening.

Authors:  J Peter Campbell; Praveer Singh; Travis K Redd; James M Brown; Parag K Shah; Prema Subramanian; Renu Rajan; Nita Valikodath; Emily Cole; Susan Ostmo; R V Paul Chan; Narendran Venkatapathy; Michael F Chiang; Jayashree Kalpathy-Cramer
Journal:  Pediatrics       Date:  2021-03       Impact factor: 7.124

7.  Translatability Analysis of National Institutes of Health-Funded Biomedical Research That Applies Artificial Intelligence.

Authors:  Feyisope R Eweje; Suzie Byun; Rajat Chandra; Fengling Hu; Ihab Kamel; Paul Zhang; Zhicheng Jiao; Harrison X Bai
Journal:  JAMA Netw Open       Date:  2022-01-04

8.  Commentary: Deep learning in retinopathy of prematurity: Where do we stand?

Authors:  Parveen Sen; Arjun Bamel
Journal:  Indian J Ophthalmol       Date:  2022-04       Impact factor: 2.969

9.  Evaluation of a Deep Learning-Derived Quantitative Retinopathy of Prematurity Severity Scale.

Authors:  J Peter Campbell; Sang Jin Kim; James M Brown; Susan Ostmo; R V Paul Chan; Jayashree Kalpathy-Cramer; Michael F Chiang
Journal:  Ophthalmology       Date:  2020-10-27       Impact factor: 14.277

  9 in total

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