Literature DB >> 31268518

Monitoring Disease Progression With a Quantitative Severity Scale for Retinopathy of Prematurity Using Deep Learning.

Stanford Taylor1, James M Brown2, Kishan Gupta1, J Peter Campbell1, Susan Ostmo1, R V Paul Chan3, Jennifer Dy4, Deniz Erdogmus4, Stratis Ioannidis4, Sang J Kim1,5, Jayashree Kalpathy-Cramer2,6, Michael F Chiang1,7.   

Abstract

IMPORTANCE: Retinopathy of prematurity (ROP) is a leading cause of childhood blindness worldwide, but clinical diagnosis is subjective and qualitative.
OBJECTIVE: To describe a quantitative ROP severity score derived using a deep learning algorithm designed to evaluate plus disease and to assess its utility for objectively monitoring ROP progression. DESIGN, SETTING, AND PARTICIPANTS: This retrospective cohort study included images from 5255 clinical examinations of 871 premature infants who met the ROP screening criteria of the Imaging and Informatics in ROP (i-ROP) Consortium, which comprises 9 tertiary care centers in North America, from July 1, 2011, to December 31, 2016. Data analysis was performed from July 2017 to May 2018. EXPOSURE: A deep learning algorithm was used to assign a continuous ROP vascular severity score from 1 (most normal) to 9 (most severe) at each examination based on a single posterior photograph compared with a reference standard diagnosis (RSD) simplified into 4 categories: no ROP, mild ROP, type 2 ROP or pre-plus disease, or type 1 ROP. Disease course was assessed longitudinally across multiple examinations for all patients. MAIN OUTCOMES AND MEASURES: Mean ROP vascular severity score progression over time compared with the RSD.
RESULTS: A total of 5255 clinical examinations from 871 infants (mean [SD] gestational age, 27.0 [2.0] weeks; 493 [56.6%] male; mean [SD] birth weight, 949 [271] g) were analyzed. The median severity scores for each category were as follows: 1.1 (interquartile range [IQR], 1.0-1.5) (no ROP), 1.5 (IQR, 1.1-3.4) (mild ROP), 4.6 (IQR, 2.4-5.3) (type 2 and pre-plus), and 7.5 (IQR, 5.0-8.7) (treatment-requiring ROP) (P < .001). When the long-term differences in the median severity scores across time between the eyes progressing to treatment and those who did not eventually require treatment were compared, the median score was higher in the treatment group by 0.06 at 30 to 32 weeks, 0.75 at 32 to 34 weeks, 3.56 at 34 to 36 weeks, 3.71 at 36 to 38 weeks, and 3.24 at 38 to 40 weeks postmenstrual age (P < .001 for all comparisons). CONCLUSIONS AND RELEVANCE: The findings suggest that the proposed ROP vascular severity score is associated with category of disease at a given point in time and clinical progression of ROP in premature infants. Automated image analysis may be used to quantify clinical disease progression and identify infants at high risk for eventually developing treatment-requiring ROP. This finding has implications for quality and delivery of ROP care and for future approaches to disease classification.

Entities:  

Year:  2019        PMID: 31268518      PMCID: PMC6613341          DOI: 10.1001/jamaophthalmol.2019.2433

Source DB:  PubMed          Journal:  JAMA Ophthalmol        ISSN: 2168-6165            Impact factor:   7.389


  33 in total

1.  Plus disease in retinopathy of prematurity: quantitative analysis of vascular change.

Authors:  Preeti J Thyparampil; Yangseon Park; M E Martinez-Perez; Thomas C Lee; David J Weissgold; Audina M Berrocal; R V Paul Chan; John T Flynn; Michael F Chiang
Journal:  Am J Ophthalmol       Date:  2010-10       Impact factor: 5.258

Review 2.  The International Classification of Retinopathy of Prematurity revisited.

Authors: 
Journal:  Arch Ophthalmol       Date:  2005-07

3.  Validation of the Children's Hospital of Philadelphia Retinopathy of Prematurity (CHOP ROP) Model.

Authors:  Gil Binenbaum; Gui-Shuang Ying; Lauren A Tomlinson
Journal:  JAMA Ophthalmol       Date:  2017-08-01       Impact factor: 7.389

Review 4.  Telemedicine for retinopathy of prematurity.

Authors:  Daniel T Weaver
Journal:  Curr Opin Ophthalmol       Date:  2013-09       Impact factor: 3.761

5.  Evaluation of a deep learning image assessment system for detecting severe retinopathy of prematurity.

Authors:  Travis K Redd; John Peter Campbell; James M Brown; Sang Jin Kim; Susan Ostmo; Robison Vernon Paul Chan; Jennifer Dy; Deniz Erdogmus; Stratis Ioannidis; Jayashree Kalpathy-Cramer; Michael F Chiang
Journal:  Br J Ophthalmol       Date:  2018-11-23       Impact factor: 4.638

6.  Plus Disease in Retinopathy of Prematurity: A Continuous Spectrum of Vascular Abnormality as a Basis of Diagnostic Variability.

Authors:  J Peter Campbell; Jayashree Kalpathy-Cramer; 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:  2016-08-31       Impact factor: 12.079

7.  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

8.  Validation of the Colorado Retinopathy of Prematurity Screening Model.

Authors:  Emily A McCourt; Gui-Shuang Ying; Anne M Lynch; Alan G Palestine; Brandie D Wagner; Erica Wymore; Lauren A Tomlinson; Gil Binenbaum
Journal:  JAMA Ophthalmol       Date:  2018-04-01       Impact factor: 7.389

9.  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

10.  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

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  20 in total

1.  Telemedicine for Retinopathy of Prematurity in 2020.

Authors:  Theodore Bowe; Cindy Ung; J Peter Campbell; Yoshihiro Yonekawa
Journal:  J Vitreoretin Dis       Date:  2019-09-05

2.  Real-time retinal layer segmentation of OCT volumes with GPU accelerated inferencing using a compressed, low-latency neural network.

Authors:  Svetlana Borkovkina; Acner Camino; Worawee Janpongsri; Marinko V Sarunic; Yifan Jian
Journal:  Biomed Opt Express       Date:  2020-06-24       Impact factor: 3.732

3.  Variability in Plus Disease Identified Using a Deep Learning-Based Retinopathy of Prematurity Severity Scale.

Authors:  Rene Y Choi; James M Brown; Jayashree Kalpathy-Cramer; R V Paul Chan; Susan Ostmo; Michael F Chiang; J Peter Campbell
Journal:  Ophthalmol Retina       Date:  2020-05-04

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

Authors:  Kellyn N Bellsmith; James Brown; Sang Jin Kim; Isaac H Goldstein; Aaron Coyner; Susan Ostmo; Kishan Gupta; R V Paul Chan; Jayashree Kalpathy-Cramer; Michael F Chiang; J Peter Campbell
Journal:  Ophthalmology       Date:  2020-02-07       Impact factor: 12.079

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.  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

7.  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

8.  Evaluation of pediatric ophthalmologists' perspectives of artificial intelligence in ophthalmology.

Authors:  Nita G Valikodath; Tala Al-Khaled; Emily Cole; Daniel S W Ting; Elmer Y Tu; J Peter Campbell; Michael F Chiang; Joelle A Hallak; R V Paul Chan
Journal:  J AAPOS       Date:  2021-06-01       Impact factor: 1.325

Review 9.  Neurodevelopmental outcomes in preterm infants with retinopathy of prematurity.

Authors:  Hao Tan; Patricia Blasco; Tamorah Lewis; Susan Ostmo; Michael F Chiang; John Peter Campbell
Journal:  Surv Ophthalmol       Date:  2021-03-02       Impact factor: 6.197

10.  Impact of Artificial Intelligence on Medical Education in Ophthalmology.

Authors:  Nita G Valikodath; Emily Cole; Daniel S W Ting; J Peter Campbell; Louis R Pasquale; Michael F Chiang; R V Paul Chan
Journal:  Transl Vis Sci Technol       Date:  2021-06-01       Impact factor: 3.283

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