@article{ac64958f5dac400e802d5c0e38ca8dfa,
title = "Evaluation of artificial intelligence-based telemedicine screening for retinopathy of prematurity",
abstract = "Retrospective evaluation of a deep learning–derived retinopathy of prematurity (ROP) vascular severity score in an operational ROP screening program demonstrated high diagnostic performance for detection of type 2 or worse ROP. To our knowledge, this is the first report in the literature that evaluated the use of artificial intelligence for ROP screening and represents a proof of concept. With further prospective validation, this technology might improve the accuracy, efficiency, and objectivity of diagnosis and facilitate earlier detection of disease progression in patients with potentially blinding ROP.",
author = "Greenwald, {Miles F.} and Danford, {Ian D.} and Malika Shahrawat and Susan Ostmo and James Brown and Jayashree Kalpathy-Cramer and Kacy Bradshaw and Robert Schelonka and Cohen, {Howard S.} and Chan, {R. V.Paul} and Chiang, {Michael F.} and Campbell, {J. Peter}",
note = "Funding Information: Funding support: NIH grants R01EY19474 , P30EY10572 , P30 EY001792 , R01EY19474 and K12EY27720 (Bethesda, MD), NSF grants SCH-1622679 and 1622542 (Arlington, VA), and unrestricted departmental funding and a Career Development Award ( JPC ) from Research to Prevent Blindness (New York, NY). The sponsor or funding organizations had no role in the design or conduct of this research. Publisher Copyright: {\textcopyright} 2020 American Association for Pediatric Ophthalmology and Strabismus",
year = "2020",
month = jun,
doi = "10.1016/j.jaapos.2020.01.014",
language = "English (US)",
volume = "24",
pages = "160--162",
journal = "Journal of AAPOS",
issn = "1091-8531",
publisher = "Mosby Inc.",
number = "3",
}