Evaluation of artificial intelligence-based telemedicine screening for retinopathy of prematurity

Miles F. Greenwald, Ian D. Danford, Malika Shahrawat, Susan Ostmo, James Brown, Jayashree Kalpathy-Cramer, Kacy Bradshaw, Robert Schelonka, Howard S. Cohen, R. V.Paul Chan, Michael F. Chiang, J. Peter Campbell

Research output: Contribution to journalArticle

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.

Original languageEnglish (US)
Pages (from-to)160-162
Number of pages3
JournalJournal of AAPOS
Volume24
Issue number3
DOIs
StatePublished - Jun 2020

ASJC Scopus subject areas

  • Pediatrics, Perinatology, and Child Health
  • Ophthalmology

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  • Cite this

    Greenwald, M. F., Danford, I. D., Shahrawat, M., Ostmo, S., Brown, J., Kalpathy-Cramer, J., Bradshaw, K., Schelonka, R., Cohen, H. S., Chan, R. V. P., Chiang, M. F., & Campbell, J. P. (2020). Evaluation of artificial intelligence-based telemedicine screening for retinopathy of prematurity. Journal of AAPOS, 24(3), 160-162. https://doi.org/10.1016/j.jaapos.2020.01.014