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Artificial Intelligence to Detect Papilledema from Ocular Fundus Photographs.

Dan MileaRaymond P NajjarJiang ZhuboDaniel TingCaroline VasseneixXinxing XuMasoud Aghsaei FardPedro FonsecaKavin VanikietiWolf A LagrèzeChiara La MorgiaCarol Y CheungSteffen HamannChristophe ChiquetNicolae SandaHui YangLuis J MejicoMarie-Bénédicte RougierRichard KhoThi Ha Chau TranShweta SinghalPhilippe GohierCatherine Clermont-VignalChing-Yu ChengJost B JonasPatrick Yu-Wai-ManClare L FraserJohn J ChenSelvakumar AmbikaNeil R MillerYong LiuNancy J NewmanTien Y WongValérie Bioussenull null
Published in: The New England journal of medicine (2020)
A deep-learning system using fundus photographs with pharmacologically dilated pupils differentiated among optic disks with papilledema, normal disks, and disks with nonpapilledema abnormalities. (Funded by the Singapore National Medical Research Council and the SingHealth Duke-NUS Ophthalmology and Visual Sciences Academic Clinical Program.).
Keyphrases
  • artificial intelligence
  • deep learning
  • quality improvement
  • big data
  • machine learning
  • diabetic retinopathy
  • convolutional neural network
  • optical coherence tomography
  • optic nerve
  • healthcare
  • medical students