AI for medical imaging goes deep (original) (raw)

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Nature Medicine volume 24, pages 539–540 (2018)Cite this article

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An artificial intelligence (AI) using a deep-learning approach can classify retinal images from optical coherence tomography for early diagnosis of retinal diseases and has the potential to be used in other image-based medical diagnoses.

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Fig. 1: Transfer learning can be applied to classify retinal optical coherence tomography images for early diagnosis of retinal diseases.

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Authors and Affiliations

  1. Singapore National Eye Center, Duke-NUS Medical School, National University of Singapore, Singapore, Singapore
    Daniel S. W. Ting & Tien Y. Wong
  2. Institute of High Performance Computing, Agency for Science, Technology and Research (A*STAR), Singapore, Singapore
    Daniel S. W. Ting, Yong Liu & Xinxing Xu
  3. Applied Physics Laboratory, Johns Hopkins University, Baltimore, MD, USA
    Philippe Burlina
  4. Wilmer Eye Institute, Johns Hopkins University, Baltimore, MD, USA
    Neil M. Bressler

Authors

  1. Daniel S. W. Ting
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  2. Yong Liu
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  3. Philippe Burlina
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  4. Xinxing Xu
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  5. Neil M. Bressler
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  6. Tien Y. Wong
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Corresponding author

Correspondence toDaniel S. W. Ting.

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Competing interests

D.S.W.T. and T.Y.W. are co-inventors of a patent on a deep learning system in detection of retinal diseases. N.M.B. and P.B. are co-inventors of a patent on a deep learning system in detection of age-related macular degeneration.

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Ting, D.S.W., Liu, Y., Burlina, P. et al. AI for medical imaging goes deep.Nat Med 24, 539–540 (2018). https://doi.org/10.1038/s41591-018-0029-3

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