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MACHINE LEARNING

AI for medical imaging goes deep

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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 to Daniel 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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