AI for medical imaging goes deep

Daniel S.W. Ting; Yong Liu; Philippe Burlina; Xinxing Xu; Neil M. Bressler; Tien Y. Wong

doi:10.1038/s41591-018-0029-3

AI for medical imaging goes deep

Daniel S.W. Ting, Yong Liu, Philippe Burlina, Xinxing Xu, Neil M. Bressler, Tien Y. Wong

School of Medicine

Research output: Contribution to journal › Article › peer-review

50 Scopus citations

Abstract

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.

Original language	English (US)
Pages (from-to)	539-540
Number of pages	2
Journal	Nature medicine
Volume	24
Issue number	5
DOIs	https://doi.org/10.1038/s41591-018-0029-3
State	Published - May 1 2018

ASJC Scopus subject areas

General Biochemistry, Genetics and Molecular Biology

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10.1038/s41591-018-0029-3

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title = "AI for medical imaging goes deep",

abstract = "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.",

author = "Ting, {Daniel S.W.} and Yong Liu and Philippe Burlina and Xinxing Xu and Bressler, {Neil M.} and Wong, {Tien Y.}",

year = "2018",

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AU - Ting, Daniel S.W.

AU - Liu, Yong

AU - Burlina, Philippe

AU - Xu, Xinxing

AU - Bressler, Neil M.

AU - Wong, Tien Y.

PY - 2018/5/1

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AB - 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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JO - Nature medicine

JF - Nature medicine

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AI for medical imaging goes deep

Abstract

ASJC Scopus subject areas

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