Current status and future trends of clinical diagnoses via image-based deep learning

With the recent developments in deep learning technologies, artificial intelligence (AI) has gradually been transformed from cutting-edge technology into practical applications. AI plays an important role in disease diagnosis and treatment, health management, drug research and development, and preci...

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Detalles Bibliográficos
Autores principales: Xu, Jie, Xue, Kanmin, Zhang, Kang
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Ivyspring International Publisher 2019
Materias:
Review
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6831476/
https://www.ncbi.nlm.nih.gov/pubmed/31695786
http://dx.doi.org/10.7150/thno.38065
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author Xu, Jie
Xue, Kanmin
Zhang, Kang
author_facet Xu, Jie
Xue, Kanmin
Zhang, Kang
author_sort Xu, Jie
collection PubMed
description With the recent developments in deep learning technologies, artificial intelligence (AI) has gradually been transformed from cutting-edge technology into practical applications. AI plays an important role in disease diagnosis and treatment, health management, drug research and development, and precision medicine. Interdisciplinary collaborations will be crucial to develop new AI algorithms for medical applications. In this paper, we review the basic workflow for building an AI model, identify publicly available databases of ocular fundus images, and summarize over 60 papers contributing to the field of AI development.
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spelling pubmed-68314762019-11-06 Current status and future trends of clinical diagnoses via image-based deep learning Xu, Jie Xue, Kanmin Zhang, Kang Theranostics Review With the recent developments in deep learning technologies, artificial intelligence (AI) has gradually been transformed from cutting-edge technology into practical applications. AI plays an important role in disease diagnosis and treatment, health management, drug research and development, and precision medicine. Interdisciplinary collaborations will be crucial to develop new AI algorithms for medical applications. In this paper, we review the basic workflow for building an AI model, identify publicly available databases of ocular fundus images, and summarize over 60 papers contributing to the field of AI development. Ivyspring International Publisher 2019-10-12 /pmc/articles/PMC6831476/ /pubmed/31695786 http://dx.doi.org/10.7150/thno.38065 Text en © The author(s) This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/). See http://ivyspring.com/terms for full terms and conditions.
spellingShingle Review
Xu, Jie
Xue, Kanmin
Zhang, Kang
Current status and future trends of clinical diagnoses via image-based deep learning
title Current status and future trends of clinical diagnoses via image-based deep learning
title_full Current status and future trends of clinical diagnoses via image-based deep learning
title_fullStr Current status and future trends of clinical diagnoses via image-based deep learning
title_full_unstemmed Current status and future trends of clinical diagnoses via image-based deep learning
title_short Current status and future trends of clinical diagnoses via image-based deep learning
title_sort current status and future trends of clinical diagnoses via image-based deep learning
topic Review
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6831476/
https://www.ncbi.nlm.nih.gov/pubmed/31695786
http://dx.doi.org/10.7150/thno.38065
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