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Review
. 2019 Oct 12;9(25):7556-7565.
doi: 10.7150/thno.38065. eCollection 2019.

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

Jie Xu  1 , Kanmin Xue  2 , Kang Zhang  3
Affiliations
Review

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

Jie Xu et al. Theranostics. .

Abstract

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.

Keywords: artificial intelligence; deep learning; machine learning; ophthalmology.

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Conflict of interest statement

Competing Interests: The authors have declared that no competing interest exists.

Figures

Figure 1
Figure 1
A typical deep learning neural network with multiple deep layers between input and output layers
Figure 2
Figure 2
Workflow diagram of developing a deep learning-based medical diagnostic algorithm.
Figure 3
Figure 3
Illustrations of transfer learning: a neural network is pretrained on ImageNet and subsequently trained on retinal, OCT, X-ray images, B-scans for different disease classifications
See this image and copyright information in PMC

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