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Review
. 2020 Jan;55(1):1-7.
doi: 10.1097/RLI.0000000000000600.

A Practical Guide to Artificial Intelligence-Based Image Analysis in Radiology

Thomas Weikert  1 , Joshy Cyriac, Shan Yang, Ivan Nesic, Victor Parmar, Bram Stieltjes
Affiliations
Review

A Practical Guide to Artificial Intelligence-Based Image Analysis in Radiology

Thomas Weikert et al. Invest Radiol. 2020 Jan.

Abstract

The use of artificial intelligence (AI) is a powerful tool for image analysis that is increasingly being evaluated by radiology professionals. However, due to the fact that these methods have been developed for the analysis of nonmedical image data and data structure in radiology departments is not "AI ready", implementing AI in radiology is not straightforward. The purpose of this review is to guide the reader through the pipeline of an AI project for automated image analysis in radiology and thereby encourage its implementation in radiology departments. At the same time, this review aims to enable readers to critically appraise articles on AI-based software in radiology.

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References

    1. Röntgen WC. Über eine neue Art von Strahlen. Sitzungsberichte der Physikalisch-Medizinischen Gesellschaft zu Würzburg. 1895:2–16.
    1. Buchanan BG. A (very) brief history of artificial intelligence. AI Mag. 2005;26:53–53.3.
    1. McCorduck P. Machines Who Think. A K Peters: Wellesley, MA; 2004.
    1. Jordan MI, Mitchell TM. Machine learning: trends, perspectives, and prospects. Science. 2015;349:255–260.
    1. Chartrand G, Cheng PM, Vorontsov E, et al. Deep learning: a primer for radiologists. Radiographics. 2017;37:2113–2131.

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