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Digestive Disease Interventions 2021; 05(04): 331-337
DOI: 10.1055/s-0041-1726300
Review Article

Use of Artificial Intelligence in Nononcologic Interventional Radiology: Current State and Future Directions

Rohil Malpani
1   Department of Radiology and Biomedical Imaging, Yale University School of Medicine, New Haven, Connecticut
,
Christopher W. Petty
1   Department of Radiology and Biomedical Imaging, Yale University School of Medicine, New Haven, Connecticut
,
Neha Bhatt 
1   Department of Radiology and Biomedical Imaging, Yale University School of Medicine, New Haven, Connecticut
,
Lawrence H. Staib 
1   Department of Radiology and Biomedical Imaging, Yale University School of Medicine, New Haven, Connecticut
,
Julius Chapiro 
1   Department of Radiology and Biomedical Imaging, Yale University School of Medicine, New Haven, Connecticut
› Author Affiliations Funding Research reported in this publication was supported by National Cancer Institute of the National Institutes of Health under grant number R01CA206180. The total cost was covered by this grant. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.
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Abstract

The future of radiology is disproportionately linked to the applications of artificial intelligence (AI). Recent exponential advancements in AI are already beginning to augment the clinical practice of radiology. Driven by a paucity of review articles in the area, this article aims to discuss applications of AI in nononcologic IR across procedural planning, execution, and follow-up along with a discussion on the future directions of the field. Applications in vascular imaging, radiomics, touchless software interactions, robotics, natural language processing, postprocedural outcome prediction, device navigation, and image acquisition are included. Familiarity with AI study analysis will help open the current “black box” of AI research and help bridge the gap between the research laboratory and clinical practice.

Keywords

artificial intelligence - machine learning - radiomics - deep learning - interventional radiology

IRB

This study does not involve human subjects and is IRB-exempt.


Disclaimer

Financial remuneration was not provided to authors and family members related to the subject of this article. J.C. reports grant support from the Society of Interventional Oncology, Guerbet Pharmaceuticals, Philips Healthcare, Boston Scientific, Yale Center for Clinical Investigation, and the NIH R01CA206180 outside the submitted work.




Publication History

Received: 03 November 2020

Accepted: 28 January 2021

Article published online:
17 July 2021

© 2021. Thieme. All rights reserved.

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