Artificial Intelligence in Interventional Radiology: A Literature Review and Future Perspectives.
Journal
Journal of oncology
ISSN: 1687-8450
Titre abrégé: J Oncol
Pays: Egypt
ID NLM: 101496537
Informations de publication
Date de publication:
2019
2019
Historique:
received:
18
05
2019
revised:
22
09
2019
accepted:
01
10
2019
entrez:
30
11
2019
pubmed:
30
11
2019
medline:
30
11
2019
Statut:
epublish
Résumé
The term "artificial intelligence" (AI) includes computational algorithms that can perform tasks considered typical of human intelligence, with partial to complete autonomy, to produce new beneficial outputs from specific inputs. The development of AI is largely based on the introduction of artificial neural networks (ANN) that allowed the introduction of the concepts of "computational learning models," machine learning (ML) and deep learning (DL). AI applications appear promising for radiology scenarios potentially improving lesion detection, segmentation, and interpretation with a recent application also for interventional radiology (IR) practice, including the ability of AI to offer prognostic information to both patients and physicians about interventional oncology procedures. This article integrates evidence-reported literature and experience-based perceptions to assist not only residents and fellows who are training in interventional radiology but also practicing colleagues who are approaching to locoregional mini-invasive treatments.
Identifiants
pubmed: 31781215
doi: 10.1155/2019/6153041
pmc: PMC6874978
doi:
Types de publication
Journal Article
Review
Langues
eng
Pagination
6153041Informations de copyright
Copyright © 2019 Roberto Iezzi et al.
Déclaration de conflit d'intérêts
The authors declare that they have no conflicts of interest.
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