Artificial Intelligence in Interventional Radiology: A Literature Review and Future Perspectives. (3rd November 2019)

• Record Type: Journal Article
• Authors: Iezzi, Roberto; Goldberg, S. N.; Merlino, B.; Posa, A.; Valentini, V.; Manfredi, R.
• Other Names: De Felice Francesca Academic Editor.
• Is Part Of: Journal of oncology. Volume 2019(2019)
• Publication Date: 2019-11-03
• Subjects: Oncology -- Research -- Periodicals; Tumors -- Periodicals; Neoplasms; Oncology -- Research; Tumors; Periodicals; Periodicals; 616.994
• DOI: 10.1155/2019/6153041
• Languages: English
• ISSNs: 1687-8450

Abstract

Abstract : 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.