TY - JOUR
T1 - Artificial Intelligence in Interventional Radiology: A Literature Review and Future Perspectives
AU - Iezzi, Roberto
AU - Goldberg, S. N.
AU - Merlino, Biagio
AU - Posa, A.
AU - Valentini, V.
AU - Manfredi, Riccardo
PY - 2019
Y1 - 2019
N2 - 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.
AB - 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.
KW - interventional in artificial intelligence
KW - locoregional
KW - interventional in artificial intelligence
KW - locoregional
UR - https://publicatt.unicatt.it/handle/10807/155242
UR - https://www.scopus.com/inward/citedby.uri?partnerID=HzOxMe3b&scp=85075381448&origin=inward
UR - https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85075381448&origin=inward
U2 - 10.1155/2019/6153041
DO - 10.1155/2019/6153041
M3 - Article
SN - 1687-8450
VL - 2019
SP - N/A-N/A
JO - Journal of Oncology
JF - Journal of Oncology
IS - N/A
ER -