Artificial Intelligence in Interventional Radiology: A Literature Review and Future Perspectives.
Roberto IezziS N GoldbergB MerlinoA PosaV ValentiniR ManfrediPublished in: Journal of oncology (2019)
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.
Keyphrases
- artificial intelligence
- deep learning
- machine learning
- neural network
- big data
- primary care
- convolutional neural network
- end stage renal disease
- healthcare
- ejection fraction
- endothelial cells
- newly diagnosed
- systematic review
- chronic kidney disease
- peritoneal dialysis
- preterm infants
- palliative care
- case report
- climate change
- health information
- induced pluripotent stem cells
- patient reported outcomes
- gestational age
- quantum dots
- label free