Prediction, pattern recognition and modelling of complications post-endovascular infra renal aneurysm repair by artificial intelligence.

Journal: Vascular

PMID: 32829694

Abstract

OBJECTIVES: The study evaluates the plausibility and applicability of prediction, pattern recognition and modelling of complications post-endovascular aneurysm repair (EVAR) by artificial intelligence for more accurate surveillance in practice.

Authors

Ali Kordzadeh

Department of Vascular, Endovascular and Renal Access, Mid Essex Hospitals Services NHS Trust, Broomfield Hospital, Essex, UK.
Mohammad A Hanif

Department of Interventional Radiology, Mid Essex Hospitals Services NHS Trust, Broomfield Hospital, Essex, UK.
Manfred J Ramirez

Department of Vascular, Endovascular and Renal Access, Mid Essex Hospitals Services NHS Trust, Broomfield Hospital, Essex, UK.
Nicholas Railton

Department of Interventional Radiology, Mid Essex Hospitals Services NHS Trust, Broomfield Hospital, Essex, UK.
Ioannis Prionidis

Department of Vascular, Endovascular and Renal Access, Mid Essex Hospitals Services NHS Trust, Broomfield Hospital, Essex, UK.
Thomas Browne

Department of Vascular, Endovascular and Renal Access, Mid Essex Hospitals Services NHS Trust, Broomfield Hospital, Essex, UK.

Keywords

Aged Aged, 80 and over Aortic Aneurysm, Abdominal Artificial Intelligence Blood Vessel Prosthesis Blood Vessel Prosthesis Implantation Decision Trees Endoleak Endovascular Procedures Female Foreign-Body Migration Graft Occlusion, Vascular Humans Male Middle Aged Pattern Recognition, Automated Postoperative Complications Prospective Studies Risk Assessment Risk Factors Support Vector Machine Time Factors Treatment Outcome

External Resources

View on PubMed Access via DOI PubMed (32829694)

Prediction, pattern recognition and modelling of complications post-endovascular infra renal aneurysm repair by artificial intelligence.

Abstract

Authors

Keywords

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