Original Article Prediction, pattern recognition and modelling of complications post- endovascular infra renal aneurysm repair by artificial intelligence Ali Kordzadeh 1 , Mohammad A Hanif 2 , Manfred J Ramirez 1 , Nicholas Railton 2 , Ioannis Prionidis 1 and Thomas Browne 1 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. Methods: A single-centre prospective data collection on (n ¼ 250) EVAR cases with n ¼ 26 preoperative attributes (factors) on endpoint of endoleak (types I–VI), occlusion, migration and mortality over a 13-year period was conducted. In addition to the traditional statistical analysis, data was subjected to machine learning algorithm through artificial neural network. The predictive accuracy (specificity and –1 sensitivity) on each endpoint is presented with percentage and receiver operative curve. The pattern recognition and model classification were conducted using discriminate analysis, decision tree, logistic regression, naive Bayes and support vector machines, and the best fit model was deployed for pattern recognition and modelling. Results: The accuracy of the training, validation and predictive ability of artificial neural network in detection of endoleak type I was 95, 96 and 94%, type II (94, 83, 90 and 82%) and type III was 96, 94 and 96%, respectively. Endpoints are associated with increase in weights through predictive modeling that were not detected through statistical analytics. The overall accuracy of the model was >86%. Conclusion: The study highlights the applicability, accuracy and reliability of artificial intelligence in the detection of adverse outcomes post-EVAR for an accurate surveillance stratification. Keywords Artificial intelligence, artificial neural network, endovascular aneurysm repair, prediction, pattern recognition Introduction Evidence suggests that one in every five individuals post-endovascular aneurysm repair (EVAR) could face endoleak, sac expansion, device migration, limb occlusion and stenosis over the first five years of sur- veillance. 1–4 EVAR is subjected to lifelong surveillance by different modalities such as duplex sonography, contrast enhanced ultrasound (CEUS), computed tomography angiogram (CTA) and magnetic reso- nance imaging (MRI). 5,6 To date, various surveillance protocols tailored to the local population and expertise have been formulated. Despite this, 50–90% of compli- cations remain undetected, prevail beyond surveillance, present between surveillance time frames and attribute to symptoms or outcomes that demand acute interven- tion associated with significant mortality and morbid- ity. 7,8 Thus, subjecting every individual to repeated radiation and contrast, besides the financial burden 1 Department of Vascular, Endovascular and Renal Access, Mid Essex Hospitals Services NHS Trust, Broomfield Hospital, Essex, UK 2 Department of Interventional Radiology, Mid Essex Hospitals Services NHS Trust, Broomfield Hospital, Essex, UK Corresponding author: Ali Kordzadeh, Mid Essex Hospital Service NHS Trust, Department of vascular, Endovascular and renal access, Broomfield Hospital, Court Road, Essex CM1 7ET, UK. Email: Alikordzadeh@gmail.com Vascular 0(0) 1–12 ! The Author(s) 2020 Article reuse guidelines: sagepub.com/journals-permissions DOI: 10.1177/1708538120949658 journals.sagepub.com/home/vas