The Role of Artificial Intelligence in the Prediction of Functional Maturation of Arteriovenous Fistula

Objective: The aim of this study is to examine the application of virtual artificial intelligence (AI) in the prediction of functional maturation (FM) and pattern recognition of factors in autogenous radiocephalic arteriovenous fistula (RCAVF) formation. Material and Methods: A prospective database of 266 individuals over a 4four-year period with n=10 variables, were used to train, validate and test an artificial neural network (ANN). The ANN was constructed to create a predictive model and evaluate the impact of variables on the endpoint of FM. Results: The overall accuracy of the training, validation, testing and all data on each output matrix at detecting FM was 86.4%, 82.5%, 77.5% and 84.5%, respectively. The results corresponded with their AUC for each output matrix at best sensitivity and at 1-specificty with the log-rank test p<0.01. ANN classification identified age, artery and vein diameter to influence FM with an accuracy of (>89%). Artificial intelligence has the ability to predict with a high grade of accuracy FM and recognizing patterns that influence it with a high grade of accuracy. Conclusion: AI is a replicable tool that could remain up-to-date and flexible too for ongoing deep learning with further data feed ensuring substantial enhancement in its accuracy with the further data feed. It AI could serve as a clinical decisionmaking tool and its application.