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

ABSTRACT: 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. Materials and Methods: A prospective database of 266 individuals over a four-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 area under the curve for

SUBMITTER: Kordzadeh A 

PROVIDER: S-EPMC6434352 | biostudies-literature | 2019 Mar

REPOSITORIES: biostudies-literature