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, 12 (1), 44-49

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

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

Ali Kordzadeh et al. Ann Vasc Dis.

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 each output matrix at best sensitivity and at 1-specificity with the log-rank test p<0.01. ANN classification identified age, artery and vein diameter to influence FM with an accuracy of (>89%). AI has the ability of predicting with a high grade of accuracy FM and recognising patterns that influence it. Conclusion: AI is a replicable tool that could remain up to date and flexible to ongoing deep learning with further data feed ensuring substantial enhancement in its accuracy. AI could serve as a clinical decision-making tool and its application in vascular access requires further evaluation.

Keywords: artificial intelligence (AI); artificial neural network (ANN); functional maturation (FM); pattern recognition; radiocephalic arteriovenous fistula (RCAVF).

Figures

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Fig. 1 Artificial neural network training performance.
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Fig. 2 Artificial neural network confusion matrix and their corresponding area under curve (AUC) for best sensitivity and 1-specificity from the network’s training, validation and testing data with 10 hidden layers, 10 input elements and two outputs.
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Fig. 3 (a) Three dimensional scatter plot and the cubic surface interpolant with different combinations of features (artery size and vein size vs. age at the time of surgery); (b) the 2 dimensional plot for patient’s ASize (artery size) and VSize (vein size). The sample values are shown in black points; (c) the scale conjugate gradient used for training the data in artificial neural network pattern recognition with 30 epochs; (d) the epochs value used for data validation which is in the range of (0–6).

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