A neural network - based algorithm for predicting stone - free status after ESWL therapy.

Journal: International braz j urol : official journal of the Brazilian Society of Urology

Published Date: Jan 1, 2017

Abstract

OBJECTIVE: The prototype artificial neural network (ANN) model was developed using data from patients with renal stone, in order to predict stone-free status and to help in planning treatment with Extracorporeal Shock Wave Lithotripsy (ESWL) for kidney stones.

Authors

Ilker Seckiner

Department of Urology, Gaziantep University, Gaziantep, Turkey.
Serap Seckiner

Department of Endustrial Engineering, Gaziantep University, Gaziantep, Turkey.
Haluk Sen

Department of Urology, Gaziantep University, Gaziantep, Turkey.
Omer Bayrak

Department of Urology, Gaziantep University, Gaziantep, Turkey.
Kazim Dogan

Department of Urology, Gaziantep University, Gaziantep, Turkey.
Sakip Erturhan

Department of Urology, Gaziantep University, Gaziantep, Turkey.

Keywords

Adolescent Adult Aged Algorithms Child Child, Preschool Female Humans Infant Kidney Calculi Lithotripsy Male Middle Aged Neural Networks, Computer Predictive Value of Tests Regression Analysis Young Adult

External Resources

View on PubMed Access via DOI PubMed (28727384)

A neural network - based algorithm for predicting stone - free status after ESWL therapy.

Abstract

Authors

Keywords

External Resources

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