SWL literature
Slider

Seckiner I et al, 2017: A neural network - based algorithm for predicting stone - free status after ESWL therapy.

Seckiner I, Seckiner S, Sen H, Bayrak O, Dogan K, Erturhan S.
Department of Gastroenterology, Changhai Hospital, the Second Military Medical University, Shanghai, China.
Digestive Endoscopy Center, Changhai Hospital, the Second Military Medical University, Shanghai, China.

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.
MATERIALS AND METHODS: Data were collected from the 203 patients including gender, single or multiple nature of the stone, location of the stone, infundibulopelvic angle primary or secondary nature of the stone, status of hydronephrosis, stone size after ESWL, age, size, skin to stone distance, stone density and creatinine, for eleven variables. Regression analysis and the ANN method were applied to predict treatment success using the same series of data.
RESULTS: Subsequently, patients were divided into three groups by neural network software, in order to implement the ANN: training group (n=139), validation group (n=32), and the test group (n=32). ANN analysis demonstrated that the prediction accuracy of the stone-free rate was 99.25% in the training group, 85.48% in the validation group, and 88.70% in the test group.
CONCLUSIONS: Successful results were obtained to predict the stone-free rate, with the help of the ANN model designed by using a series of data collected from real patients in whom ESWL was implemented to help in planning treatment for kidney stones.

Int Braz J Urol. 2017 Jul 20;43. doi: 10.1590/S1677-5538.IBJU.2016.0630. [Epub ahead of print]

 

0
 

Комментарии 1

Hans-Göran Tiselius в 18.01.2018 09:02

Numerous methods have been published during recent years with the aim of predicting outcome / stone-free rate after SWL. In this report the authors used an artificial neural network (ANN) for this purpose. The reviewer is poorly familiar with the mathematics that underlies the conclusions drawn by an ANN.
Nevertheless, the predicted stone-free rate in the test patients around 89% is impressive. The most important factors emphasized were number, size and location of stones in addition to the IP-angle and SSD. Surprisingly stone density was not and data on stone composition were obviously not included. To which extent ANN will turn out to be superior to other systems for predicting SWL success remains to be shown.

Numerous methods have been published during recent years with the aim of predicting outcome / stone-free rate after SWL. In this report the authors used an artificial neural network (ANN) for this purpose. The reviewer is poorly familiar with the mathematics that underlies the conclusions drawn by an ANN. Nevertheless, the predicted stone-free rate in the test patients around 89% is impressive. The most important factors emphasized were number, size and location of stones in addition to the IP-angle and SSD. Surprisingly stone density was not and data on stone composition were obviously not included. To which extent ANN will turn out to be superior to other systems for predicting SWL success remains to be shown.
Гость
23.08.2019
STORZ MEDICAL AG
Lohstampfestrasse 8
8274 Tägerwilen
Switzerland
Tel.: +41 (0)71 677 45 45
Fax: +41 (0)71 677 45 05

www.storzmedical.com