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

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.

Predicting the Postoperative Outcome of Percutaneous Nephrolithotomy with Machine Learning System: Software Validation and Comparative Analysis with Guy's Stone Score and the CROES Nomogram

“…Previously, artificial intelligence systems were found to show similar or better efficacy than statistical data mining models in the evaluation of stone-free status after SWL. 30,31 Nevertheless, additional comparative studies are needed to evaluate the prognostic accuracy of these modern predictive approaches compared with statistical models in the field of PCNL. The ability to simultaneously predict the need for ancillary procedures and/or blood transfusion with the accuracy of ‡80% with intelligence systems is also acknowledgeable.…”

Predicate the Ability of Extracorporeal Shock Wave Lithotripsy (ESWL) to treat the Kidney Stones by used Combined Classifier

“…The area under ROC curve of the NCC for training sample is (0.97) which is higher than (0.85) of PR1 and (0.96) of NN1, and the area under ROC curve of NCC for testing sample is (0.87) which is also higher than PR1 and NN1 which are (0.72 and 0.81) respectively. By compared with previous works of [6] and [8], the results achieved higher accuracy than [6] in both NN and NCC where the accuracy of training result got about 22% accuracy improvement in training data detection and about 10% accuracy improvement in tested data. By compared with [8] the both got close accuracy result in detection of trained data where NCC got ~98% while their method achieved relatively higher accuracy of 99.25%, but for non-trained (tested) samples the proposed method (NCC) has achieved more accuracy reach to 91.5% which 3% higher accuracy than their method that achieved 88.7%.…”

The use and applicability of machine learning algorithms in predicting the surgical outcome for patients with benign prostatic enlargement. Which model to use?

“…Besides cancer-related research, AI systems have also been utilized in other aspects of urological pathology. In urinary stone disease, there are reports that AI systems have been implemented in order to predict stone composition (22), surgical outcomes of percutaneous nephrolithotomy (PNL) (23), and shock wave lithotripsy (SWL) (24), with excellent accuracy. Similarly in patients with vesicoureteral reflux, as reported by Seckiner et al (25), the ANN reported 98.5% sensitivity, 92.5% specificity, 97% positive predictive value, and 96% negative predictive value, which can definitely be considered very promising.…”