A neural network-based algorithm for predicting the spontaneous passage of ureteral stones
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ORIGINAL PAPER
A neural network‑based algorithm for predicting the spontaneous passage of ureteral stones Mehmet Solakhan1 · Serap Ulusam Seckiner2 · Ilker Seckiner3 Received: 28 May 2019 / Accepted: 9 October 2019 © Springer-Verlag GmbH Germany, part of Springer Nature 2019
Abstract In this study, a prototype artificial neural network model (ANN) was used to estimate the stone passage rate and to determine the effectivity of predictive factors on this rate in patients with ureteral stones. The retrospective study included a total of 192 patients with ureteral stones, comprising 128 (66.7%) men and 64 (33.3%) women. Patients were divided into two groups. Group 1 (n: 125) consisted of people who spontaneously passed their stones, Group 2 (n: 67) consisted of people who could not pass stones spontaneously. The groups were compared with regard to the relationship between input data and stone passage rate by using both ANN and standard statistical tests. To implement the ANN, the patients were randomly divided into three groups: (a) training group (n = 132), (b) validation group (n = 30), and (c) test group (n = 30). The accuracy rate of ANN in the estimation of the stone passage ratio was 99.1% in the group a, 89.9% in the group b, and 87.3% in the group c. It was revealed that certain criteria (stone size, body weight, pain score, ESR, and CRP) were relatively more significant for saving treatment cost and time and for avoiding unnecessary treatment. ANN can be highly useful for the avoidance of unnecessary interventions in patients with ureteral stones as it showed remarkably high performance in the estimation of stone passage rate (99.16%). Keywords Artificial neural network · Ureteral stones · Spontaneous passage · Prediction · Decision-making
Introduction Ureteral stones are responsible for 20% of all urolithiasis cases, with a prevalence of 3–5% and a higher preponderance in men [1]. In these stones, the stone passage rate is 71–98% when the diameter is less than 5 mm and can be 21–51% for stones with a diameter of 5–10 mm. Due to these high rates, conventional methods have been preferred more frequently in the management of ureteral stones in recent * Mehmet Solakhan [email protected] Serap Ulusam Seckiner [email protected] Ilker Seckiner [email protected] 1
Department of Urology, Bahcesehir University School of Medicine, Istanbul, Turkey
2
Department of Industrial Engineering, Gaziantep University School of Engineering, Gaziantep, Turkey
3
Department of Urology, Gaziantep University School of Medicine, Gaziantep, Turkey
years [2]. Moreover, since they have a remarkable effect on the patients’ quality of life, ureteral stones need to be treated at the earliest stage. Medical expulsive treatment is commonly used to facilitate spontaneous passage of ureteral stones, particularly of distal ureteral stones. The non-passing stones, however, can be treated by ureterorenoscopy (URS) and extracorporeal shock-wave lithotripsy (ESWL) depending on the clinical characteristics of the patient
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