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| The use of an artificial neural network in the evaluation of the extracorporeal shockwave lithotripsy as a treatment of choice for urinary lithiasis |
Athanasios Tsitsiflisa,Yiannis Kiouvrekisb,c,Georgios Chasiotisa,Georgios Perifanosd,Stavros Gravasa,Ioannis Stefanidise,Vassilios Tzortzisa,Anastasios Karatzasa,*( )
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a Department of Urology, Faculty of Medicine, School of Health Sciences, University of Thessaly,Larissa, Greece
b Department of Public and Integrated Health, University of Thessaly, Karditsa, Greece
c Business School, University of Nicosia, Nicosia, Cyprus
d Department of Internal Medicine, Faculty of Medicine, School of Health Sciences, University of Thessaly, Larissa, Greece
e Department of Nephrology, Faculty of Medicine, School of Health Sciences, University of Thessaly, Larissa, Greece |
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Abstract Objective: Artificial neural networks (ANNs) are widely applied in medicine, since they substantially increase the sensitivity and specificity of the diagnosis, classification, and the prognosis of a medical condition. In this study, we constructed an ANN to evaluate several parameters of extracorporeal shockwave lithotripsy (ESWL), such as the outcome and safety of the procedure. Methods: Patients with urinary lithiasis suitable for ESWL treatment were enrolled. An ANN was designed using MATLAB. Medical data were collected from all patients and 12 nodes were used as inputs. Conventional statistical analysis was also performed. Results: Finally, 716 patients were included in our study. Univariate analysis revealed that diabetes and hydronephrosis were positively correlated with ESWL complications. Regarding efficacy, univariate analysis revealed that stone location, stone size, the number and density of shockwaves delivered, and the presence of a stent in the ureter were independent factors of the ESWL outcome. This was further confirmed when adjusted for sex and age in a multivariate analysis. The performance of the ANN at the end of the training state reached 98.72%. The four basic ratios (sensitivity, specificity, positive predictive value, and negative predictive value) were calculated for both training and evaluation data sets. The performance of the ANN at the end of the evaluation state was 81.43%. Conclusion: Our ANN achieved high score in predicting the outcome and the side effects of the ESWL treatment for urinary stones.
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Received: 26 November 2020
Available online: 20 April 2022
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Corresponding Authors:
Anastasios Karatzas
E-mail: karatzas@med.uth.gr
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| Patient category | Complication after ESWL, n | No complication after ESWL, n | p-Value | | Sex | | | 0.470 | | Male | 213 | 191 | | | Female | 156 | 156 | | | Age, year | | | 0.945 | | ≤30 | 18 | 29 | | | 31-45 | 76 | 69 | | | 46-60 | 136 | 117 | | | ≥61 | 139 | 132 | | | BMI, kg/m2 | | | 0.035 | | <18.50 | 0 | 3 | | | 18.50-24.99 | 104 | 92 | | | 25.00-29.99 | 167 | 166 | | | ≥30.00 | 98 | 86 | | | Stone location | | | 0.311 | | Right kidney | 131 | 113 | | | Left kidney | 116 | 121 | | | Bladder | 7 | 8 | | | Left ureteral | 49 | 58 | | | Right ureteral | 66 | 47 | | | Stone size (diameter), mm | | | 0.541 | | ≤6 | 42 | 31 | | | 7-9 | 89 | 78 | | | 10-11 | 78 | 71 | | | 12-13 | 59 | 67 | | | 14-15 | 44 | 40 | | | 16-20 | 47 | 51 | | | 21-32 | 10 | 9 | | | Comorbidity | | | 0.533 | | No symptoms | 245 | 215 | | | One symptom | 68 | 71 | | | Two or more symptoms | 56 | 61 | | | Previous ESWL sessions | | | <0.001 | | Yes | 262 | 146 | | | No | 107 | 201 | | | Analgesia | | | 0.013 | | Yes | 16 | 31 | | | No | 353 | 316 | | | Number of shocks | | | 0.118 | | 2500-3500 | 349 | 325 | | | ≥3500 | 22 | 20 | | | Intensity | | | 0.060 | | <40% | 3 | 2 | | | 40%-60% | 78 | 52 | | | 61%-80% | 265 | 267 | | | 81%-100% | 23 | 26 | | | Pig-tail existence | | | 0.797 | | Yes | 80 | 78 | | | No | 289 | 269 | | | Hydronephrosis, n | <0.001 | | Yes | 74 | 137 | | | No | 295 | 210 | |
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Characteristics of 716 patients treated with ESWL.
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| Variable | Mean | SD | | Age, year | 54.70 | 14.19 | | BMI, kg/m2 | 27.66 | 4.25 | | Stone size (diameter), mm | 11.50 | 4.45 | | Number of shock | 3050.54 | 484.92 |
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Statistical analysis of input variables.
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Artificial neural network nodes and connection. BMI, body mass index; ESWL, extracorporeal shockwave lithotripsy.
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| Variables | Neuron/variable, n | Input value (neuron) | | Sex | 1 | - Male or female | | Age | 1 | - Positive number | | BMI | 1 | - Positive number | | Stone location | 5 | - Right kidney, left kidney, bladder, left ureter, or right ureter | | Stone size | 1 | - Positive number | | Comorbidity | 5 | - Anticoagulant, heart issues, diabetes, hypertension, orcoagulation issues | | Previous ESWL | 1 | - Yes or no | | Analgesia | 1 | - Yes or no | | Number of shocks | 1 | - Positive number percentage | | Intensity | 1 | - Yes or no | | Pig-tail existence | 1 | - Yes or no | | Hydronephrosis | 1 | - 1: For complications; 0: Without complications | | Output neuron | 1 | - 1: For complications; 0: Without complications |
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ANN input data.
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Stone location. Numbers (1, 0) were used in the ANN to denote the presence of the stone in kidneys, ureters, and bladder. 1: Stone presence; 0: Stone absence.
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| Variable | Training set (334 patients), % | Evaluation set (84 patients), % | | Performance | 92.81 | 59.52 | | Specificity | 93.41 | 55.93 | | Sensitivity | 92.21 | 68.00 | | Positive predictive value | 92.30 | 80.48 | | Negative predictive value | 93.33 | 39.53 |
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Artificial neural network with seven inputs.
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| Variable | Training set (334 patients), % | Evaluation set (84 patients), % | | Performance | 99.10 | 75.00 | | Specificity | 99.40 | 71.18 | | Sensitivity | 98.80 | 84.00 | | Positive predictive value | 98.80 | 91.30 | | Negative predictive value | 99.39 | 55.26 |
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Artificial neural network with 12 inputs.
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| Variable | Training set (549 patients), % | Evaluation set (167 patients), % | | Performance | 98.72 | 81.43 | | Specificity | 98.88 | 74.02 | | Sensitivity | 98.56 | 87.77 | | Positive predictive value | 98.52 | 83.82 | | Negative predictive value | 98.92 | 79.79 |
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Final artificial neural network.
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