title: Artificial Neural Network System to Predict the Postoperative Outcome of Percutaneous Nephrolithotomy.
abstract: To construct, train, and apply an artificial neural network (ANN) system for prediction of different outcome variables of percutaneous nephrolithotomy (PCNL). We calculated predictive accuracy, sensitivity, and precision for each outcome variable.
  
  
  
  During the study period, all adult patients who underwent PCNL at our institute were enrolled in the study. Preoperative and postoperative variables were recorded, and stone-free status was assessed perioperatively with computed tomography scans. MATLAB software was used to design and train the network in a feed forward back-propagation error adjustment scheme. Preoperative and postoperative data from 200 patients (training set) were used to analyze the effect and relative relevance of preoperative values on postoperative parameters. The validated adequately trained ANN was used to predict postoperative outcomes in the subsequent 254 adult patients (test set) whose preoperative values were serially fed into the system. To evaluate system accuracy in predicting each postoperative variable, predicted values were compared with actual outcomes.
  
  
  
  Two hundred fifty-four patients (155 [61%] males) were considered the test set. Mean stone burden was 6702.86 ± 381.6 mm3. Overall stone-free rate was 76.4%. Fifty-four out of 254 patients (21.3%) required ancillary procedures (shockwave lithotripsy 5.9%, transureteral lithotripsy 10.6%, and repeat PCNL 4.7%). The accuracy and sensitivity of the system in predicting different postoperative variables ranged from 81.0% to 98.2%.
  
  
  
  As a complex nonlinear mathematical model, our ANN system is an interconnected data mining tool, which prospectively analyzes and "learns" the relationships between variables. The accuracy and sensitivity of the system for predicting the stone-free rate, the need for blood transfusion, and post-PCNL ancillary procedures ranged from 81.0% to 98.2%.The stone burden and the stone morphometry were among the most significant preoperative characteristics that affected all postoperative outcome variables and they received the highest relative weight by the ANN system.
authors.affiliation: 1 Department of Urology, Shiraz University of Medical Sciences , Shiraz, Iran .|2 Laparoscopy Research Center, Shiraz University of Medical Sciences , Shiraz, Iran .|3 Division of Urologic Surgery, Department of Surgery, Duke University Medical Center , Durham, North Carolina.
authors.forename: Alireza
authors.initials: A
authors.lastname: Aminsharifi
authors.affiliation: 1 Department of Urology, Shiraz University of Medical Sciences , Shiraz, Iran .
authors.forename: Dariush
authors.initials: D
authors.lastname: Irani
authors.affiliation: 4 Department of Computer Engineering, Yasuj Branch, Islamic Azad University , Yasuj, Iran .
authors.forename: Shima
authors.initials: S
authors.lastname: Pooyesh
authors.affiliation: 5 Department of Computer Engineering, Islamic Azad University , Nourabad, Iran .|6 Elite Club Nourabad Mamasani Branch, Islamic Azad University , Nourabad, Iran .
authors.forename: Hamid
authors.initials: H
authors.lastname: Parvin
authors.affiliation: 7 Imaging Research Center, Shiraz University of Medical Sciences , Shiraz, Iran .
authors.forename: Sakineh
authors.initials: S
authors.lastname: Dehghani
authors.affiliation: 7 Imaging Research Center, Shiraz University of Medical Sciences , Shiraz, Iran .
authors.forename: Khalilolah
authors.initials: K
authors.lastname: Yousofi
authors.affiliation: 1 Department of Urology, Shiraz University of Medical Sciences , Shiraz, Iran .
authors.forename: Ebrahim
authors.initials: E
authors.lastname: Fazel
authors.affiliation: 1 Department of Urology, Shiraz University of Medical Sciences , Shiraz, Iran .
authors.forename: Fatemeh
authors.initials: F
authors.lastname: Zibaie
mesh_terms: D000328:Adult; D001803:Blood Transfusion; D057225:Data Mining; D005260:Female; D006801:Humans; D007669:Kidney Calculi / Q000601:surgery*; D008096:Lithotripsy / Q000379:methods*; D000069550:Machine Learning; D008297:Male; D008875:Middle Aged; D015233:Models, Statistical; D000074642:Nephrolithotomy, Percutaneous / Q000379:methods*; D009403:Nephrostomy, Percutaneous / Q000379:methods*; D016571:Neural Networks, Computer; D011184:Postoperative Period; D011446:Prospective Studies; D015203:Reproducibility of Results; D012984:Software; D014057:Tomography, X-Ray Computed; D016896:Treatment Outcome
keywords: artificial intelligence; artificial neural network; outcome; percutaneous nephrolithotomy; renal calculus; stone