Alireza Aminsharifi1,2,3, Dariush Irani1, Shima Pooyesh4, Hamid Parvin5,6, Sakineh Dehghani7, Khalilolah Yousofi7, Ebrahim Fazel1, Fatemeh Zibaie1. 1. 1 Department of Urology, Shiraz University of Medical Sciences , Shiraz, Iran . 2. 2 Laparoscopy Research Center, Shiraz University of Medical Sciences , Shiraz, Iran . 3. 3 Division of Urologic Surgery, Department of Surgery, Duke University Medical Center , Durham, North Carolina. 4. 4 Department of Computer Engineering, Yasuj Branch, Islamic Azad University , Yasuj, Iran . 5. 5 Department of Computer Engineering, Islamic Azad University , Nourabad, Iran . 6. 6 Elite Club Nourabad Mamasani Branch, Islamic Azad University , Nourabad, Iran . 7. 7 Imaging Research Center, Shiraz University of Medical Sciences , Shiraz, Iran .
Abstract
PURPOSE: 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. METHODS: 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. RESULTS: 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%. CONCLUSION: 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.
PURPOSE: 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. METHODS: 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. RESULTS: 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%. CONCLUSION: 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: B M Zeeshan Hameed; Aiswarya V L S Dhavileswarapu; Syed Zahid Raza; Hadis Karimi; Harneet Singh Khanuja; Dasharathraj K Shetty; Sufyan Ibrahim; Milap J Shah; Nithesh Naik; Rahul Paul; Bhavan Prasad Rai; Bhaskar K Somani Journal: J Clin Med Date: 2021-04-26 Impact factor: 4.241