Artificial Neural Network System to Predict the Postoperative Outcome of Percutaneous Nephrolithotomy

Abstract

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.

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

“…29 In 2017, we reported the accuracy of an artificial neural network (ANN) algorithm (ranging between 81.0% and 98.2%, AUC = 0.861) in predicting SFR, the need for post-PCNL ancillary procedures, and the need for blood transfusion. 6 Supervised learning algorithms have previously been used to adjust weight vectors and classifiers. 6 In this study, we set out to validate the accuracy of our adequately trained SVMbased software in processing prospective data from a new cohort.…”

“…The machine learning technique used for data analysis, classification, and regression as well as for identifying the connections between input and output variables is based on the support vector machine (SVM) model. 6,9,10 The SVM networks have an efficient training phase and are accurate, especially for clean datasets with well-defined input and output variables. 9,10 Figure 2A-C illustrate this model when two or more variables are to be classified and analyzed.…”

“…Recently, our group designed a machine learning-based system to predict post-PCNL outcomes such as SFR and the need for ancillary procedures. 6 More than 20 preoperative and intraoperative variables and their relative weights are involved in this intelligent network. Preliminary work showed that this system showed promising accuracy (81.0%-98.2%) in predicting stone-free status and the need for blood transfusion or ancillary procedures.…”

“…Preliminary work showed that this system showed promising accuracy (81.0%-98.2%) in predicting stone-free status and the need for blood transfusion or ancillary procedures. 6 Based on machine learning algorithms, we then designed software and a userfriendly interface (Fig. 1).…”

Precision Medicine, Artificial Intelligence, and Genomic Markers in Urology. Do we need to Tailor our Clinical Practice?

“…One of them used the information on 454 patients (200 for training set and 254 for test set), to assess the relevance of clinical preoperative parameters on postoperative results (PCNL) by comparing them to the actual (observed) outcomes; the accuracy and sensitivity of the system was found to range between 81% and 98.2%, and it was able to predict stone-free rates with an accuracy of 86%. 53 Stone-free rates after SWL were also evaluated by an ANN system based on information of 139 patients that was able to predict this outcome with an accuracy of 88.7% 54 Finally, it is due to highlight the role of AI in surgical training. It has been studied mainly in robotic and laparoscopic surgery, with emphasis in anatomical landmark recognition as a fundamental step in automated surgery.…”