Application of Artificial Intelligence-Based Classifiers to Predict the Outcome Measures and Stone-Free Status Following Percutaneous Nephrolithotomy for Staghorn Calculi: Cross-Validation of Data and Estimation of Accuracy

Abstract

Objective: To develop a decision support system (DSS) for the prediction of the postoperative outcome of a kidney stone treatment procedure, particularly percutaneous nephrolithotomy (PCNL) to serve as a promising tool to provide counseling before an operation. Materials and Methods: The overall procedure includes data collection and prediction model development. Pre-/postoperative variables of 100 patients with staghorn calculus, who underwent PCNL, were collected. For feature vector, variables and categories including patient history variables, kidney stone parameters, and laboratory data were considered. The prediction model was developed using machine learning techniques, which include dimensionality reduction and supervised classification. Multiple classifier scheme was used for prediction. The derived DSS was evaluated by running the leave-one-patient-out cross-validation approach on the data set. Results: The system provided favorable accuracy (81%) in predicting the outcome of a treatment procedure. Performance in predicting the stone-free rate with the Minimum Redundancy Maximum Relevance feature (MRMR) treatment extracting top 3 features using Random Forest (RF) was 67%, with MRMR treatment extracting top 5 features using RF was 63%, and with MRMR treatment extracting top 10 features using Decision Tree was 62%. The statistical significance using standard error between the best area under the curves (AUCs) obtained from the Linear Discriminant Analysis (LDA) and MRMR. The results obtained from the LDA approach (0.81 AUC) was statistically significant ( p = 0.027, z = 2.21) from the MRMR (0.64 AUC) ( p = 0.05). Conclusion:The promising results of the developed DSS could be used in assisting urologists to provide counseling, predict a surgical outcome, and ultimately choose an appropriate surgical treatment for removing kidney stones.

Artificial intelligence in endourology: emerging technology for individualized care

“…Hameed et al developed several decision support models to specifically predict stone- free rate for staghorn stones. The optimal model incorporating linear discriminant analysis and random forest classifier was 81% accurate [52]. Similar models predicting PCNL outcomes are summarized in Table 3.…”

A retrospective cohort study on the use of machine learning to predict stone-free status following percutaneous nephrolithotomy: An experience from Saudi Arabia

“…Furthermore, some authors have successfully developed ML-based decision support systems (DSS) for the prediction of the postoperative outcomes of PCNL [ 28 , 29 ]. It has been established that DSS can serve as a useful tool to give clinicians and surgeons more insight into the patient's condition and allow them to provide counseling preoperatively, predict post-PCNL outcomes, and ultimately choose the appropriate surgical management for the patient.…”

Human Factors and Technological Characteristics Influencing the Interaction of Medical Professionals With Artificial Intelligence–Enabled Clinical Decision Support Systems: Literature Review

“…Owing to technological innovations, health care technologies, including CDSSs, are increasingly enabled by artificial intelligence (AI) [22]. The first evaluation of an AI-enabled CDSS promises increased performance and accuracy compared with a conventional CDSS [23]. In addition, clinicians and experts in the field generally expect simplification of organizational processes, such as patient flows, with the advent of AI [24].…”