February 29, 2024
Amin Torabi Jahromi

Amin Torabi Jahromi

Academic Rank: Assistant professor
Degree: Ph.D in Electrical Engineering
Phone: 09171023389
Faculty: Faculty of Intelligent Systems and Data Science


Title An artificial intelligence-based clinical decision support system for large kidney stone treatment
Type Article
Artificial intelligence Classification Decision support system Kidney stone treatment Stone-free rate prediction
DOI https://doi.org/10.1007/s13246-019-00780-3
Researchers Tayebeh Shabanian (First researcher) , Alireza Aminsharifi (Third researcher) , Mohammadmehdi Movahedi (Fourth researcher) , Amin Torabi Jahromi (Fifth researcher) , Shima Pouyesh (Not in first six researchers) , Hamid Parvin (Not in first six researchers)


A decision support system (DSS) was developed to predict postoperative outcome of a kidney stone treatment procedure, particularly percutaneous nephrolithotomy (PCNL). The system can serve as a promising tool to provide counseling before an operation. The overall procedure includes data collection and prediction model development. Pre/postoperative variables of 254 patients were collected. For feature vector, we used 26 variables from three categories including patient history variables, kidney stone parameters, and laboratory data. The prediction model was developed using machine learning techniques, which includes dimensionality reduction and supervised classification. A novel method based on the combination of sequential forward selection and Fisher’s discriminant analysis was developed to reduce the dimensionality of the feature space and to improve the performance of the system. Multiple classifier scheme was used for prediction. The derived DSS was evaluated by running leave-one-patient-out cross-validation approach on the dataset. The system provided favorable accuracy (94.8%) in predicting the outcome of a treatment procedure. The system also correctly estimated 85.2% of the cases that required stent placement after the removal of a stone. In predicting whether the patient might require a blood transfusion during the surgery or not, the system predicted 95.0% of the cases correctly. The results are promising and show that 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.