An artificial intelligence-based clinical decision support system for large kidney stone treatment

Tayyebe Shabaniyan, Hossein Parsaei, Alireza Aminsharifi, Mohammad Mehdi Movahedi, Amin Torabi Jahromi, Shima Pouyesh, Hamid Parvin

Research output: Contribution to journalArticlepeer-review

16 Scopus citations

Abstract

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.

Original languageEnglish (US)
Pages (from-to)771-779
Number of pages9
JournalAustralasian Physical and Engineering Sciences in Medicine
Volume42
Issue number3
DOIs
StatePublished - Sep 15 2019

All Science Journal Classification (ASJC) codes

  • Biophysics
  • Biomedical Engineering
  • Radiology Nuclear Medicine and imaging
  • Physics and Astronomy(all)

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