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

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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.

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Acknowledgements

This work has been extracted from parts of the M.Sc. thesis of Tayyebeh Shabaneyan supported by the Research Council of Shiraz University of Medical Sciences under Grant Number 95-01-01-11983. The authors wish to thank Mr. H. Argasi at the Research Consultation Center (RCC) of the Shiraz University of Medical Sciences, for his invaluable assistance in editing this manuscript.

Funding

This study was funded by Shiraz University of Medical Sciences (Grant # 95-01-01-11983).

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Authors and Affiliations

  1. Department of Medical Physics and Engineering, School of Medicine, Shiraz University of Medical Sciences, Shiraz, Iran

    Tayyebe Shabaniyan, Hossein Parsaei & Mohammad Mehdi Movahedi

  2. Shiraz Neuroscience Research Center, Shiraz University of Medical Sciences, Shiraz, Iran

    Hossein Parsaei

  3. Department of Urology, School of Medicine, Shiraz University of Medical Sciences, Shiraz, Iran

    Alireza Aminsharifi

  4. Electrical and Electronic Engineering Group, Engineering College, Persian Gulf University, Bushehr, Iran

    Amin Torabi Jahromi

  5. Department of Computer Engineering, Islamic Azad University, Yasooj, Iran

    Shima Pouyesh

  6. Department of Computer Engineering, Islamic Azad University, Nourabad Mamasani, Iran

    Hamid Parvin

Authors
  1. Tayyebe Shabaniyan
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  2. Hossein Parsaei
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  3. Alireza Aminsharifi
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  4. Mohammad Mehdi Movahedi
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  5. Amin Torabi Jahromi
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  6. Shima Pouyesh
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  7. Hamid Parvin
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Corresponding author

Correspondence to Hossein Parsaei.

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Conflicts of interest

The authors declare that they have no relevant conflict of interest.

Ethical Approval

For the part of this study that we used data of patients, all the procedures performed in this work involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.

Research involving human participants and/or animals

This study was conducted according to the Ethics Committee of Human Experimentation (ECHE) of Shiraz University of Medical Sciences. Owing to this type of this study that patients were not directly involved, the requirement for obtaining written informed consent from each patient was waived by the ECHE of the Shiraz University of Medical Sciences.

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Appendix 1: Preoperative, intraoperative, and postoperative data for all patients

Appendix 1: Preoperative, intraoperative, and postoperative data for all patients

See Tables 8 and 9.

Table 8 Preoperative and intraoperative variables for 254 patients included in this work
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Table 9 Postoperative outcomes for 254 patients studied
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Shabaniyan, T., Parsaei, H., Aminsharifi, A. et al. An artificial intelligence-based clinical decision support system for large kidney stone treatment. Australas Phys Eng Sci Med 42, 771–779 (2019). https://doi.org/10.1007/s13246-019-00780-3

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  • DOI: https://doi.org/10.1007/s13246-019-00780-3

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