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Updated: May 8, 2025
Robot-Assisted Kidney Transplantation
Published on: July 19, 2021
Predictive Capacities of a Machine Learning Decision Tree Model Created to Analyse Feasibility of an Open or Robotic
Alessandro Martinino1, Ojus Khanolkar2, Erdem Koyuncu3
1Division of Transplantation, Department of Surgery, University of Illinois at Chicago, Chicago, Illinois, USA.
Background:
Machine learning has emerged as a potent tool in healthcare. A decision tree model was built to improve the decision-making process when determining the optimal choice between an open or robotic surgical approach for kidney transplant.
Methods:
822 patients (OKT) and 169 (RKT) underwent kidney transplantation at our centre during the study period. A decision tree model was built in a two-step process consisting of: (1) Creating the model on the training data and (2) testing the predictive capabilities of the model using the test data.
Results:
Our model correctly predicted an OKT in 148 patients out of 161 test cases who received an OKT (accuracy 91%) and predicted an RKT in 19 out of 25 test cases of patients receiving an RKT (accuracy 76%).
Conclusion:
Our model represents the inaugural data-driven model that furnishes concrete insights for the discernment between employing robotic and open surgery techniques.
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