A machine learning approach to predict surgical learning curves.

Journal: Surgery
Published Date:

Abstract

BACKGROUND: Contemporary surgical training programs rely on the repetition of selected surgical motor tasks. Such methodology is inherently open ended with no control on the time taken to attain a set level of proficiency, given the trainees' intrinsic differences in initial skill levels and learning abilities. Hence, an efficient training program should aim at tailoring the surgical training protocols to each trainee. In this regard, a predictive model using information from the initial learning stage to predict learning curve characteristics should facilitate the whole surgical training process.

Authors

  • Yuanyuan Gao
  • Uwe Kruger
  • Xavier Intes
    Center for Modeling, Simulation and Imaging in Medicine, Rensselaer Polytechnic Institute, Troy, NY; Department of Biomedical Engineering, Rensselaer Polytechnic Institute, Troy, NY.
  • Steven Schwaitzberg
    Jacobs School of Medicine and Biomedical Sciences, The State University of New York, Buffalo, NY; Department of Surgery, The State University of New York, Buffalo, NY; Buffalo General Hospital, NY.
  • Suvranu De
    Rensselaer Polytechnic Institute, Troy, New York, USA.