IDEAS home Printed from https://ideas.repec.org/a/plo/pone00/0229596.html
   My bibliography  Save this article

The Virtual Operative Assistant: An explainable artificial intelligence tool for simulation-based training in surgery and medicine

Author

Listed:
  • Nykan Mirchi
  • Vincent Bissonnette
  • Recai Yilmaz
  • Nicole Ledwos
  • Alexander Winkler-Schwartz
  • Rolando F Del Maestro

Abstract

Simulation-based training is increasingly being used for assessment and training of psychomotor skills involved in medicine. The application of artificial intelligence and machine learning technologies has provided new methodologies to utilize large amounts of data for educational purposes. A significant criticism of the use of artificial intelligence in education has been a lack of transparency in the algorithms’ decision-making processes. This study aims to 1) introduce a new framework using explainable artificial intelligence for simulation-based training in surgery, and 2) validate the framework by creating the Virtual Operative Assistant, an automated educational feedback platform. Twenty-eight skilled participants (14 staff neurosurgeons, 4 fellows, 10 PGY 4–6 residents) and 22 novice participants (10 PGY 1–3 residents, 12 medical students) took part in this study. Participants performed a virtual reality subpial brain tumor resection task on the NeuroVR simulator using a simulated ultrasonic aspirator and bipolar. Metrics of performance were developed, and leave-one-out cross validation was employed to train and validate a support vector machine in Matlab. The classifier was combined with a unique educational system to build the Virtual Operative Assistant which provides users with automated feedback on their metric performance with regards to expert proficiency performance benchmarks. The Virtual Operative Assistant successfully classified skilled and novice participants using 4 metrics with an accuracy, specificity and sensitivity of 92, 82 and 100%, respectively. A 2-step feedback system was developed to provide participants with an immediate visual representation of their standing related to expert proficiency performance benchmarks. The educational system outlined establishes a basis for the potential role of integrating artificial intelligence and virtual reality simulation into surgical educational teaching. The potential of linking expertise classification, objective feedback based on proficiency benchmarks, and instructor input creates a novel educational tool by integrating these three components into a formative educational paradigm.

Suggested Citation

  • Nykan Mirchi & Vincent Bissonnette & Recai Yilmaz & Nicole Ledwos & Alexander Winkler-Schwartz & Rolando F Del Maestro, 2020. "The Virtual Operative Assistant: An explainable artificial intelligence tool for simulation-based training in surgery and medicine," PLOS ONE, Public Library of Science, vol. 15(2), pages 1-15, February.
  • Handle: RePEc:plo:pone00:0229596
    DOI: 10.1371/journal.pone.0229596
    as

    Download full text from publisher

    File URL: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0229596
    Download Restriction: no

    File URL: https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0229596&type=printable
    Download Restriction: no

    File URL: https://libkey.io/10.1371/journal.pone.0229596?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Kay M Stanney & JoAnn Archer & Anna Skinner & Charis Horner & Claire Hughes & Nicholas P Brawand & Eric Martin & Stacey Sanchez & Larry Moralez & Cali M Fidopiastis & Ray S Perez, 2022. "Performance gains from adaptive eXtended Reality training fueled by artificial intelligence," The Journal of Defense Modeling and Simulation, , vol. 19(2), pages 195-218, April.
    2. Rosch-Grace, Dominic & Straub, Jeremy, 2022. "Analysis of the likelihood of quantum computing proliferation," Technology in Society, Elsevier, vol. 68(C).
    3. Volkmar, Gioia & Fischer, Peter M. & Reinecke, Sven, 2022. "Artificial Intelligence and Machine Learning: Exploring drivers, barriers, and future developments in marketing management," Journal of Business Research, Elsevier, vol. 149(C), pages 599-614.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:plo:pone00:0229596. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: plosone (email available below). General contact details of provider: https://journals.plos.org/plosone/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.