Validation of a novel, low-fidelity virtual reality simulator and an artificial intelligence assessment approach for peg transfer laparoscopic training.

Journal: Scientific reports
PMID: 39030307

Abstract

Simulators are widely used in medical education, but objective and automatic assessment is not feasible with low-fidelity simulators, which can be solved with artificial intelligence (AI) and virtual reality (VR) solutions. The effectiveness of a custom-made VR simulator and an AI-based evaluator of a laparoscopic peg transfer exercise was investigated. Sixty medical students were involved in a single-blinded randomised controlled study to compare the VR simulator with the traditional box trainer. A total of 240 peg transfer exercises from the Fundamentals of Laparoscopic Surgery programme were analysed. The experts and AI-based software used the same criteria for evaluation. The algorithm detected pitfalls and measured exercise duration. Skill improvement showed no significant difference between the VR and control groups. The AI-based evaluator exhibited 95% agreement with the manual assessment. The average difference between the exercise durations measured by the two evaluation methods was 2.61 s. The duration of the algorithmic assessment was 59.47 s faster than the manual assessment. The VR simulator was an effective alternative practice compared with the training box simulator. The AI-based evaluation produced similar results compared with the manual assessment, and it could significantly reduce the evaluation time. AI and VR could improve the effectiveness of basic laparoscopic training.

Authors

  • Peter Zoltan Bogar
    3D Printing and Visualisation Centre, University of Pecs, Medical School, Boszorkany Str. 2, Pecs, 7624, Hungary.
  • Mark Virag
    3D Printing and Visualisation Centre, University of Pecs, Medical School, Boszorkany Str. 2, Pecs, 7624, Hungary.
  • Matyas Bene
    3D Printing and Visualisation Centre, University of Pecs, Medical School, Boszorkany Str. 2, Pecs, 7624, Hungary.
  • Peter Hardi
    Medical Skills Education and Innovation Centre, Medical School, University of Pecs, Szigeti Str. 12, Pecs, 7624, Hungary.
  • András Matuz
    Department of Behavioural Sciences, Medical School, University of Pécs, Szigeti Str. 12, Pécs, 7624, Hungary. andras.matuz@aok.pte.hu.
  • Adam Tibor Schlegl
    Medical Skills Education and Innovation Centre, Medical School, University of Pecs, Szigeti Str. 12, Pecs, 7624, Hungary.
  • Luca Tóth
    Általános Orvostudományi Kar, Klinikai Központ, Idegsebészeti Klinika,Pécsi Tudományegyetem, Pécs.
  • Ferenc Molnar
    Medical Skills Education and Innovation Centre, Medical School, University of Pecs, Szigeti Str. 12, Pecs, 7624, Hungary.
  • Balint Nagy
    Medical Skills Education and Innovation Centre, Medical School, University of Pecs, Szigeti Str. 12, Pecs, 7624, Hungary.
  • Szilard Rendeki
    Medical Skills Education and Innovation Centre, Medical School, University of Pecs, Szigeti Str. 12, Pecs, 7624, Hungary.
  • Krisztina Berner-Juhos
    Department of Surgical Research and Techniques, Heart and Vascular Centre, Semmelweis University, Nagyvarad Square 4, Budapest, 1089, Hungary.
  • Andrea Ferencz
    Heart and Vascular Center, Semmelweis University, Budapest, Hungary.
  • Krisztina Fischer
    Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, 75 Francis St, Boston, MA, 02115, USA.
  • Péter Maróti
    3D Oktatási és Vizualizációs Központ,Pécsi Tudományegyetem, Pécs.

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