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Related Experiment Video

Updated: Jul 1, 2025

Author Spotlight: Revolutionizing Remote Surgery with Augmented Reality and Robotics for Enhanced Precision and Accessibility
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Automatic 3D Augmented-Reality Robot-Assisted Partial Nephrectomy Using Machine Learning: Our Pioneer Experience.

Alberto Piana1, Daniele Amparore1, Michele Sica2

  • 1Division of Urology, Department of Oncology, School of Medicine, University of Turin, San Luigi Hospital, 10043 Turin, Italy.

Cancers
|March 13, 2024
PubMed
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This summary is machine-generated.

A new software, ikidney, uses artificial intelligence (AI) with convolutional neural networks (CNNs) for precise 3D virtual model (3DVM) overlapping during robotic-assisted partial nephrectomy (RAPN). This AI-driven approach enhances surgical accuracy and reduces patient impact.

Area of Science:

  • Medical technology
  • Artificial intelligence in surgery
  • Surgical navigation

Background:

  • Precision surgery aims to minimize patient health impact.
  • Three-dimensional virtual models (3DVMs) offer intraoperative guidance, improving anatomical target visualization and reducing healthy tissue dissection.
  • Augmented reality (AR) guidance in surgery is an evolving field.

Purpose of the Study:

  • To develop and test a novel software, "ikidney", for automatic 3DVM overlapping in the surgical field.
  • To evaluate the efficacy of convolutional neural networks (CNNs) for AR-guided robotic-assisted partial nephrectomy (RAPN).

Main Methods:

  • Development of the "ikidney" software utilizing CNNs for automatic 3DVM generation and overlapping.
  • Enrollment of 13 patients undergoing RAPN between January 2022 and April 2023.
Keywords:
artificial intelligencekidney cancernephron-sparing surgerypartial nephrectomyrenal cell carcinomarobotic surgerythree-dimensional imaging

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  • Collaboration between bioengineers, software developers, and surgeons to create hyper-accurate 3D models for AR-guided RAPN.

    • Main Results:

    • The average anchoring time for 3DVM overlapping was 11 seconds.
    • Temporary co-registration failures occurred in one patient in static and one in dynamic settings.
    • One case required conversion to ultrasound guidance due to a failure, with no major complications recorded (Clavien Dindo > 2).

    Conclusions:

    • The study demonstrates the potential of AI, specifically CNNs, for autonomous 3DVM overlapping in RAPN.
    • The "ikidney" software shows promise in improving the accuracy of 3D superimposition during AR-guided surgery.
    • AI integration offers new possibilities for enhancing clinical practice in precision surgery.