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Author Spotlight: Revolutionizing Remote Surgery with Augmented Reality and Robotics for Enhanced Precision and Accessibility

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Computer Vision and Machine-Learning Techniques for Automatic 3D Virtual Images Overlapping During Augmented Reality

Daniele Amparore¹, Michele Sica¹, Paolo Verri¹

¹Division of Urology, Dept. of Oncology, School of Medicine, University of Turin, San Luigi Hospital, Orbassano (Turin), Italy.

Technology in Cancer Research & Treatment

|February 20, 2024

Summary

This summary is machine-generated.

This study developed software for automatically overlaying 3D kidney models during robotic surgery. Computer vision and convolutional neural networks show promise for improving augmented reality guidance in partial nephrectomy.

Keywords:

artificial intelligence kidney cancer nephron-sparing surgery renal cell carcinoma robotic surgery three-dimensional imaging

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Mixed Reality Assisted Radical Endoscopic Thyroidectomy

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Published on: January 31, 2025

Area of Science:

Medical Engineering
Robotic Surgery
Medical Imaging

Background:

Minimally-invasive partial nephrectomy utilizes 3D models for augmented reality (AR) robotic surgery guidance.
Current co-registration of virtual models to the operative field is manual, limiting efficiency.

Purpose of the Study:

To develop software for automatic integration and overlay of 3D kidney models onto the Da Vinci robotic console.
To assist surgeons by providing real-time augmented reality guidance during robotic interventions.

Main Methods:

Two strategies were explored: computer vision with Indocyanine green (ICG) enhancement and convolutional neural network (CNN) processing of live endoscopic images.
Hyper-accurate 3D models were created for automatic 3D-AR-guided robot-assisted partial nephrectomy (RAPN).
Prospective study involving 20 patients (12 in Group A, 8 in Group B).

Main Results:

Both technologies demonstrated successful 3D model co-registration.
Computer vision with ICG achieved an average co-registration time of 7 seconds; CNN achieved 11 seconds.
No major intraoperative or postoperative complications were observed, with comparable functional outcomes between groups.

Conclusions:

Computer vision with ICG enabled successful 3D model anchoring, requiring minor manual adjustments.
CNN improved automatic kidney detection and boundary identification without ICG, showing potential for enhanced AR guidance.
Further research is necessary to validate these preliminary findings.