Creating a Digital Twin of Spinal Surgery: A Proof of Concept

TL;DR

This paper demonstrates a proof of concept for creating a digital twin of spinal surgery by integrating multiple imaging and tracking technologies to digitally replicate the surgical scene, aiding training and automation.

Contribution

It introduces a novel multi-sensor approach for surgical scene digitalization, combining RGB-D cameras, infrared stereo, and laser scanning for comprehensive modeling.

Findings

High-quality 3D reconstruction of surgical scenes

Effective integration of multiple imaging modalities

Potential for automated digital twin development

Abstract

Surgery digitalization is the process of creating a virtual replica of real-world surgery, also referred to as a surgical digital twin (SDT). It has significant applications in various fields such as education and training, surgical planning, and automation of surgical tasks. In addition, SDTs are an ideal foundation for machine learning methods, enabling the automatic generation of training data. In this paper, we present a proof of concept (PoC) for surgery digitalization that is applied to an ex-vivo spinal surgery. The proposed digitalization focuses on the acquisition and modelling of the geometry and appearance of the entire surgical scene. We employ five RGB-D cameras for dynamic 3D reconstruction of the surgeon, a high-end camera for 3D reconstruction of the anatomy, an infrared stereo camera for surgical instrument tracking, and a laser scanner for 3D reconstruction of the operating room and data fusion. We justify the proposed methodology, discuss the challenges faced and further extensions of our prototype. While our PoC partially relies on manual data curation, its high quality and great potential motivate the development of automated methods for the creation of SDTs.