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A Mixed Reality Approach To Patient-specific Robotic Thoracic Surgery Planning
Philipp Feodorovici1, Jan Arensmeyer
1, Henrik Bonsmann
1, An-Nhien Vo
1, Hruy Menghesha
2, Philipp Schnorr
2, Donatas Zalepugas
1, Joachim Schmidt
1.
1University Hospital Bonn, Bonn, Germany,
2Helios Hospital Bonn/Rhein-Sieg, Bonn, Germany.
BACKGROUND:Robotic surgery is becoming the gold standard in minimally invasive thoracic procedures. Despite technological advances, these robotic systems still have clinically relevant mechanical limits to where they can operate without collision. Good access to anatomical structures is highly dependent on trocar positioning, with the coordinated work of the surgical team benefiting from good placement of the entire system in the OR. Standards for proper trocar positioning and overall system alignment have been developed over many years of thoracic surgery practice. However, certain procedures, and especially individual patient anatomy, may require a deviation from these standards. Adjustments in trocar position or orientation of the systems in the room are based on the cognitive spatial imagination of the experienced surgeon. The same challenge arises with new robotic assistance systems entering the market, where such standards have yet to be worked out. The aim of this work was to develop a system for patient- and procedure-specific planning for robot-assisted surgery inside a mixed reality (MR) environment.
METHODS: An MR application incorporating 3D scans of the Dexter surgical robot (Distalmotion, Lausanne, Switzerland), accurately scaled and visualized using an Apple Vision Pro (Apple Inc., Cupertino, CA, USA) headset was developed. Patient-specific anatomy was reconstructed from chest CT images. Automated lung segmentation and deformations provided anatomically accurate models. The virtual dimensions and constraints of the robot were verified with real measurements. Validation of the digital planning was performed on a life-size, multi-filament 3D-printed thorax model, simulating various surgical scenarios.
RESULTS:Physical parameters of the actual surgical robot and its virtual model showed close alignment. The MR simulation accurately replicated operative conditions, spatial relationships, and target anatomy reachability. Tests on the synthetic thoracic model correlated well with virtual predictions, confirming the system’s feasibility and accuracy.
CONCLUSIONS:Our MR-based robotic planning system enables patient- and procedure-specific preoperative simulations, potentially improving the introduction and utilization of novel robotic platforms. By providing tailored guidance in trocar placement and system orientation, it may enhance surgical training, support adherence to ideal operative parameters, and ultimately improve patient safety and outcomes—particularly in complex thoracic surgeries.
LEGEND: A-B: Preoperative MR planning, C-D: Intraoperative alignment on 3D-printed replica.
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