Streamlining microsurgical procedures: a phantom trial of an artificial intelligence-driven robotic microscope assistant.

Objective: Surgical microscopes are essential in microsurgery for magnification, focus, and illumination. However, surgeons must frequently adjust the microscope manually-typically via a handgrip or mouth switch-to maintain a well-centered view that ensures clear visibility of the operative field and surrounding anatomy. These frequent adjustments can disrupt surgical workflow, increase cognitive load, and divert surgeons' focus from their surgical task. To address these challenges, the authors introduced and evaluated a novel robotic assistance system that leverages AI to automatically detect the surgical area of interest by localizing surgical instrument tips and robotically recentering the microscope's field of view.

Methods: This preclinical user study with 19 neurosurgeons compared the robotic assistance system with state-of-the-art microscope controls, i.e., a handgrip and mouth switch. Participants engaged in a custom-designed microsurgical scenario involving a phantom-based anastomosis requiring frequent microscope adjustments. Task load related to microscope handling was assessed using the National Aeronautics and Space Administration Task Load Index questionnaire, and efficiency and workflow compatibility were analyzed based on suturing time and interruption frequency. To evaluate the effectiveness of the robotic assistance system in maintaining a centered view, heat maps that visualize the areas where surgeons operated with their instrument tips were computed.

Results: The robotic assistance system significantly reduced microscope-associated task load compared to the handgrip, decreasing physical (r = 0.59, p < 0.001) and temporal (r = 0.49, p = 0.022) demand while enhancing microscope handling performance (r = 0.40, p = 0.003). In comparison to the mouth switch, reductions in physical (r = 0.45, p = 0.002) and mental (r = 0.32, p = 0.031) demand were observed, alongside performance improvements (r = 0.41, p = 0.008). Furthermore, robotic assistance increased effective suturing time by approximately 10% (r = 0.90, p < 0.001), reduced interruptions (r = 0.52, p = 0.035), and enabled faster reaction times when readjusting the microscope (r = 0.68, p = 0.005) in contrast to the handgrip. According to the heat map analysis, the robotic assistance system consistently promoted a more centered microscope view compared with manual controls.

Conclusions: The novel robotic assistance system enhances microsurgical efficiency by AI-assisted microscope adjustments, thereby reducing task load and streamlining workflow. Compared to manual microscope control, automating microscope adjustments minimizes distractions and task switching, allowing surgeons to maintain a consistently centered view of the operative field. Future studies should focus on clinical validation in live surgeries.