Skip to main content
AAO-HNSF 2023 Annual Meeting & OTO Experience Main banner
Icon Legend
This session is not in your schedule.
This session is in your schedule. Click again to remove it.

Laryngology/Broncho-Esophagology

Computer Vision–Based Tremor Assessment in Robot-Assisted Microlaryngeal Surgery

Sunday, October 1, 2023
4:27 PM - 4:33 PM East Coast USA Time
Location: 106BC

    Presenting Author(s)

  • Henry H. Joo, BS

    Medical Student
    Johns Hopkins School of Medicine
    Baltimore, Maryland, United States

    • Co-Author(s)

  • SC

    Sue Min Cho, MS

    Ph.D. Student
    Department of Computer Science, Johns Hopkins
    Northbrook, IL, United States

  • MS

    Manish Sahu, PhD

    Department of Computer Science, Johns Hopkins, USA

  • Francis X. Creighton, Jr., MD

    Assistant Professor
    Department of Otolaryngology – Head & Neck Surgery, Johns Hopkins
    Boston, MA, United States

  • Lee M. Akst, MD

    Director, Johns Hopkins Voice Center
    Johns Hopkins University
    Lutherville, MD, United States

  • RT

    Russell H. Taylor, PhD

    Professor
    Department of Computer Science, Johns Hopkins
    Baltimore, Maryland, USA

    • Senior Author(s)

  • DG

    Deepa Galaiya, MD

    Department of Otolaryngology – Head & Neck Surgery, Johns Hopkins, USA

Disclosure(s):
Lee M. Akst, MD: No relevant relationships to disclose.
Introduction: A novel robotic ear, nose, and throat microsurgery system (REMS), previously described by our laboratory, emphasizes cooperative control rather than remote control, of microsurgical instruments. Robot assistance in microlaryngeal procedures has been proposed to improve precision and accuracy, but the potential benefits have not been quantified using a high-fidelity cadaveric model. In this study, we compared the economy of motion and tremor during freehand versus robot-assisted cordotomies in a cadaveric pig model using an automated computer vision-based tool tracking algorithm.

Methods: In this randomized cross-over trial, 36 videos were recorded from six surgeons performing left and right cordotomies on cadaveric pig larynges in three conditions: without robotic assistance, with REMS-assisted scissors, and with REMS-assisted graspers. Economy of motion and tool tremor were quantified using computer vision-based surgical tool tracking algorithms. Wilcoxon rank sum exact tests and multivariable linear regression analyses were used to compare trials. Surveys were administered to assess the robot’s perceived ease of use.

Results: When controlling for tool type, handedness, and level of training, robot assistance decreased the normalized path length (p=0.020) and increased the economy of area compared to freehand (p=0.033). After stratifying by tool type, the normalized path length decreased with robot-assistance for both the grasper (p=0.005) and scissors (p=0.018). Further analysis showed that robot assistance in the nondominant hand decreased the tremor of the scissors compared to freehand (p=0.024). The surgeons rated the robot as relatively easy to use, with an average rating of 3.7 out of 5.

Conclusions: This study is the first of its kind to use computer vision to assess tool motion in simulated microlaryngeal procedures. REMS reduced tremor and increased economy of motion, while still allowing for a traditional operating field compared to remotely operated surgical robots. Our findings suggest that robot assistance may improve the precision and accuracy of microlaryngeal procedures.