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Autonomous System for Tumor Resection (ASTR) - Dual-Arm Robotic Midline Partial Glossectomy

  1. Author:
    Ge, Jiawei
    Kam, Michael
    Opfermann, Justin D
    Saeidi, Hamed
    Leonard, Simon
    Mady, Leila J
    Schnermann,Martin
    Krieger, Axel
  2. Author Address

    Department of Mechanical Engineering, Johns Hopkins University, Baltimore, MD 21211 USA., Department of Computer Science, University of North Carolina Wilmington, Wilmington, NC 28403, USA., Department of Electrical and Computer Engineering, Johns Hopkins University, Baltimore, MD 21211, USA., Department of Otolaryngology - Head and Neck Surgery, Johns Hopkins School of Medicine, Johns Hopkins University, Baltimore, MD 21287, USA., Chemical Biology Laboratory, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Frederick, MD 21702, USA.,
    1. Year: 2024
    2. Date: Feb
    3. Epub Date: 2023 12 12
  1. Journal: IEEE Robotics and Automation Letters
    1. 9
    2. 2
    3. Pages: 1166-1173
  2. Type of Article: Article
  1. Abstract:

    Head and neck cancers are the seventh most common cancers worldwide, with squamous cell carcinoma being the most prevalent histologic subtype. Surgical resection is a primary treatment modality for many patients with head and neck squamous cell carcinoma, and accurately identifying tumor boundaries and ensuring sufficient resection margins are critical for optimizing oncologic outcomes. This study presents an innovative autonomous system for tumor resection (ASTR) and conducts a feasibility study by performing supervised autonomous midline partial glossectomy for pseudotumor with millimeter accuracy. The proposed ASTR system consists of a dual-camera vision system, an electrosurgical instrument, a newly developed vacuum grasping instrument, two 6-DOF manipulators, and a novel autonomous control system. The letter introduces an ontology-based research framework for creating and implementing a complex autonomous surgical workflow, using the glossectomy as a case study. Porcine tongue tissues are used in this study, and marked using color inks and near-infrared fluorescent (NIRF) markers to indicate the pseudotumor. ASTR actively monitors the NIRF markers and gathers spatial and color data from the samples, enabling planning and execution of robot trajectories in accordance with the proposed glossectomy workflow. The system successfully performs six consecutive supervised autonomous pseudotumor resections on porcine specimens. The average surface and depth resection errors measure 0.73 177;0.60 mm and 1.89 177;0.54 mm, respectively, with no positive tumor margins detected in any of the six resections. The resection accuracy is demonstrated to be on par with manual pseudotumor glossectomy performed by an experienced otolaryngologist.

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External Sources

  1. DOI: 10.1109/lra.2023.3341773
  2. PMID: 38292408
  3. PMCID: PMC10824540

Library Notes

  1. Fiscal Year: FY2023-2024
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