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

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±0.60 mm and 1.89±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.

Medienart:

E-Artikel

Erscheinungsjahr:

2024

Erschienen:

2024

Enthalten in:

Zur Gesamtaufnahme - volume:9

Enthalten in:

IEEE robotics and automation letters - 9(2024), 2 vom: 03. Feb., Seite 1166-1173

Sprache:

Englisch

Beteiligte Personen:

Ge, Jiawei [VerfasserIn]
Kam, Michael [VerfasserIn]
Opfermann, Justin D [VerfasserIn]
Saeidi, Hamed [VerfasserIn]
Leonard, Simon [VerfasserIn]
Mady, Leila J [VerfasserIn]
Schnermann, Martin J [VerfasserIn]
Krieger, Axel [VerfasserIn]

Links:

Volltext

Themen:

Control architectures and programming
Journal Article
Medical robots and systems
Software architecture for robotic and automation

Anmerkungen:

Date Revised 01.02.2024

published: Print-Electronic

Citation Status PubMed-not-MEDLINE

doi:

10.1109/lra.2023.3341773

funding:

Förderinstitution / Projekttitel:

PPN (Katalog-ID):

NLM367827301