Evidence map›Paper›PMID 33458603›Full record

ArticleIEEE transactions on medical robotics and bionics2019

Supervised Autonomous Electrosurgery via Biocompatible Near-Infrared Tissue Tracking Techniques.

H Saeidi, J Ge, M Kam, J D Opfermann, S Leonard, A S Joshi, A Krieger

Open access · greenAbstract read
In one paragraph

Article in IEEE transactions on medical robotics and bionics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.

0numbers the graph read from it
0cells of the map it votes in
13citing papers in PubMed
0.9field-weighted citation impact, top 27% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

13 citing papers in PubMed, 26 citations in OpenAlex.

  1. Article
  2. A Confidence-Based Shared Control Strategy for Robotic Electrosurgery.IEEE transactions on medical robotics and bionics · 2025
    Article
  3. Article
  4. Review
  5. Article
  6. Article
  7. Review
  8. Review
  9. Article
  10. Article
  11. Supervised Autonomous Electrosurgery for Soft Tissue Resection.Proceedings. IEEE International Symposium on Bioinformatics and Bioengineering · 2021
    Article
  12. Article
  13. Autonomous Robot for Removing Superficial Traumatic Blood.IEEE journal of translational engineering in health and medicine · 2021
    Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors at 5 institutions in 1 country.

H SaeidiMechanical Engineering Department, University of Maryland, College Park, MD 20742, USA., Fischell Institute for Biomedical Devices and the Marlene and Stewart Greenebaum Cancer Center.
J GeMechanical Engineering Department, University of Maryland, College Park, MD 20742, USA., Fischell Institute for Biomedical Devices and the Marlene and Stewart Greenebaum Cancer Center.
M KamMechanical Engineering Department, University of Maryland, College Park, MD 20742, USA., Fischell Institute for Biomedical Devices and the Marlene and Stewart Greenebaum Cancer Center.
J D OpfermannSheikh Zayed Institute for Pediatric Surgical Innovation, Childrens National Health System, 111 Michigan Ave. N.W., Washington, DC 20010.
S LeonardElectrical and Computer Science Eng. Dept., Johns Hopkins University, Baltimore, MD 21211.
A S JoshiDivision of Otolaryngology - Head & Neck Surgery at The George Washington University Medical Faculty Associates, 2300 M St. NW 4th Floor, Washington DC 20037.
A KriegerMechanical Engineering Department, University of Maryland, College Park, MD 20742, USA., Fischell Institute for Biomedical Devices and the Marlene and Stewart Greenebaum Cancer Center.
University of Maryland, College Park · USChildren's National · USGeorge Washington University · USJohns Hopkins University · USUniversity of Maryland Marlene and Stewart Greenebaum Comprehensive Cancer Center

Funding

Patient specific 3D printed tissue engineered vascular graft for aortic reconstruction designed by artificial intelligence algorithm.R01HL143468 · NHLBI · UNIVERSITY OF CHICAGO · PI FUGE, MARK, HIBINO, NARUTOSHI · 2018 to 2021
$2.7M
Next Generation of Surgical Imaging and Robotics for Supervised Autonomous Soft Tissue SurgeryR01EB020610 · NIBIB · UNIV OF MARYLAND, COLLEGE PARK · PI HSIEH, MICHAEL HARRISON, KANG, JIN U · 2016 to 2019
$1.5M
Development and Pre-Clinical Evaluation of a Robotic System for Supervised Autonomous Tumor SurgeryR21EB024707 · NIBIB · UNIV OF MARYLAND, COLLEGE PARK · PI KRIEGER, AXEL · 2017 to 2018
$446k
NHLBI NIH HHS R01 HL143468NIBIB NIH HHS R01 EB020610NIBIB NIH HHS R21 EB024707
6 · The paper itself

Abstract

Autonomous robotic surgery systems aim to improve patient outcomes by leveraging the repeatability and consistency of automation and also reducing human induced errors. However, intraoperative autonomous soft tissue tracking and robot control still remains a challenge due to the lack of structure, and high deformability of such tissues. In this paper, we take advantage of biocompatible Near-Infrared (NIR) marking methods and develop a supervised autonomous 3D path planning, filtering, and control strategy for our Smart Tissue Autonomous Robot (STAR) to enable precise and consistent incisions on complex 3D soft tissues. Our experimental results on cadaver porcine tongue samples indicate that the proposed strategy reduces surface incision error and depth incision error by 40.03% and 51.5%, respectively, compared to a teleoperation strategy via da Vinci. Furthermore, compared to an autonomous path planning method with linear interpolation between the NIR markers, the proposed strategy reduces the incision depth error by 48.58% by taking advantage of 3D tissue surface information.

Indexed as

Medical Robots and SystemsSupervised Autonomous ControlSurgical Robotics Planning

Identifiers

PMID33458603
PMCPMC7810241
OpenAlexW2982662787

What OpenQuestion holds

Textmetadata
LicenceTDM
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.