Automating Surgical Peg Transfer: Calibration With Deep Learning Can Exceed Speed, Accuracy, and Consistency of Humans-Reference-Cited by-同舟云学术

Automating Surgical Peg Transfer: Calibration With Deep Learning Can Exceed Speed, Accuracy, and Consistency of Humans

Author:

Hwang Minho¹^ORCID,Ichnowski Jeffrey²,Thananjeyan Brijen²^ORCID,Seita Daniel²,Paradis Samuel²,Fer Danyal³,Low Thomas⁴^ORCID,Goldberg Ken²^ORCID

Affiliation:

1. Department of Robotics Engineering, Daegu Gyeongbuk Institute of Science and Technology (DGIST), Daegu, Republic of Korea

2. Department of Electrical Engineering and Computer Sciences, University of California at Berkeley, Berkeley, CA, USA

3. Department of General Surgery, University of California at San Francisco, San Francisco Bay Area, Oakland, CA, USA

4. Robotics Laboratory, SRI International, Menlo Park, CA, USA

Funder

Technology and Advanced Telemedicine Research Center

Siemens

Google

Toyota Research Institute

National Science Foundation through the National Robotics Initiative

collaborative research project “Software Framework for Research in Semi-Autonomous Teleoperation” between Johns Hopkins University

Worcester Polytechnic Institute

University of Washington

(da Vinci Research Kit)

Graduate Fellowships for Science, Technology, Engineering, and Mathematics Diversity

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Control and Systems Engineering

Link

Reference47 articles.

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