Uncertainty-Aware Self-Supervised Learning for Cross-Domain Technical Skill Assessment in Robot-Assisted Surgery-Reference-Cited by-同舟云学术

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Uncertainty-Aware Self-Supervised Learning for Cross-Domain Technical Skill Assessment in Robot-Assisted Surgery

Published:2023-05 Issue:2 Volume:5 Page:301-311
ISSN:2576-3202
Container-title:IEEE Transactions on Medical Robotics and Bionics
language:
Short-container-title:IEEE Trans. Med. Robot. Bionics

Author:

Wang Ziheng¹,Mariani Andrea²^ORCID,Menciassi Arianna²^ORCID,De Momi Elena³^ORCID,Fey Ann Majewicz⁴

Affiliation:

1. Department of Mechanical Engineering, The University of Texas at Dallas, Richardson, TX, USA

2. The BioRobotics Institute and the Department of Excellence in Robotics & AI, Scuola Superiore Sant’Anna, Pisa, Italy

3. Department of Electronics, Information and Bioengineering, Politecnico di Milano, Milan, Italy

4. Department of Mechanical Engineering, The University of Texas at Austin, Austin, TX, USA

Funder

Intuitive Surgical Technology Research Grant

National Science Foundation

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Artificial Intelligence,Control and Optimization,Computer Science Applications,Human-Computer Interaction,Biomedical Engineering

Link

https://ieeexplore.ieee.org/ielam/8253409/10133907/10121491-aam.pdf

Reference51 articles.

1. Current status of robotic simulators in acquisition of robotic surgical skills

2. Accelerating Surgical Robotics Research: A Review of 10 Years With the da Vinci Research Kit

3. Current status of validation for robotic surgery simulators - a systematic review

4. Does Training on a Virtual Reality Robotic Simulator Improve Performance on the da Vinci® Surgical System?

5. Constructing a validity argument for the Objective Structured Assessment of Technical Skills (OSATS): a systematic review of validity evidence

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1. Transforming Surgical Training: A Systematic Review of AI Techniques for Assessment and Evaluation (Preprint);2024-03-29

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