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Automatic microsurgical skill assessment based on cross-domain transfer learning

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Title: Automatic microsurgical skill assessment based on cross-domain transfer learning
Authors: Zhang, D
Wu, Z
Chen, J
Gao, A
Chen, X
Li, P
Wang, Z
Yang, G
Lo, B
Yang, G-Z
Item Type: Journal Article
Abstract: The assessment of microsurgical skills for Robot-Assisted Microsurgery (RAMS) still relies primarily on subjective observations and expert opinions. A general and automated evaluation method is desirable. Deep neural networks can be used for skill assessment through raw kinematic data, which has the advantages of being objective and efficient. However, one of the major issues of deep learning for the analysis of surgical skills is that it requires a large database to train the desired model, and the training process can be time-consuming. This letter presents a transfer learning scheme for training a model with limited RAMS datasets for microsurgical skill assessment. An in-house Microsurgical Robot Research Platform Database (MRRPD) is built with data collected from a microsurgical robot research platform (MRRP). It is used to verify the proposed cross-domain transfer learning for RAMS skill level assessment. The model is fine-tuned after training with the data obtained from the MRRP. Moreover, microsurgical tool tracking is developed to provide visual feedback while task-specific metrics and the other general evaluation metrics are provided to the operator as a reference. The method proposed has shown to offer the potential to guide the operator to achieve a higher level of skills for microsurgical operation.
Issue Date: 1-Jul-2020
Date of Acceptance: 7-Apr-2020
URI: http://hdl.handle.net/10044/1/88073
DOI: 10.1109/LRA.2020.2989075
ISSN: 2377-3766
Publisher: Institute of Electrical and Electronics Engineers
Start Page: 4148
End Page: 4155
Journal / Book Title: IEEE Robotics and Automation Letters
Volume: 5
Issue: 3
Copyright Statement: © 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Sponsor/Funder: Engineering & Physical Science Research Council (EPSRC)
Funder's Grant Number: EP/P012779/1
Keywords: Science & Technology
Technology
Robotics
Microsurgical skill analysis
transfer learning
WORKSPACE ANALYSIS
PERFORMANCE
TRACKING
METRICS
Science & Technology
Technology
Robotics
Microsurgical skill analysis
transfer learning
WORKSPACE ANALYSIS
PERFORMANCE
TRACKING
METRICS
0913 Mechanical Engineering
Publication Status: Published
Online Publication Date: 2020-04-20
Appears in Collections:Department of Surgery and Cancer
Computing
Faculty of Medicine
Institute of Global Health Innovation