Automatic Microsurgical Skill Assessment Based on Cross-Domain Transfer Learning
Zhang, D., Wu, Z., Chen, J. , Gao, A., Chen, X., Li, P., Wang, Z., Yang, G. 
ORCID: 0000-0002-4826-6719, Lo, B. & Yang, G-Z. (2020).
Automatic Microsurgical Skill Assessment Based on Cross-Domain Transfer Learning.
IEEE Robotics and Automation Letters, 5(3),
pp. 4148-4155.
doi: 10.1109/lra.2020.2989075
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.
| Publication Type: | Article |
|---|---|
| Additional Information: | Copyright © 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. |
| Publisher Keywords: | Microsurgery, Task analysis, Robots, Databases, Random access memory, Measurement |
| Subjects: | Q Science > QA Mathematics > QA75 Electronic computers. Computer science |
| Departments: | School of Science & Technology School of Science & Technology > Department of Computer Science |
| SWORD Depositor: |
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