Surgeon Technical Skill Assessment using Computer Vision based Analysis

Hei Law, Khurshid Ghani, Jia Deng
Proceedings of the 2nd Machine Learning for Healthcare Conference, PMLR 68:88-99, 2017.

Abstract

In this paper, we propose a computer vision based method to assess the technical skill level of surgeons by analyzing the movement of robotic instruments in robotic surgical videos. First, our method leverages the power of crowd workers on the internet to obtain high quality data in a scalable and cost-efficient way. Second, we utilize the high quality data to train an accurate and efficient robotic instrument tracker based on the state-of-the-art Hourglass Networks. Third, we assess the movement of the robotic instruments and automatically classify the technical level of a surgeon with a linear classifier, using peer evaluations of skill as the reference standard. Since the proposed method relies only on video data, this method has the potential to be transferred to other minimally invasive surgical procedures.

Cite this Paper

BibTeX
@InProceedings{pmlr-v68-law17a, title = {Surgeon Technical Skill Assessment using Computer Vision based Analysis}, author = {Law, Hei and Ghani, Khurshid and Deng, Jia}, booktitle = {Proceedings of the 2nd Machine Learning for Healthcare Conference}, pages = {88--99}, year = {2017}, editor = {Doshi-Velez, Finale and Fackler, Jim and Kale, David and Ranganath, Rajesh and Wallace, Byron and Wiens, Jenna}, volume = {68}, series = {Proceedings of Machine Learning Research}, month = {18--19 Aug}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v68/law17a/law17a.pdf}, url = {https://proceedings.mlr.press/v68/law17a.html}, abstract = {In this paper, we propose a computer vision based method to assess the technical skill level of surgeons by analyzing the movement of robotic instruments in robotic surgical videos. First, our method leverages the power of crowd workers on the internet to obtain high quality data in a scalable and cost-efficient way. Second, we utilize the high quality data to train an accurate and efficient robotic instrument tracker based on the state-of-the-art Hourglass Networks. Third, we assess the movement of the robotic instruments and automatically classify the technical level of a surgeon with a linear classifier, using peer evaluations of skill as the reference standard. Since the proposed method relies only on video data, this method has the potential to be transferred to other minimally invasive surgical procedures.} }
Endnote
%0 Conference Paper %T Surgeon Technical Skill Assessment using Computer Vision based Analysis %A Hei Law %A Khurshid Ghani %A Jia Deng %B Proceedings of the 2nd Machine Learning for Healthcare Conference %C Proceedings of Machine Learning Research %D 2017 %E Finale Doshi-Velez %E Jim Fackler %E David Kale %E Rajesh Ranganath %E Byron Wallace %E Jenna Wiens %F pmlr-v68-law17a %I PMLR %P 88--99 %U https://proceedings.mlr.press/v68/law17a.html %V 68 %X In this paper, we propose a computer vision based method to assess the technical skill level of surgeons by analyzing the movement of robotic instruments in robotic surgical videos. First, our method leverages the power of crowd workers on the internet to obtain high quality data in a scalable and cost-efficient way. Second, we utilize the high quality data to train an accurate and efficient robotic instrument tracker based on the state-of-the-art Hourglass Networks. Third, we assess the movement of the robotic instruments and automatically classify the technical level of a surgeon with a linear classifier, using peer evaluations of skill as the reference standard. Since the proposed method relies only on video data, this method has the potential to be transferred to other minimally invasive surgical procedures.
APA
Law, H., Ghani, K. & Deng, J.. (2017). Surgeon Technical Skill Assessment using Computer Vision based Analysis. Proceedings of the 2nd Machine Learning for Healthcare Conference, in Proceedings of Machine Learning Research 68:88-99 Available from https://proceedings.mlr.press/v68/law17a.html.

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