Evaluation of surgical skills during robotic surgery by deep learning-based multiple surgical instrument tracking in training and actual operations

Dongheon Lee, Hyeong Won Yu, Hyungju Kwon, Hyoun Joong Kong, Kyu Eun Lee, Hee Chan Kim

Research output: Contribution to journalArticlepeer-review

18 Scopus citations

Abstract

As the number of robotic surgery procedures has increased, so has the importance of evaluating surgical skills in these techniques. It is difficult, however, to automatically and quantitatively evaluate surgical skills during robotic surgery, as these skills are primarily associated with the movement of surgical instruments. This study proposes a deep learning-based surgical instrument tracking algorithm to evaluate surgeons’ skills in performing procedures by robotic surgery. This method overcame two main drawbacks: occlusion and maintenance of the identity of the surgical instruments. In addition, surgical skill prediction models were developed using motion metrics calculated from the motion of the instruments. The tracking method was applied to 54 Video Segments and evaluated by root mean squared error (RMSE), area under the curve (AUC), and Pearson correlation analysis. The RMSE was 3.52 mm, the AUC of 1 mm, 2 mm, and 5mm were 0.7, 0.78, and 0.86, respectively, and Pearson’s correlation coefficients were 0.9 on the x-axis and 0.87 on the y-axis. The surgical skill prediction models showed an accuracy of 83% with Objective Structured Assessment of Technical Skill (OSATS) and Global Evaluative Assessment of Robotic Surgery (GEARS). The proposed method was able to track instruments during robotic surgery, suggesting that the current method of surgical skill assessment by surgeons can be replaced by the proposed automatic and quantitative evaluation method.

Original languageEnglish
Article number1964
Pages (from-to)1-15
Number of pages15
JournalJournal of Clinical Medicine
Volume9
Issue number6
DOIs
StatePublished - Jun 2020

Keywords

  • Deep learning
  • Quantitative evaluation
  • Robotic surgery
  • Surgical instrument tracking
  • Surgical skills

Fingerprint

Dive into the research topics of 'Evaluation of surgical skills during robotic surgery by deep learning-based multiple surgical instrument tracking in training and actual operations'. Together they form a unique fingerprint.

Cite this