Surgical skill levels: Classification and analysis using deep neural network model and motion signals. (August 2019)

• Record Type: Journal Article
• Authors: Nguyen, Xuan Anh; Ljuhar, Damir; Pacilli, Maurizio; Nataraja, Ramesh Mark; Chauhan, Sunita
• Is Part Of: Computer methods and programs in biomedicine. Volume 177(2019)
• Page Start: 1
• Page End: 8
• Publication Date: 2019-08
• Subjects: Surgical skill assessment -- Surgical education -- Deep neural network -- Hand motion signals; Medicine -- Computer programs -- Periodicals; Biology -- Computer programs -- Periodicals; Computers -- Periodicals; Medicine -- Periodicals; Médecine -- Logiciels -- Périodiques; Biologie -- Logiciels -- Périodiques; Biology -- Computer programs; Medicine -- Computer programs; Periodicals; Electronic journals; 610.28
• DOI: 10.1016/j.cmpb.2019.05.008
• Languages: English
• ISSNs: 0169-2607

Abstract

Highlights: We proposed to use an innovative CNN-LSTM network to objectively classify surgical skills levels. One open surgery dataset and one RMIS dataset were used to evaluate the performance of the proposed method. Accelerometers provided a better accuracy compared to gyroscopes. Higher accuracy achieved when added SENet block and restart learning rate to the CNN-LSTM network. Abstract: Background and Objectives: Currently, the assessment of surgical skills relies primarily on the observations of expert surgeons. This may be time-consuming, non-scalable, inconsistent and subjective. Therefore, an automated system that can objectively identify the actual skills level of a junior trainee is highly desirable. This study aims to design an automated surgical skills evaluation system. Methods: We propose to use a deep neural network model that can analyze raw surgical motion data with minimal preprocessing. A platform with inertial measurement unit sensors was developed and participants with different levels of surgical experience were recruited to perform core open surgical skills tasks. JIGSAWS a publicly available robot based surgical training dataset was used to evaluate the generalization of our deep network model. 15 participants (4 experts, 4 intermediates and 7 novices) were recruited into the study. Results: The proposed deep model achieved an accuracy of 98.2%. With comparison to JIGSAWS; our method outperformed some existing approaches with an accuracy of 98.4%, 98.4% and 94.7% for suturing, needle-passing, and knot-tying, respectively. The experimental results demonstrated the applicability of this method in both open surgery and robot-assisted minimally invasive surgery. Conclusions: This study demonstrated the potential ability of the proposed deep network model to learn the discriminative features between different surgical skills levels.