Abstract
Mutual trust is important in surgical teams, especially in robot-assisted surgery (RAS) where interaction with robot-assisted interface increases the complexity of relationships within the surgical team. However, evaluation of trust between surgeons is challenging and generally based on subjective measures. Mentor-Trainee trust was defined as assessment of mentor on trainee’s performance quality and approving trainee’s ability to continue performing the surgery. Here, we proposed a novel method of objectively assessing mentor-trainee trust during RAS based on patterns of brain activity of surgical mentor observing trainees. We monitored the EEG activity of a mentor surgeon while he observed procedures performed by surgical trainees and quantified the mentor’s brain activity using functional and cognitive brain state features. We used methods from machine learning classification to identity key features that distinguish trustworthiness from concerning performances. Results showed that during simple surgical task, functional brain features are sufficient to classify trust. While, during more complex tasks, the addition of cognitive features could provide additional accuracy, but functional brain state features drive classification performance. These results indicate that functional brain network interactions hold information that may help objective trainee specific mentorship and aid in laying the foundation of automation in the human-robot shared control environment during RAS.
Highlights
robot-assisted surgery (RAS) requires a surgeon to master motor skills in order to operate the robotic surgical system, and to develop cognitive competence while operating remotely with no tactile feedback[5,6]
In order to quantify the brain state of the expert surgeon, we extracted 12 cognitive and 21 functional measures of one expert’s brain activity from EEG recordings as he observed three trainees’ performances during 87 Urethrovesical Anastomosis (UVA) and 83 Lymph Node Dissection (LND) operations, as part of the “Mind Maps” program (Methods). These measures represent total brain activity calculated across six cognitive systems[32]: frontal (F), prefrontal (PF), temporal (T), central (C), occipital (O), and parietal (Pa)
The 21 functional state features were extracted by calculating the average phase synchronization, using equation (7), within and between the six cognitive systems
Summary
RAS requires a surgeon to master motor skills (human-machine interaction) in order to operate the robotic surgical system (console), and to develop cognitive competence while operating remotely with no tactile feedback[5,6]. The expert surgeon must switch from surgical console or monitor and follow a trainee on a dual console In this shared environment, trust plays a key role and can lead to an open communication[10] and cooperation[11,12] leading to quality decision making[13], safe risk-taking[14], and satisfaction[15,16]. Different approaches for trust evaluation have been proposed: affect and cognition-based[24] Both are associated with performance in different ways[24] and influence the psychological state of a team. Lack of mutual trust between team members[30] results in anxiety, stress, and disappointment[31] These effects can negatively influence performance, reduce cognition-based trust and subsequently, affect-based trust[25]. Surgical performance was categorized as “trustworthy” or “concerning” based on validated NASA Task Load Index (NASA-TLX) scores and written feedback of the expert mentor surgeon
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