Somayeh B. Shafiei

Technical mentorship during robot-assisted surgery: a cognitive analysis

To investigate cognitive and mental workload assessments, which may play a critical role in defining successful mentorship.

Understanding Cognitive Performance During Robot-Assisted Surgery

OBJECTIVE To understand cognitive function of an expert surgeon in various surgical scenarios while performing robot-assisted surgery. MATERIALS AND METHODS In an Internal Review Board approved study, National Aeronautics and Space Administration-Task Load Index (NASA-TLX) questionnaire with surgical field notes were simultaneously completed. A wireless electroencephalography (EEG) headset was used to monitor brain activity during all procedures. Three key portions were evaluated: lysis of adhesions, extended lymph node dissection, and urethro-vesical anastomosis (UVA). Cognitive metrics extracted were distraction, mental workload, and mental state. RESULTS In evaluating lysis of adhesions, mental state (EEG) was associated with better performance (NASA-TLX). Utilizing more mental resources resulted in better performance as self-reported. Outcomes of lysis were highly dependent on cognitive function and decision-making skills. In evaluating extended lymph node dissection, there was a negative correlation between distraction level (EEG) and mental demand, physical demand and effort (NASA-TLX). Similar to lysis of adhesion, utilizing more mental resources resulted in better performance (NASA-TLX). Lastly, with UVA, workload (EEG) negatively correlated with mental and temporal demand and was associated with better performance (NASA-TLX). The EEG recorded workload as seen here was a combination of both cognitive performance (finding solution) and motor workload (execution). Majority of workload was contributed by motor workload of an expert surgeon. During UVA, muscle memory and motor skills of expert are keys to completing the UVA. CONCLUSION Cognitive analysis shows that expert surgeons utilized different mental resources based on their need.

Cognitive learning and its future in urology: surgical skills teaching and assessment

Purpose of review The aim of this study is to provide an overview of the current status of novel cognitive training approaches in surgery and to investigate the potential role of cognitive training in surgical education. Recent findings Kinematics of end-effector trajectories, as well as cognitive state features of surgeon trainees and mentors have recently been studied as modalities to objectively evaluate the expertise level of trainees and to shorten the learning process. Virtual reality and haptics also have shown promising in research results in improving the surgical learning process by providing feedback to the trainee. Summary ‘Cognitive training’ is a novel approach to enhance training and surgical performance. The utility of cognitive training in improving motor skills in other fields, including sports and rehabilitation, is promising enough to justify its utilization to improve surgical performance. However, some surgical procedures, especially ones performed during human–robot interaction in robot-assisted surgery, are much more complicated than sport and rehabilitation. Cognitive training has shown promising results in surgical skills-acquisition in complicated environments such as surgery. However, these methods are mostly developed in research groups using limited individuals. Transferring this research into the clinical applications is a demanding challenge. The aim of this review is to provide an overview of the current status of these novel cognitive training approaches in surgery and to investigate the potential role of cognitive training in surgical education.

Using Two-Third Power Law for Segmentation of Hand Movement in Robotic Assisted Surgery

In this study, we have developed a robust and accurate algorithm based on concept of two-third power law in human motor control to segment the hand trajectory of robotic surgeons into smaller segments. We hypothesis that tracking a longer trajectory is subjected to higher cognitive workload that may lead in to an imperfect CNS performance in programming muscle activation which will lead to more number of segment trajectories and pause points in hand movements.To test our hypothesis, after segmenting the trajectory, we determine the correlation between affine velocity and workload extracted from Surgeon’s Electroencephalography (EEG) features. EEG features are extracted by using brain waves recorded by wireless brain computer interface (B-Alert X-10 system). In our experimental study, 2 groups of participants three “experts” and five “Competent and Proficient” performed Urethro-vesical Anastomosis on an inanimate model, using the da-Vinci Surgical System® (Sunnyvale, CA).Copyright

MP51-05 DOES TRAINEE PERFORMANCE IMPACT SURGEON'S STRESS DURING ROBOT-ASSISTED SURGERY?

INTRODUCTION AND OBJECTIVES: Stress increases mental workload leading to reduction in surgical performance and subsequently risking patient safety. Console surgeon and their teams often experience mental stress, yet there is little research about objective measurement of stress levels in the operating room during Robot-assisted Surgery (RAS). In the study, brain activity data are used to differentiate between causes of mental stress of mentor surgeon and the impact of trainee performance during RAS. METHODS: EEG data from surgical mentor while observing 87 Urethro-Vesical Anastomoses (UVA) and 74 Pelvic Lymph Node Dissections (PLND) performed by 3 trainees, as well as performing 26 UVA and 26 PLND is recorded. Level and type of mental stress were determined using the power spectral density, during different frequencies, of signals from 20 channel EEG. Performance scores were used to identify the relationship between performance and stress. Stress caused by worry about ability of safe completion were estimated by using the brain activity during upper alpha (11-12 Hz), sensorimotor rhythm (SMR, 12-15 Hz), and low beta (19-22 Hz) bands in the “Cz” channel (area in motor cortex). The activity at the upper beta and gamma was used to estimate stress level and anxiety and fear caused by risk prediction. RESULTS: Mentor’s brain faces two main types of stresses during RAS. While observing low quality performance by trainee surgeons, the cause of mentor’s mental stress is mostly worries about lack of proficiency of trainee surgeon (Type 1). However, stress of mentor while performing surgery or observing a high quality performance by trainee surgeon, is mostly the result of situation awareness and risk prediction on the operative field (Type 2). These two types of stress activate different areas of the brain in specific frequencies. CONCLUSIONS: EEG can be used to separate different types of stress experienced during performing and mentoring robot-assisted surgery. A deeper understanding of the difference and effect of these stresses and their outcomes can lead to targeted intervention and quality improvement. Source of Funding: Roswell Park Alliance Foundation.

Aligning brain activity and sketch in multi-modal CAD interface

This paper investigates the proper synchronization of sketch data and cognitive states in a multi-modal CAD interface. In a series of experiments, 5 subjects were instructed to watch and then explain 6 mechanical mechanisms by sketching them on a touch based screen. Simultaneously, subject’s brain waves were recorded in terms of electroencephalogram (EEG) signals from 9 locations on the scalp. EEG signals were analyzed and translated into mental workload and cognitive state. A dynamic time window was then constructed to align these features with sketch features such that the combination of two modalities maximizes the classification of gesture from non-gesture strokes. Quadratic Discriminant Analysis (QDA) was used as classification method. Our experimental results show that the best temporal alignment for workload and sketch analysis starts from 30% time lag with previous stroke and ends before 30% time lag with next stroke.

Aligning Brain Activity and Sketch in Multi-Modal CAD Interface

This paper investigates the proper synchronization of sketch data and cognitive states in a multi-modal CAD interface. In a series of experiments, 5 subjects were instructed to watch and then explain 6 mechanical mechanisms by sketching them on a touch based screen. Simultaneously, subject’s brain waves were recorded in terms of electroencephalogram (EEG) signals from 9 locations on the scalp. EEG signals were analyzed and translated into mental workload and cognitive state. A dynamic time window was then constructed to align these features with sketch features such that the combination of two modalities maximizes the classification of gesture from non-gesture strokes. Quadratic Discriminant Analysis (QDA) was used as classification method. Our experimental results show that the best temporal alignment for workload and sketch analysis starts from 30% time lag with previous stroke and ends before 30% time lag with next stroke.Copyright

Functional Brain States Measure Mentor-Trainee Trust during Robot-Assisted Surgery

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.

PD46-02 LOOKING FOR YOUR OWN REFLECTION: ASSESSING BRAIN FUNCTIONAL STATE OF SURGICAL MENTOR DURING ROBOT-ASSISTED SURGERY

RESULTS: Of all features tested, we found that the five most predictive features were Stress, mental workload (MW), Frustration, Surprise and Modularity. These features were significantly different between Trustworthy and Concerning performances, showing higher frustration, stress, MW, surprise and lower modularity while mentoring concerning as opposed to trustworthy performances. CONCLUSIONS: Cognition-based Trust can be objectively evaluated using EEG features. This is the first reported study to objectively evaluate trust during RAS by featuring cognitive and brain functioning features.

PD41-08 SKILL ACQUISITION AND ITS RETENTION AFTER SIMULATION-BASED PRACTICE DURING ROBOT-ASSISTED SURGERY: CAN FUNCTIONAL BRAIN STATES HELP US FORGE FORWARD?

INTRODUCTION AND OBJECTIVES: Patient safety is fundamental to surgical practice and it is critical to ensure surgical training and competence. Little has been published on brain cognitive states during learning and retention of basic Robot-Assisted Surgical skills. We sought to evaluate the feasibility of utilizing a novel brain functional states to evaluate surgical competency. METHODS: 27 medical students were evaluated while performing four key tasks of the validated Fundamental Skills of Robot Surgery (FSRS) Curriculum and one advanced surgical module the Hands-on Surgical Training (HoST) over six sessions, utilizing the robotic Surgery Simulator (RoSS). The four FSRS tasks evaluated were Instrument Control Task, Ball Placement Task, Spatial Control II Task, Threading string through a series of hoops and 4th Arm Tissue Retraction. Tool -based metrics were assessed and recorded by RoSS. Brain states are extracted using the pairwise phase synchronization between EEG channels and are presented as functional brain networks. The functional brain networks are then quantified using network statistics, and spectral density of signals for all channels (mental workload). RESULTS: The average mental workload initially increases before significantly decreasing across sessions(Fig 1). This trend is also observed in functional brain states during the four tool-based metrics, as integration and segregation features increase at the beginning of learning and later decrease (Fig 2). We observed significant correlations between brain state and tool-based metrics (RoSS), while performing HOST task, where brain states do not correlate. CONCLUSIONS: We report to our knowledge, the first study that evaluates brain states during skill acquisition and learning after simulation-based training. Various brain areas are functionally activated and integrated while acquiring new skills but these interactions decrease after preliminary learning.