An Integrated Electroencephalography and Eye-Tracking Analysis Using eXtreme Gradient Boosting for Mental Workload Evaluation in Surgery.

Objective: We aimed to develop advanced machine learning models using electroencephalogram (EEG) and eye-tracking data to predict the mental workload associated with engaging in various surgical tasks.

Background: Traditional methods of evaluating mental workload often involve self-report scales, which are subject to individual biases. Due to the multidimensional nature of mental workload, there is a pressing need to identify factors that contribute to mental workload across different surgical tasks.

Classification of subtask types and skill levels in robot-assisted surgery using EEG, eye-tracking, and machine learning.

Background: Objective and standardized evaluation of surgical skills in robot-assisted surgery (RAS) holds critical importance for both surgical education and patient safety. This study introduces machine learning (ML) techniques using features derived from electroencephalogram (EEG) and eye-tracking data to identify surgical subtasks and classify skill levels.

Method: The efficacy of this approach was assessed using a comprehensive dataset encompassing nine distinct classes, each representing a unique combination of three surgical subtasks executed by surgeons while performing operations on pigs.

Prediction of Robotic Anastomosis Competency Evaluation (RACE) metrics during vesico-urethral anastomosis using electroencephalography, eye-tracking, and machine learning.

Residents learn the vesico-urethral anastomosis (VUA), a key step in robot-assisted radical prostatectomy (RARP), early in their training. VUA assessment and training significantly impact patient outcomes and have high educational value. This study aimed to develop objective prediction models for the Robotic Anastomosis Competency Evaluation (RACE) metrics using electroencephalogram (EEG) and eye-tracking data.

Mental workload evaluation using weighted phase lag index and coherence features extracted from EEG data.

Electroencephalogram (EEG) represents an effective, non-invasive technology to study mental workload. However, volume conduction, a common EEG artifact, influences functional connectivity analysis of EEG data. EEG coherence has been used traditionally to investigate functional connectivity between brain areas associated with mental workload, while weighted Phase Lag Index (wPLI) is a measure that improves on coherence by reducing susceptibility to volume conduction, a common EEG artifact.

Development of performance and learning rate evaluation models in robot-assisted surgery using electroencephalography and eye-tracking.

The existing performance evaluation methods in robot-assisted surgery (RAS) are mainly subjective, costly, and affected by shortcomings such as the inconsistency of results and dependency on the raters' opinions. The aim of this study was to develop models for an objective evaluation of performance and rate of learning RAS skills while practicing surgical simulator tasks. The electroencephalogram (EEG) and eye-tracking data were recorded from 26 subjects while performing Tubes, Suture Sponge, and Dots and Needles tasks.

Neurocognitive Correlates of Clinical Decision Making: A Pilot Study Using Electroencephalography.

The development of sound clinical reasoning, while essential for optimal patient care, can be quite an elusive process. Researchers typically rely on a self-report or observational measures to study decision making, but clinicians' reasoning processes may not be apparent to themselves or outside observers. This study explored electroencephalography (EEG) to examine neurocognitive correlates of clinical decision making during a simulated American Board of Anesthesiology-style standardized oral exam.

Surgical skill level classification model development using EEG and eye-gaze data and machine learning algorithms.

The aim of this study was to develop machine learning classification models using electroencephalogram (EEG) and eye-gaze features to predict the level of surgical expertise in robot-assisted surgery (RAS). EEG and eye-gaze data were recorded from 11 participants who performed cystectomy, hysterectomy, and nephrectomy using the da Vinci robot. Skill level was evaluated by an expert RAS surgeon using the modified Global Evaluative Assessment of Robotic Skills (GEARS) tool, and data from three subtasks were extracted to classify skill levels using three classification models-multinomial logistic regression (MLR), random forest (RF), and gradient boosting (GB).

Developing cognitive workload and performance evaluation models using functional brain network analysis.

Cognition, defined as the ability to learn, remember, sustain attention, make decisions, and solve problems, is essential in daily activities and in learning new skills. The purpose of this study was to develop cognitive workload and performance evaluation models using features that were extracted from Electroencephalogram (EEG) data through functional brain network and spectral analyses. The EEG data were recorded from 124 brain areas of 26 healthy participants conducting two cognitive tasks on a robot simulator.

Performance and learning rate prediction models development in FLS and RAS surgical tasks using electroencephalogram and eye gaze data and machine learning.

Objective: This study explored the use of electroencephalogram (EEG) and eye gaze features, experience-related features, and machine learning to evaluate performance and learning rates in fundamentals of laparoscopic surgery (FLS) and robotic-assisted surgery (RAS).

Methods: EEG and eye-tracking data were collected from 25 participants performing three FLS and 22 participants performing two RAS tasks. Generalized linear mixed models, using L1-penalized estimation, were developed to objectify performance evaluation using EEG and eye gaze features, and linear models were developed to objectify learning rate evaluation using these features and performance scores at the first attempt.

Developing surgical skill level classification model using visual metrics and a gradient boosting algorithm.

Objective: Assessment of surgical skills is crucial for improving training standards and ensuring the quality of primary care. This study aimed to develop a gradient boosting classification model (GBM) to classify surgical expertise into inexperienced, competent, and experienced levels in robot-assisted surgery (RAS) using visual metrics.

Methods: Eye gaze data were recorded from 11 participants performing four subtasks; blunt dissection, retraction, cold dissection, and hot dissection using live pigs and the da Vinci robot.

Utilizing deep neural networks and electroencephalogram for objective evaluation of surgeon's distraction during robot-assisted surgery.

Objective: To develop an algorithm for objective evaluation of distraction of surgeons during robot-assisted surgery (RAS).

Materials And Methods: Electroencephalogram (EEG) of 22 medical students was recorded while performing five key tasks on the robotic surgical simulator: Instrument Control, Ball Placement, Spatial Control II, Fourth Arm Tissue Retraction, and Hands-on Surgical Training Tasks. All students completed the Surgery Task Load Index (SURG-TLX), which includes one domain for subjective assessment of distraction (scale: 1-20).

Association between Functional Brain Network Metrics and Surgeon Performance and Distraction in the Operating Room.

Objective: The aim of this work was to examine (electroencephalogram) EEG features that represent dynamic changes in the functional brain network of a surgical trainee and whether these features can be used to evaluate a robot assisted surgeon's (RAS) performance and distraction level in the operating room.

Materials And Methods: Electroencephalogram (EEG) data were collected from three robotic surgeons in an operating room (OR) via a 128-channel EEG headset with a frequency of 500 samples/second. Signal processing and network neuroscience algorithms were applied to the data to extract EEG features.

Surgical Hand Gesture Recognition Utilizing Electroencephalogram as Input to the Machine Learning and Network Neuroscience Algorithms.

Surgical gestures detection can provide targeted, automated surgical skill assessment and feedback during surgical training for robot-assisted surgery (RAS). Several sources including surgical videos, robot tool kinematics, and an electromyogram (EMG) have been proposed to reach this goal. We aimed to extract features from electroencephalogram (EEG) data and use them in machine learning algorithms to classify robot-assisted surgical gestures.

Dynamic changes of brain functional states during surgical skill acquisition.

There is lack of a standardized measure of technical proficiency and skill acquisition for robot-assisted surgery (RAS). Learning surgical skills, in addition to the interaction with the machine and the new surgical environment adds to the complexity of the learning process. Moreover, evaluation of surgeon performance in operating room is required to optimize patient safety.

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.

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.

Technical mentorship during robot-assisted surgery: a cognitive analysis.

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

Materials And Methods: The 'Mind Maps' project aimed at evaluating cognitive function with regard to surgeon's expertise and trainee's skills. The study included electroencephalogram (EEG) recordings of a mentor observing trainee surgeons in 20 procedures involving extended lymph node dissection (eLND) or urethrovesical anastomosis (UVA), with simultaneous assessment of trainees using the National Aeronautics and Space Administration Task Load index (NASA-TLX) questionnaire.

Mentor's brain functional connectivity network during robotic assisted surgery mentorship.

In many complicated cognitive-motor tasks mentoring is inevitable during the learning process. Although mentors are expert in doing the task, trainee's operation might be new for a mentor. This makes mentoring a very difficult task which demands not only the knowledge and experience of a mentor, but also his/her ability to follow trainee's movements and patiently advise him/her during the operation.

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.