scholarly journals Subjective anxiety ratings before and after stressful neurosurgical virtual reality tumor resection task

2015 ◽  
Vol 42 (S1) ◽  
pp. S42-S42
Author(s):  
A Winkler-Schwartz ◽  
J Fares ◽  
B Khalid ◽  
M Baggiani ◽  
S Christie ◽  
...  
Keyword(s):  
Virtual Reality ◽  
Medical Students ◽  
Tumor Resection ◽  
Tumor Removal ◽  
Surgical Performance ◽  
Surgical Simulators ◽  
Surgical Expertise ◽  
Before And After ◽  
Operative Performance ◽  
Junior Residents

Background: The availability of virtual reality (VR) surgical simulators affords the opportunity to assess the influence of stress on neurosurgical operative performance in a controlled laboratory environment. This study sought to examine the effect of a stressful VR neurosurgical task on the subjective anxiety ratings of participants with varying levels of surgical expertise. Methods: Twenty four participants comprised of six staff neurosurgeons, six senior neurosurgical residents (PGY4-6), six junior neurosurgical residents (PGY1-3), and six senior medical students took part in a bimanual VR tumor removal task with a component of sudden uncontrollable intra-operative bleeding. State Trait Anxiety Inventory (STAI) questionnaires were completed immediately pre and post the stress stimulus. The STAI questionnaire consisted of six items (calm, tense, upset, relaxed, content and worried) measured on a Likert scale. Results: Significant increases in subjective anxiety ratings were noted in junior residents (p=0.005) and medical students (p=0.025) while no significant changes were observed for staff and senior neurosurgical residents. Conclusions: Staff and senior residents more effectively mitigate stress compared to junior colleagues in a VR operative environment. Further physiological correlates are needed to determine whether this increased anxiety is paralleled by physiological arousal and altered surgical performance.

scholarly journals P.215 Tumor Heatmaps – Feedback Tool for Virtual Reality Neurosurgical Simulation

2021 ◽  
Vol 48 (s3) ◽  
pp. S82-S82
Author(s):  
R Sawaya ◽  
R Yilmaz ◽  
A Bugdadi ◽  
A Winkler-Schwartz ◽  
H Azarnoush ◽  
...  
Keyword(s):  
Virtual Reality ◽  
Spatial Distribution ◽  
Medical Students ◽  
Tumor Volume ◽  
Performance Metrics ◽  
Operative Procedures ◽  
Tumor Removal ◽  
Critical Areas ◽  
Competency Based ◽  
Junior Residents

Background: Performance Heatmaps were designed to visualize the spatial distribution of performance metrics during resection of complex tumors. This novel methodology provides experts (neurosurgeons) and trainees (residents and medical students) with visual feedback on their neurosurgical performance during operative procedures. Methods: Neurosurgeons (NS), senior residents (SR), junior residents (JR) and medical students (MS) performed resection of a complex tumor on the NeuroVR simulation platform. Metrics including time spent, force applied, and tumor volume removed were used to create Performance Heatmaps for each group. Results: During complex operative procedures, greater expertise correlated increased time spent in critical areas (NS = 121.0 s, SR = 103.0 s, JR = 86.1 s, MS = 84.9 s), increased force application (NS = 387 N, SR = 317 N, JR = 340 N, MS = 304 N), and increased tumor removal (NS = .096 cc, SR = .081 cc, JR = .074 cc, MS = .069 cc). Conclusions: Performance Heatmaps further our understanding of neurosurgical expertise by identifying key differences between experts (neurosurgeons) and trainees (residents and medical students). With the adoption of competency-based curricula, intuitive feedback tools will prove essential for trainees seeking surgical mastery.

scholarly journals SP2.1.1Continuous Monitoring and Assessment of Surgical Technical Skills Using Deep Learning

2021 ◽  
Vol 108 (Supplement_7) ◽  
Author(s):  
Recai Yilmaz ◽  
Alexander Winkler-Schwartz ◽  
Aiden Reich ◽  
Rolando Del Maestro
Keyword(s):  
Neural Network ◽  
Virtual Reality ◽  
Deep Learning ◽  
Medical Students ◽  
Deep Neural Network ◽  
Performance Metrics ◽  
Technical Skills ◽  
Surgical Performance ◽  
Surgical Simulators ◽  
Surgical Technical

Abstract Aims Excellent surgical technical skills are of paramount importance to perform surgical procedures, safely and efficiently. Virtual reality surgical simulators can both simulate real operations while providing standardized, risk-free surgical hands-on experience. The integration of AI (artificial intelligence) and virtual reality simulators provides opportunities to carry out comprehensive continuous assessments of surgical performance. We developed and tested a deep learning algorithm which can continuously monitor and assess surgical bimanual performance on virtual reality surgical simulators. Methods Fifty participants from four expertise levels (14 experts/neurosurgeons, 14 senior residents, 10 junior residents, 12 medical students) performed a simulated subpial tumor resection 5 times and a complex simulated brain tumor operation once on the NeuroVR platform. Participants were asked to remove the tumors completely while minimizing bleeding and damage to surrounding tissues employing a simulated ultrasonic aspirator and bipolar forceps. A deep neural network continually tracked the surgical performance utilizing 16 performance metrics generated every 0.2-seconds. Results The deep neural network was successfully trained using neurosurgeons and medical students’ data, learning the composites of expertise comparing high and lower skill levels. The trained algorithm was able to score the technical skills of individuals continuously at 0.2-second intervals. Statistically significant differences in average scores were identified between the 4 groups. Conclusions AI-powered surgical simulators provide continuous assessment of bimanual technical skills during surgery which may further define the composites necessary to train surgical expertise. To our knowledge, this is the first attempt in surgery to continuously assess surgical technical skills using deep learning.

scholarly journals O51: ARTIFICIAL INTELLIGENCE UTILIZING RECURRENT NEURAL NETWORKS TO CONTINUOUSLY MONITOR COMPOSITES OF SURGICAL EXPERTISE

2021 ◽  
Vol 108 (Supplement_1) ◽  
Author(s):  
R Yilmaz ◽  
A Winkler-Schwartz ◽  
N Mirchi ◽  
A Reich ◽  
R Del Maestro
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Virtual Reality ◽  
Medical Students ◽  
Recurrent Neural Network ◽  
Haptic Feedback ◽  
Performance Metrics ◽  
Tumor Resection ◽  
Technical Skills ◽  
Surgical Expertise

Abstract Introduction Many surgical adverse events occur secondary to technical errors related to poor bimanual skills, fatigue and lack of the required expertise. We developed AI algorithms to continuously assess surgical bimanual technical performance during virtual reality simulated surgical tasks. To our knowledge, this is the first attempt in surgery to train AI algorithms to continuously monitor and evaluate bimanual skills comprehensively. Method Fifty individuals from four expertise levels (14 experts/neurosurgeons, 14 senior residents, 10 junior residents, 12 medical students) performed two virtual reality simulated surgical tasks with haptic feedback: a subpial tumor resection 5 times and a complex, realistically simulated brain tumor operation once. Each task required complete tumor removal while minimizing bleeding and damage to surrounding tissues using a simulated ultrasonic aspirator and a bipolar. A recurrent neural network continually tracked individual bimanual performance utilizing 16 performance metrics generated every 0.2 seconds. Result The recurrent neural network algorithm was successfully trained using neurosurgeons and medical students' data, learning the composites of expertise comparing high and lower skill levels. The trained algorithm outlined and monitored technical skills every 0.2 second continuously organizing performance of each surgical task into three levels: ‘excellent’, ‘average’ and ‘poor’. The percentage time spent on each level was calculated and significant differences found between all four groups for ‘excellent’ and ‘poor’ levels. Conclusion AI-powered surgical simulators provide an advanced assessment and training tool. AI's ability to continuous assess bimanual technical skills during surgery may further define the composites necessary to train surgical expertise. Abbrev AI: artificial intelligence Take-home message By advanced artificial intelligence algorithms surgeon's bi-manual technical skills can be assessed continuously, time periods of poor performance which increase the possibility of errors in performance can be identified.

scholarly journals Development of a performance model for virtual reality tumor resections

2019 ◽  
Vol 131 (1) ◽  
pp. 192-200 ◽  
Author(s):  
Robin Sawaya ◽  
Ghusn Alsideiri ◽  
Abdulgadir Bugdadi ◽  
Alexander Winkler-Schwartz ◽  
Hamed Azarnoush ◽  
...  
Keyword(s):  
Virtual Reality ◽  
Brain Tumor ◽  
Medical Students ◽  
Direct Contact ◽  
Tumor Volume ◽  
Tumor Resection ◽  
Performance Model ◽  
Hypothetical Model ◽  
Study Results ◽  
And Task

OBJECTIVEPrevious work from the authors has shown that hand ergonomics plays an important role in surgical psychomotor performance during virtual reality brain tumor resections. In the current study they propose a hypothetical model that integrates the human and task factors at play during simulated brain tumor resections to better understand the hand ergonomics needed for optimal safety and efficiency. They hypothesize that 1) experts (neurosurgeons), compared to novices (residents and medical students), spend a greater proportion of their time in direct contact with critical tumor areas; 2) hand ergonomic conditions (most favorable to unfavorable) prompt participants to adapt in order to optimize tumor resection; and 3) hand ergonomic adaptation is acquired with increasing expertise.METHODSIn an earlier study, experts (neurosurgeons) and novices (residents and medical students) were instructed to resect simulated brain tumors on the NeuroVR (formerly NeuroTouch) virtual reality neurosurgical simulation platform. For the present study, the simulated tumors were divided into four quadrants (Q1 to Q4) to assess hand ergonomics at various levels of difficulty. The spatial distribution of time expended, force applied, and tumor volume removed was analyzed for each participant group (total of 22 participants).RESULTSNeurosurgeons spent a significantly greater percentage of their time in direct contact with critical tumor areas. Under the favorable hand ergonomic conditions of Q1 and Q3, neurosurgeons and senior residents spent significantly more time in Q1 than in Q3. Although forces applied in these quadrants were similar, neurosurgeons, having spent more time in Q1, removed significantly more tumor in Q1 than in Q3. In a comparison of the most favorable (Q2) to unfavorable (Q4) hand ergonomic conditions, neurosurgeons adapted the forces applied in each quadrant to resect similar tumor volumes. Differences between Q2 and Q4 were emphasized in measures of force applied per second, tumor volume removed per second, and tumor volume removed per unit of force applied. In contrast, the hand ergonomics of medical students did not vary across quadrants, indicating the existence of an “adaptive capacity” in neurosurgeons.CONCLUSIONSThe study results confirm the experts’ (neurosurgeons) greater capacity to adapt their hand ergonomics during simulated neurosurgical tasks. The proposed hypothetical model integrates the study findings with various human and task factors that highlight the importance of learning in the acquisition of hand ergonomic adaptation.

scholarly journals Percent reduction of growth hormone levels correlates closely with percent resected tumor volume in acromegaly

2015 ◽  
Vol 122 (4) ◽  
pp. 798-802 ◽  
Author(s):  
Lucia Schwyzer ◽  
Robert M. Starke ◽  
John A. Jane ◽  
Edward H. Oldfield
Keyword(s):  
Growth Hormone ◽  
Tumor Volume ◽  
Tumor Resection ◽  
Volume Growth ◽  
Residual Tumor ◽  
Close Correlation ◽  
Tumor Removal ◽  
Hormone Levels ◽  
Before And After ◽  
Plasma Gh

OBJECT Correlation between tumor volume and hormone levels in individual patients would permit calculation of the fraction of tumor removed by surgery, by measuring postoperative hormone levels. The goals of this study were to examine the relationship between tumor volume, growth hormone (GH), and insulin-like growth factor–1 (IGF-1) levels, and to assess the correlation between percent tumor removal and the reduction in plasma GH and IGF-1 in patients with acromegaly. METHODS The 3D region of interest–based volumetric method was used to measure tumor volume via MRI before and after surgery in 11 patients with GH-secreting adenomas. The volume of residual tumor as a fraction of preoperative tumor volume was correlated with GH levels before and after surgery. Examination of this potential correlation required selection of patients with acromegaly who 1) had incomplete tumor removal, 2) had precise measurements of initial and residual tumor, and 3) were not on medical therapy. RESULTS Densely granulated tumors produced more peripheral GH per mass of tumor than sparsely granulated tumors (p = 0.04). There was a correlation between GH and IGF-1 levels (p = 0.001). Although there was no close correlation between tumor size and peripheral GH levels, after normalizing each tumor to its own plasma GH level and tumor volume, a comparison of percent tumor resection with percent drop in plasma GH yielded a high correlation coefficient (p = 0.006). CONCLUSIONS Densely granulated somatotropinomas produce more GH per mass of tumor than do sparsely granulated tumors. Each GH-secreting tumor has its own intrinsic level of GH production per mass of tumor, which is homogeneous over the tumor mass, and which varies greatly between tumors. In most patients the fraction of a GH-secreting tumor removed by surgery can be accurately estimated by simply comparing plasma GH levels after surgery to those before surgery.

Learning and Usability within a Virtual Reality Trainer for Medical Students

2005 ◽  
Vol 49 (26) ◽  
pp. 2226-2230 ◽  
Author(s):  
Susan M. Stevens ◽  
Timothy E. Goldsmith ◽  
Thomas P. Caudell ◽  
Dale C. Alverson
Keyword(s):  
Virtual Reality ◽  
Medical Students ◽  
Head Injury ◽  
Expert Knowledge ◽  
Medical Curriculum ◽  
Knowledge Structure ◽  
Knowledge Structures ◽  
Before And After ◽  
Vr Training ◽  
The University

Virtual reality (VR) offers the potential to train medical students on high-risk situations. The current study investigated VR training of medical students to diagnose and treat a patient avatar experiencing a serious head injury. The user interface was investigated, including use of tools, comfort and VR locomotion, and found to be sufficiently high to warrant training within a medical curriculum. In addition, actual learning as a function of VR training was assessed by comparing medical students' knowledge structures to an expert knowledge structure before and after training. Students' knowledge structures became more similar to an expert's knowledge structure indicating that their conceptual understanding of core head-injury concepts increased as a result of VR training. The study was carried out under the auspices of Project TOUCH (Telehealth Outreach for Unified Community Health), a multi-year collaboration between The University of Hawaii (UH) and The University of New Mexico (UNM).

Can Surgical Performance for Varying Experience be Measured from Hand Motions?

2018 ◽  
Vol 62 (1) ◽  
pp. 583-587 ◽  
Author(s):  
David P. Azari ◽  
Brady L. Miller ◽  
Brian V. Le ◽  
Jacob A. Greenberg ◽  
Caprice C. Greenberg ◽  
...  
Keyword(s):  
Medical Students ◽  
Motion Tracking ◽  
Rating Scales ◽  
Video Frame ◽  
Surgical Performance ◽  
Tissue Handling ◽  
Visual Analog Scales ◽  
Short Video ◽  
Expert Ratings ◽  
Junior Residents

This study evaluates if hand movements, tracked using digital video, can quantify in-context surgical performance. Participants of varied experience completed simple interrupted suturing and running subcuticular suturing tasks. Marker-less motion tracking software traced the two-dimensional position of a region of the hand for every video frame. Four expert observers rated 219 short video clips of participants performing the task from 0 to 10 along the following visual analog scales: fluidity of motion, motion economy, tissue handling, and coordination. Expert ratings of attending surgeon hand motions (mean=7.5, sd=1.3) were significantly greater ( p<0.05) than medical students (mean=5.0, sd=1.9) and junior residents (mean=6.4, sd=1.5) for all rating scales. Significant differences ( p<0.02) in mean path length per cycle were also observed both between medical students (803 mm, sd=374) and senior residents (491 mm, sd=216), and attendings (424 mm, sd=250) and junior residents (609 mm, sd=187). These results suggest that substantial gains in performance are attained after the second year of residency and that hand kinematics can predict differences in expert ratings for simulated suturing tasks commensurate with experience – a necessary step to develop valid and automatic on-demand feedback tools.

The force pyramid: a spatial analysis of force application during virtual reality brain tumor resection

2017 ◽  
Vol 127 (1) ◽  
pp. 171-181 ◽  
Author(s):  
Hamed Azarnoush ◽  
Samaneh Siar ◽  
Robin Sawaya ◽  
Gmaan Al Zhrani ◽  
Alexander Winkler-Schwartz ◽  
...  
Keyword(s):  
Virtual Reality ◽  
Medical Student ◽  
Spatial Analysis ◽  
Medical Students ◽  
Brain Tumors ◽  
Tumor Resection ◽  
Study Groups ◽  
Student Groups ◽  
Force Application ◽  
Tier 3

OBJECTIVEVirtual reality simulators allow development of novel methods to analyze neurosurgical performance. The concept of a force pyramid is introduced as a Tier 3 metric with the ability to provide visual and spatial analysis of 3D force application by any instrument used during simulated tumor resection. This study was designed to answer 3 questions: 1) Do study groups have distinct force pyramids? 2) Do handedness and ergonomics influence force pyramid structure? 3) Are force pyramids dependent on the visual and haptic characteristics of simulated tumors?METHODSUsing a virtual reality simulator, NeuroVR (formerly NeuroTouch), ultrasonic aspirator force application was continually assessed during resection of simulated brain tumors by neurosurgeons, residents, and medical students. The participants performed simulated resections of 18 simulated brain tumors with different visual and haptic characteristics. The raw data, namely, coordinates of the instrument tip as well as contact force values, were collected by the simulator. To provide a visual and qualitative spatial analysis of forces, the authors created a graph, called a force pyramid, representing force sum along the z-coordinate for different xy coordinates of the tool tip.RESULTSSixteen neurosurgeons, 15 residents, and 84 medical students participated in the study. Neurosurgeon, resident and medical student groups displayed easily distinguishable 3D “force pyramid fingerprints.” Neurosurgeons had the lowest force pyramids, indicating application of the lowest forces, followed by resident and medical student groups. Handedness, ergonomics, and visual and haptic tumor characteristics resulted in distinct well-defined 3D force pyramid patterns.CONCLUSIONSForce pyramid fingerprints provide 3D spatial assessment displays of instrument force application during simulated tumor resection. Neurosurgeon force utilization and ergonomic data form a basis for understanding and modulating resident force application and improving patient safety during tumor resection.

scholarly journals Virtual Reality Simulation as a Tool to Monitor Surgical Performance Indicators: VIRESI Observational Study

2017 ◽  
Vol 30 (5) ◽  
pp. 388 ◽  
Author(s):  
Nuno Muralha ◽  
Manuel Oliveira ◽  
Maria Amélia Ferreira ◽  
José Costa-Maia
Keyword(s):  
Virtual Reality ◽  
Performance Indicators ◽  
Virtual Reality Simulator ◽  
Virtual Reality Training ◽  
Virtual Reality Simulation ◽  
Mentor Training ◽  
Operative Field ◽  
Surgical Performance ◽  
Before And After ◽  
Training Simulation

Introduction: Virtual reality simulation is a topic of discussion as a complementary tool to traditional laparoscopic surgical training in the operating room. However, it is unclear whether virtual reality training can have an impact on the surgical performance of advanced laparoscopic procedures. Our objective was to assess the ability of the virtual reality simulator LAP Mentor to identify and quantify changes in surgical performance indicators, after LAP Mentor training for digestive anastomosis.Material and Methods: Twelve surgeons from Centro Hospitalar de São João in Porto (Portugal) performed two sessions of advanced task 5: anastomosis in LAP Mentor, before and after completing the tutorial, and were evaluated on 34 surgical performance indicators.Results: The results show that six surgical performance indicators significantly changed after LAP Mentor training. The surgeons performed the task significantly faster as the median ‘total time’ significantly reduced (p < 0.05) from 759.5 to 523.5 seconds. Significant decreases (p < 0.05) were also found in median ‘total needle loading time’ (303.3 to 107.8 seconds), ‘average needle loading time’ (38.5 to 31.0 seconds), ‘number of passages in which the needle passed precisely through the entrance dots’ (2.5 to 1.0), ‘time the needle was held outside the visible field’ (20.9 to 2.4 seconds), and ‘total time the needle-holders’ ends are kept outside the predefined operative field’ (88.2 to 49.6 seconds).Discussion: This study raises the possibility of using virtual reality training simulation as a benchmark tool to assess the surgical performance of Portuguese surgeons.Conclusion: LAP Mentor is able to identify variations in surgical performance indicators of digestive anastomosis.

scholarly journals P.079 Development of a performance model for virtual reality tumor resections

2018 ◽  
Vol 45 (s2) ◽  
pp. S37-S37 ◽  
Author(s):  
R Sawaya ◽  
G Alsideiri ◽  
A Bugdadi ◽  
A Winkler-Schwartz ◽  
H Azarnoush ◽  
...  
Keyword(s):  
Virtual Reality ◽  
Brain Tumor ◽  
Medical Students ◽  
Tumor Volume ◽  
Performance Model ◽  
Total Force ◽  
Haptic Information ◽  
Tumor Removal ◽  
Hypothetical Model ◽  
And Task

Background: This work proposes a hypothetical model that integrates human factors (e.g. inherent ability and acquired expertise) and task factors (e.g. pre-procedural data, visual and haptic information) to better understand the hand ergonomics adaptation needed for optimal safety and efficiency during simulated brain tumor resections. Methods: Hand ergonomics of neurosurgeons, residents and medical students were assessed during simulated brain tumors resection on the NeuroVR virtual reality neurosurgical simulation platform. Spatial distribution of time expended, force applied, and tumor volume removed, and other metrics were analyzed in each tumor quadrant (Q1 to Q4). Results: Significant differences were observed between the most favorable hand ergonomics condition (Q2) and the unfavorable hand ergonomics condition (Q4). Neurosurgeons applied more total force, more mean force, and removed less tumor per unit of force applied in Q4. However, total volume removed was not significant between the two quadrants indicating hand ergonomics adaptation in order to maximize tumor removal. In comparison, hand ergonomics of medical students remained unchanged in all quadrants, indicating a learning phenomenon. Conclusions: Neurosurgeons are more capable of adapting their hand ergonomics during simulated brain tumor resections. Our proposed hypothetical model integrates our findings with the literature and highlights the importance of experience in the acquisition of adaptive hand ergonomics.

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