IU Indianapolis ScholarWorks :: Browsing by Author "Yu, Denny"

Browsing by Author "Yu, Denny"

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A neurotechnological aid for semi-autonomous suction in robotic-assisted surgery
(Springer, 2022-03-16) Barragan, Juan Antonio; Yang, Jing; Yu, Denny; Wachs, Juan P.; Surgery, School of Medicine
Adoption of robotic-assisted surgery has steadily increased as it improves the surgeon’s dexterity and visualization. Despite these advantages, the success of a robotic procedure is highly dependent on the availability of a proficient surgical assistant that can collaborate with the surgeon. With the introduction of novel medical devices, the surgeon has taken over some of the surgical assistant’s tasks to increase their independence. This, however, has also resulted in surgeons experiencing higher levels of cognitive demands that can lead to reduced performance. In this work, we proposed a neurotechnology-based semi-autonomous assistant to release the main surgeon of the additional cognitive demands of a critical support task: blood suction. To create a more synergistic collaboration between the surgeon and the robotic assistant, a real-time cognitive workload assessment system based on EEG signals and eye-tracking was introduced. A computational experiment demonstrates that cognitive workload can be effectively detected with an 80% accuracy. Then, we show how the surgical performance can be improved by using the neurotechnological autonomous assistant as a close feedback loop to prevent states of high cognitive demands. Our findings highlight the potential of utilizing real-time cognitive workload assessments to improve the collaboration between an autonomous algorithm and the surgeon.
An Adaptive Human-Robotic Interaction Architecture for Augmenting Surgery Performance Using Real-Time Workload Sensing—Demonstration of a Semi-autonomous Suction Tool
(Sage, 2024) Yang, Jing; Barragan, Juan Antonio; Farrow, Jason Michael; Sundaram, Chandru P.; Wachs, Juan P.; Yu, Denny; Urology, School of Medicine
Objective: This study developed and evaluated a mental workload-based adaptive automation (MWL-AA) that monitors surgeon cognitive load and assist during cognitively demanding tasks and assists surgeons in robotic-assisted surgery (RAS). Background: The introduction of RAS makes operators overwhelmed. The need for precise, continuous assessment of human mental workload (MWL) states is important to identify when the interventions should be delivered to moderate operators' MWL. Method: The MWL-AA presented in this study was a semi-autonomous suction tool. The first experiment recruited ten participants to perform surgical tasks under different MWL levels. The physiological responses were captured and used to develop a real-time multi-sensing model for MWL detection. The second experiment evaluated the effectiveness of the MWL-AA, where nine brand-new surgical trainees performed the surgical task with and without the MWL-AA. Mixed effect models were used to compare task performance, objective- and subjective-measured MWL. Results: The proposed system predicted high MWL hemorrhage conditions with an accuracy of 77.9%. For the MWL-AA evaluation, the surgeons' gaze behaviors and brain activities suggested lower perceived MWL with MWL-AA than without. This was further supported by lower self-reported MWL and better task performance in the task condition with MWL-AA. Conclusion: A MWL-AA systems can reduce surgeons' workload and improve performance in a high-stress hemorrhaging scenario. Findings highlight the potential of utilizing MWL-AA to enhance the collaboration between the autonomous system and surgeons. Developing a robust and personalized MWL-AA is the first step that can be used do develop additional use cases in future studies. Application: The proposed framework can be expanded and applied to more complex environments to improve human-robot collaboration.
Eye-Tracking Metrics Predict Perceived Workload in Robotic Surgical Skills Training
(Sage, 2019) Wu, Chuhao; Cha, Jackie; Sulek, Jay; Zhou, Tian; Sundaram, Chandru P.; Wachs, Juan; Yu, Denny; Urology, School of Medicine
Objective: The aim of this study is to assess the relationship between eye-tracking measures and perceived workload in robotic surgical tasks. Background: Robotic techniques provide improved dexterity, stereoscopic vision, and ergonomic control system over laparoscopic surgery, but the complexity of the interfaces and operations may pose new challenges to surgeons and compromise patient safety. Limited studies have objectively quantified workload and its impact on performance in robotic surgery. Although not yet implemented in robotic surgery, minimally intrusive and continuous eye-tracking metrics have been shown to be sensitive to changes in workload in other domains. Methods: Eight surgical trainees participated in 15 robotic skills simulation sessions. In each session, participants performed up to 12 simulated exercises. Correlation and mixed-effects analyses were conducted to explore the relationships between eye-tracking metrics and perceived workload. Machine learning classifiers were used to determine the sensitivity of differentiating between low and high workload with eye-tracking features. Results: Gaze entropy increased as perceived workload increased, with a correlation of .51. Pupil diameter and gaze entropy distinguished differences in workload between task difficulty levels, and both metrics increased as task level difficulty increased. The classification model using eye-tracking features achieved an accuracy of 84.7% in predicting workload levels. Conclusion: Eye-tracking measures can detect perceived workload during robotic tasks. They can potentially be used to identify task contributors to high workload and provide measures for robotic surgery training. Application: Workload assessment can be used for real-time monitoring of workload in robotic surgical training and provide assessments for performance and learning.
Identifying Barriers and Facilitators of Exoskeleton Implementation in the Operating Room
(Sage, 2019) Cha, Jackie S.; Monfared, Sara; Ecker, Kaylee; Lee, Derek; Stefanidis, Dimitrios; Nussbaum, Maury A.; Yu, Denny; Surgery, School of Medicine
Members of the surgical team experience musculoskeletal (MS) symptoms that impact occupational health. Although the prevalence of MS symptoms in this population is well-recognized, limited interventions with sustained success exist for the operating room (OR) environment. The purpose of this work was to determine the facilitators of and barriers to exoskeleton technology in the OR, as a potential intervention to reduce upper-body MS pain and discomfort for surgical team members.
Measurement of Nontechnical Skills During Robotic-Assisted Surgery Using Sensor-Based Communication and Proximity Metrics
(American Medical Association, 2021-11-01) Cha, Jackie S.; Athanasiadis, Dimitrios; Anton, Nicholas E.; Stefanidis, Dimitrios; Yu, Denny; Surgery, School of Medicine
This cohort study uses sensor-based communication and proximity metrics to assess surgeon nontechnical skills during robotic-assisted surgery.
Physiological Metrics of Surgical Difficulty and Multi-Task Requirement during Robotic Surgery Skills
(MDPI, 2023-04-28) Lim, Chiho; Barragan, Juan Antonio; Farrow, Jason Michael; Wachs, Juan P.; Sundaram, Chandru P.; Yu, Denny; Urology, School of Medicine
Previous studies in robotic-assisted surgery (RAS) have studied cognitive workload by modulating surgical task difficulty, and many of these studies have relied on self-reported workload measurements. However, contributors to and their effects on cognitive workload are complex and may not be sufficiently summarized by changes in task difficulty alone. This study aims to understand how multi-task requirement contributes to the prediction of cognitive load in RAS under different task difficulties. Multimodal physiological signals (EEG, eye-tracking, HRV) were collected as university students performed simulated RAS tasks consisting of two types of surgical task difficulty under three different multi-task requirement levels. EEG spectral analysis was sensitive enough to distinguish the degree of cognitive workload under both surgical conditions (surgical task difficulty/multi-task requirement). In addition, eye-tracking measurements showed differences under both conditions, but significant differences of HRV were observed in only multi-task requirement conditions. Multimodal-based neural network models have achieved up to 79% accuracy for both surgical conditions.
Sensor-based indicators of performance changes between sessions during robotic surgery training
(Elsevier, 2021) Wu, Chuhao; Cha, Jackie; Sulek, Jay; Sundaram, Chandru P.; Wachs, Juan; Proctor, Robert W.; Yu, Denny; Urology, School of Medicine
Training of surgeons is essential for safe and effective usage of robotic surgery, yet current assessment tools for learning progression are limited. The objective of this study was to measure changes in trainees’ cognitive and behavioral states as they progressed in a robotic surgeon training curriculum at a medical institution. Seven surgical trainees in urology who had no formal robotic training experience participated in the simulation curriculum. They performed 12 robotic skills exercises with varying levels of difficulty repetitively in separate sessions. EEG (electroencephalogram) activity and eye movements were measured throughout to calculate three metrics: engagement index (indicator of task engagement), pupil diameter (indicator of mental workload) and gaze entropy (indicator of randomness in gaze pattern). Performance scores (completion of task goals) and mental workload ratings (NASA-Task Load Index) were collected after each exercise. Changes in performance scores between training sessions were calculated. Analysis of variance, repeated measures correlation, and machine learning classification were used to diagnose how cognitive and behavioral states associate with performance increases or decreases between sessions. The changes in performance were correlated with changes in engagement index (rrm = −.25, p < .001) and gaze entropy (rrm = −.37, p < .001). Changes in cognitive and behavioral states were able to predict training outcomes with 72.5% accuracy. Findings suggest that cognitive and behavioral metrics correlate with changes in performance between sessions. These measures can complement current feedback tools used by medical educators and learners for skills assessment in robotic surgery training.
Supporting Surgical Teams: Identifying Needs and Barriers for Exoskeleton Implementation in the Operating Room
(Sage, 2020) Cha, Jackie S.; Monfared, Sara; Stefanidis, Dimitrios; Nussbaum, Maury A.; Yu, Denny; Surgery, School of Medicine
Objective: The objective of this study was to identify potential needs and barriers related to using exoskeletons to decrease musculoskeletal (MS) symptoms for workers in the operating room (OR). Background: MS symptoms and injuries adversely impact worker health and performance in surgical environments. Half of the surgical team members (e.g., surgeons, nurses, trainees) report MS symptoms during and after surgery. Although the ergonomic risks in surgery are well recognized, little has been done to develop and sustain effective interventions. Method: Surgical team members (n = 14) participated in focus groups, performed a 10-min simulated surgical task with a commercial upper-body exoskeleton, and then completed a usability questionnaire. Content analysis was conducted to determine relevant themes. Results: Four themes were identified: (1) characteristics of individuals, (2) perceived benefits, (3) environmental/societal factors, and (4) intervention characteristics. Participants noted that exoskeletons would benefit workers who stand in prolonged, static postures (e.g., holding instruments for visualization) and indicated that they could foresee a long-term decrease in MS symptoms with the intervention. Specifically, raising awareness of exoskeletons for early-career workers and obtaining buy-in from team members may increase future adoption of this technology. Mean participant responses from the System Usability Scale was 81.3 out of 100 (SD = 8.1), which was in the acceptable range of usability. Conclusion: Adoption factors were identified to implement exoskeletons in the OR, such as the indicated need for exoskeletons and usability. Exoskeletons may be beneficial in the OR, but barriers such as maintenance and safety to adoption will need to be addressed. Application: Findings from this work identify facilitators and barriers for sustained implementation of exoskeletons by surgical teams.
Use of non-technical skills can predict medical student performance in acute care simulated scenarios
(Elsevier, 2018) Cha, Jackie S.; Anton, Nicholas E.; Mizota, Tomoko; Hennings, Julie M.; Rendina, Megan A.; Stanton-Maxey, Katie; Ritter, Hadley E.; Stefanidis, Dimitrios; Yu, Denny; Surgery, School of Medicine
Background Though the importance of physician non-technical (NT) skills for safe patient care is recognized, NT skills of medical students, our future physicians, has received little attention. This study aims to investigate the relationship of medical student NT skills and clinical performance during acute care team simulation (ACTS). Methods Forty-one medical students participated in ACTS. A nurse confederate facilitated and evaluated clinical performance. Two raters assessed participants’ NT skills using an adapted NT assessment tool and overall NT skills score was calculated. Regressions predicting clinical performance using NT constructs were conducted. Results Overall NT skills score significantly predicted students’ clinical performance (r2 = 0.178, p = 0.006). Four of the five individual NT constructs also significantly predicted performance: communication (r2 = 0.120, p = 0.027), situation awareness (r2 = 0.323, p < 0.001), leadership (r2 = 0.133, p = 0.019), and decision making (r2 = 0.163, p = 0.009). Conclusions Medical student NT skills can predict clinical performance during ACTS. NT skills assessments can be used for targeted education for better feedback to students.

Browsing by Author "Yu, Denny"

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