Learning Robotic Skills via Self-Imitation and Guide Reward

Author(s):  
Chenyang Ran ◽  
Jianbo Su
Keyword(s):  
Author(s):  
Sarwat B. Ahmad ◽  
MaryJoe Rice ◽  
Cecilia Chang ◽  
Ahmad Hamad ◽  
T. Peter Kingham ◽  
...  

Author(s):  
Christopher W. Seder ◽  
Stephen D. Cassivi ◽  
Dennis A. Wigle

Objective Although robotic technology has addressed many of the limitations of traditional videoscopic surgery, robotic surgery has not gained widespread acceptance in the general thoracic community. We report our initial robotic surgery experience and propose a structured, competency-based pathway for the development of robotic skills. Methods Between December 2008 and February 2012, a total of 79 robot-assisted pulmonary, mediastinal, benign esophageal, or diaphragmatic procedures were performed. Data on patient characteristics and perioperative outcomes were retrospectively collected and analyzed. During the study period, one surgeon and three residents participated in a triphasic, competency-based pathway designed to teach robotic skills. The pathway consisted of individual preclinical learning followed by mentored preclinical exercises and progressive clinical responsibility. Results The robot-assisted procedures performed included lung resection (n = 38), mediastinal mass resection (n = 19), hiatal or para-esophageal hernia repair (n = 12), and Heller myotomy (n = 7), among others (n = 3). There were no perioperative mortalities, with a 20% complication rate and a 3% readmission rate. Conversion to a thoracoscopic or open approach was required in eight pulmonary resections to facilitate dissection (six) or to control hemorrhage (two). Fewer major perioperative complications were observed in the later half of the experience. All residents who participated in the thoracic surgery robotic pathway perform robot-assisted procedures as part of their clinical practice. Conclusions Robot-assisted thoracic surgery can be safely learned when skill acquisition is guided by a structured, competency-based pathway.


Acta Medica ◽  
2021 ◽  
pp. 1-4
Author(s):  
Ahmet Gudeloglu ◽  
Sijo Parekattil

Objective: Robotic surgery presents the state of the art surgical techniques in the era of minimally invasive surgery. A nurse’s role in surgery has been altered with the development of robotics. Our unique program at Polk State College in Florida was a robotic nursing program in which we certified nurses after a great deal of training. In this study our goal was to assess the survey outcomes of this program and to see if there was room for any improvements. Materials and Methods: We have successfully completed 4 three-day courses. During these courses we trained a total of 30 nurses and technicians. This special three-day course involved learning through online modules, didactic education, hands on training, and live surgery. We asked for their response to various questions about the course through an online survey. The trainees were asked to rank the questions about the program. Results: We obtained 20 out of 30 responses from our certified trainees. Seventy-five percent of the certified trainees agree that this robotics nursing program has helped them advance in a career. Overall, 85% of the certified trainees stated that this program was beneficial to them, and 95% agree that they would recommend this robotic nursing program to others. Conclusion: This survey demonstrated a well-designed robotics nurse-training program might help trainees to gain robotic skills. Also, they declared that their certification helped them make some kind of advance in their career.


2015 ◽  
Vol 29 (11) ◽  
pp. 3261-3266 ◽  
Author(s):  
Monty A. Aghazadeh ◽  
Isuru S. Jayaratna ◽  
Andrew J. Hung ◽  
Michael M. Pan ◽  
Mihir M. Desai ◽  
...  

Author(s):  
Chuhao Wu ◽  
Jackie Cha ◽  
Jay Sulek ◽  
Tian Zhou ◽  
Chandru P. Sundaram ◽  
...  

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.


2013 ◽  
Vol 185 (2) ◽  
pp. 561-569 ◽  
Author(s):  
Ashirwad J. Chowriappa ◽  
Yi Shi ◽  
Syed Johar Raza ◽  
Kamran Ahmed ◽  
Andrew Stegemann ◽  
...  

2015 ◽  
Vol 22 (6) ◽  
pp. S20
Author(s):  
L Thomaier ◽  
M Abernethy ◽  
C Paka ◽  
CCG Chen
Keyword(s):  

2017 ◽  
Vol 31 (8) ◽  
pp. 3306-3312 ◽  
Author(s):  
Megan S. Orlando ◽  
Lauren Thomaier ◽  
Melinda G. Abernethy ◽  
Chi Chiung Grace Chen

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