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Choi HS, In H. The effects of operating height and the passage of time on the end-point performance of fine manipulative tasks that require high accuracy. Front Physiol 2022; 13:944866. [PMID: 36051911 PMCID: PMC9424850 DOI: 10.3389/fphys.2022.944866] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2022] [Accepted: 07/18/2022] [Indexed: 11/13/2022] Open
Abstract
Sustained shoulder abduction, which results from an inappropriate worktable height or tool shape and long task hours, leads to an accumulation of muscle fatigue and subsequent work-related injuries in workers. It can be alleviated by controlling the table height or ergonomic tool design, but workers who are doing some types of work that require a discomfortable posture, such as minimally invasive surgery, cannot avoid these situations. Loads to the shoulder joint or muscles result in several problems, such as muscle fatigue, deterioration of proprioception or changing movement strategies of the central nervous system, and these are critical to work that requires a high accuracy of the upper extremities. Therefore, in this paper, we designed and conducted an experiment with human participants to discuss how an inappropriate height of the work-table affects the task performance of workers who are performing a fine manipulative task that requires high accuracy of the end point. We developed an apparatus that can control the height and has four touch screens to evaluate the end-point accuracy with two different heights. Eighteen adults (9 women and 9 men) participated in the experiments, and the electromyography of their shoulder muscles, their movement stability, and task performance were measured for the analysis. We found that inappropriate height of a table brings about muscle fatigue, and time elapsed for conducting tasks accelerated the phenomenon. Task performance deteriorated according to increased fatigue, and improved movement stability is not enough to compensate for these situations.
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Soleymani A, Li X, Tavakoli M. A Domain-Adapted Machine Learning Approach for Visual Evaluation and Interpretation of Robot-Assisted Surgery Skills. IEEE Robot Autom Lett 2022. [DOI: 10.1109/lra.2022.3186769] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Affiliation(s)
- Abed Soleymani
- Electrical and Computer Engineering Department, University of Alberta, Edmonton, AB, Canada
| | - Xingyu Li
- Electrical and Computer Engineering Department, University of Alberta, Edmonton, AB, Canada
| | - Mahdi Tavakoli
- Electrical and Computer Engineering Department, University of Alberta, Edmonton, AB, Canada
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Azari DP, Frasier LL, Miller BL, Pavuluri Quamme SR, Le BV, Greenberg CC, Radwin RG. Modeling Performance of Open Surgical Cases. Simul Healthc 2021; 16:e188-e193. [PMID: 34860738 DOI: 10.1097/sih.0000000000000544] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
Abstract
INTRODUCTION Previous efforts used digital video to develop computer-generated assessments of surgical hand motion economy and fluidity of motion. This study tests how well previously trained assessment models match expert ratings of suturing and tying video clips recorded in a new operating room (OR) setting. METHODS Enabled through computer vision of the hands, this study tests the applicability of assessments born out of benchtop simulations to in vivo suturing and tying tasks recorded in the OR. RESULTS Compared with expert ratings, computer-generated assessments for fluidity of motion (slope = 0.83, intercept = 1.77, R2 = 0.55) performed better than motion economy (slope = 0.73, intercept = 2.04, R2 = 0.49), although 85% of ratings for both models were within ±2 of the expert response. Neither assessment performed as well in the OR as they did on the training data. Assessments were sensitive to changing hand postures, dropped ligatures, and poor tissue contact-features typically missing from training data. Computer-generated assessment of OR tasks was contingent on a clear, consistent view of both surgeon's hands. CONCLUSIONS Computer-generated assessment may help provide formative feedback during deliberate practice, albeit with greater variability in the OR compared with benchtop simulations. Future work will benefit from expanded available bimanual video records.
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Affiliation(s)
- David P Azari
- From the Department of Industrial and Systems Engineering (D.P.A., R.G.R.); Department of Surgery (S.R.P.Q., C.C.G.), Clinical Sciences Center; Department of Urology (B.V.L.); and Duane H. and Dorothy M. Bluemke Professor in the College of Engineering (R.G.R.), University of Wisconsin-Madison, Madison, WI; Department of Surgery (L.L.F.), Penn Medicine - University of Pennsylvania Health System, Philadelphia, PA; City of Hope National Comprehensive Cancer Center (B.L.M), Duarte, CA
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Davids J, Makariou SG, Ashrafian H, Darzi A, Marcus HJ, Giannarou S. Automated Vision-Based Microsurgical Skill Analysis in Neurosurgery Using Deep Learning: Development and Preclinical Validation. World Neurosurg 2021; 149:e669-e686. [PMID: 33588081 DOI: 10.1016/j.wneu.2021.01.117] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2020] [Revised: 01/22/2021] [Accepted: 01/23/2021] [Indexed: 12/22/2022]
Abstract
BACKGROUND/OBJECTIVE Technical skill acquisition is an essential component of neurosurgical training. Educational theory suggests that optimal learning and improvement in performance depends on the provision of objective feedback. Therefore, the aim of this study was to develop a vision-based framework based on a novel representation of surgical tool motion and interactions capable of automated and objective assessment of microsurgical skill. METHODS Videos were obtained from 1 expert, 6 intermediate, and 12 novice surgeons performing arachnoid dissection in a validated clinical model using a standard operating microscope. A mask region convolutional neural network framework was used to segment the tools present within the operative field in a recorded video frame. Tool motion analysis was achieved using novel triangulation metrics. Performance of the framework in classifying skill levels was evaluated using the area under the curve and accuracy. Objective measures of classifying the surgeons' skill level were also compared using the Mann-Whitney U test, and a value of P < 0.05 was considered statistically significant. RESULTS The area under the curve was 0.977 and the accuracy was 84.21%. A number of differences were found, which included experts having a lower median dissector velocity (P = 0.0004; 190.38 ms-1 vs. 116.38 ms-1), and a smaller inter-tool tip distance (median 46.78 vs. 75.92; P = 0.0002) compared with novices. CONCLUSIONS Automated and objective analysis of microsurgery is feasible using a mask region convolutional neural network, and a novel tool motion and interaction representation. This may support technical skills training and assessment in neurosurgery.
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Affiliation(s)
- Joseph Davids
- Department of Surgery and Cancer, Hamlyn Centre for Robotic Surgery, Imperial College London, London, United Kingdom; Imperial College Healthcare NHS Trust, St. Mary's Praed St., Paddington, London, United Kingdom; Department of Neurosurgery, National Hospital for Neurology and Neurosurgery, London, United Kingdom
| | - Savvas-George Makariou
- Department of Surgery and Cancer, Hamlyn Centre for Robotic Surgery, Imperial College London, London, United Kingdom
| | - Hutan Ashrafian
- Department of Surgery and Cancer, Hamlyn Centre for Robotic Surgery, Imperial College London, London, United Kingdom; Imperial College Healthcare NHS Trust, St. Mary's Praed St., Paddington, London, United Kingdom
| | - Ara Darzi
- Department of Surgery and Cancer, Hamlyn Centre for Robotic Surgery, Imperial College London, London, United Kingdom; Imperial College Healthcare NHS Trust, St. Mary's Praed St., Paddington, London, United Kingdom
| | - Hani J Marcus
- Department of Surgery and Cancer, Hamlyn Centre for Robotic Surgery, Imperial College London, London, United Kingdom; Imperial College Healthcare NHS Trust, St. Mary's Praed St., Paddington, London, United Kingdom; Department of Neurosurgery, National Hospital for Neurology and Neurosurgery, London, United Kingdom
| | - Stamatia Giannarou
- Department of Surgery and Cancer, Hamlyn Centre for Robotic Surgery, Imperial College London, London, United Kingdom.
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Azari DP, Miller BL, Le BV, Greenberg CC, Radwin RG. Quantifying surgeon maneuevers across experience levels through marker-less hand motion kinematics of simulated surgical tasks. APPLIED ERGONOMICS 2020; 87:103136. [PMID: 32501255 DOI: 10.1016/j.apergo.2020.103136] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/31/2018] [Revised: 04/20/2020] [Accepted: 04/21/2020] [Indexed: 06/11/2023]
Abstract
This paper compares clinician hand motion for common suturing tasks across a range of experience levels and tissue types. Medical students (32), residents (41), attending surgeons (10), and retirees (2) were recorded on digital video while suturing on one of: foam, pig feet, or porcine bowel. Depending on time in position, each medical student, resident, and attending participant was classified as junior or senior, yielding six experience categories. This work focuses on trends associated with increasing tenure observed from those medical students (10), residents (15), and attendings (10) who sutured on foam, and draws comparison across tissue types where pertinent. Utilizing custom software, the two-dimensional location of each of the participant's hands were automatically recorded in every video frame, producing a rich spatiotemporal feature set. While suturing on foam, increasing clinician experience was associated with conserved path length per cycle of the non-dominant hand, significantly reducing from junior medical students (mean = 73.63 cm, sd = 33.21 cm) to senior residents (mean = 46.16 cm, sd = 14.03 cm, p = 0.015), and again between senior residents and senior attendings (mean = 30.84 cm, sd = 14.51 cm, p = 0.045). Despite similar maneuver rates, attendings also accelerated less with their non-dominant hand (mean = 16.27 cm/s2, sd = 81.12 cm/s2, p = 0.002) than senior residents (mean = 24.84 cm/s2, sd = 68.29 cm/s2, p = 0.002). While tying, medical students moved their dominant hands slower (mean = 4.39 cm/s, sd = 1.73 cm/s, p = 0.033) than senior residents (mean = 6.53 cm/s, sd = 2.52 cm/s). These results suggest that increased psychomotor performance during early training manifest through faster dominant hand function, while later increases are characterized by conserving energy and efficiently distributing work between hands. Incorporating this scalable video-based motion analysis into regular formative assessment routines may enable greater quality and consistency of feedback throughout a surgical career.
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Affiliation(s)
- David P Azari
- Department of Industrial and Systems Engineering, 1550 Engineering Drive, University of Wisconsin-Madison, Madison, WI, 53706, USA
| | - Brady L Miller
- Department of Urology, Third Floor, 1685 Highland Avenue, University of Wisconsin-Madison, Madison, WI, 53705, USA
| | - Brian V Le
- Department of Urology, Third Floor, 1685 Highland Avenue, University of Wisconsin-Madison, Madison, WI, 53705, USA
| | - Caprice C Greenberg
- Wisconsin Surgical Outcomes Research (WiSOR) Program, Department of Surgery, Clinical Science Center, 600 Highland Avenue, University of Wisconsin-Madison, Madison, WI, 53792, USA
| | - Robert G Radwin
- Department of Industrial and Systems Engineering, 1550 Engineering Drive, University of Wisconsin-Madison, Madison, WI, 53706, USA; Department of Biomedical Engineering, 1415 Engineering Drive, University of Wisconsin-Madison, Madison, WI, 53706, USA.
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Translating motion tracking data into resident feedback: An opportunity for streamlined video coaching. Am J Surg 2020; 219:552-556. [DOI: 10.1016/j.amjsurg.2020.01.032] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2019] [Revised: 11/30/2019] [Accepted: 01/19/2020] [Indexed: 11/21/2022]
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