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Zhou XH, Xie XL, Liu SQ, Ni ZL, Zhou YJ, Li RQ, Gui MJ, Fan CC, Feng ZQ, Bian GB, Hou ZG. Learning Skill Characteristics From Manipulations. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2023; 34:9727-9741. [PMID: 35333726 DOI: 10.1109/tnnls.2022.3160159] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Percutaneous coronary intervention (PCI) has increasingly become the main treatment for coronary artery disease. The procedure requires high experienced skills and dexterous manipulations. However, there are few techniques to model PCI skill so far. In this study, a learning framework with local and ensemble learning is proposed to learn skill characteristics of different skill-level subjects from their PCI manipulations. Ten interventional cardiologists (four experts and six novices) were recruited to deliver a medical guidewire to two target arteries on a porcine model for in vivo studies. Simultaneously, translation and twist manipulations of thumb, forefinger, and wrist are acquired with electromagnetic (EM) and fiber-optic bend (FOB) sensors, respectively. These behavior data are then processed with wavelet packet decomposition (WPD) under 1-10 levels for feature extraction. The feature vectors are further fed into three candidate individual classifiers in the local learning layer. Furthermore, the local learning results from different manipulation behaviors are fused in the ensemble learning layer with three rule-based ensemble learning algorithms. In subject-dependent skill characteristics learning, the ensemble learning can achieve 100% accuracy, significantly outperforming the best local result (90%). Furthermore, ensemble learning can also maintain 73% accuracy in subject-independent schemes. These promising results demonstrate the great potential of the proposed method to facilitate skill learning in surgical robotics and skill assessment in clinical practice.
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Shabir D, Anbatawi M, Padhan J, Balakrishnan S, Al‐Ansari A, Abinahed J, Tsiamyrtzis P, Yaacoub E, Mohammed A, Deng Z, Navkar NV. Evaluation of user‐interfaces for controlling movements of virtual minimally invasive surgical instruments. Int J Med Robot 2022; 18:e2414. [DOI: 10.1002/rcs.2414] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2022] [Revised: 04/10/2022] [Accepted: 04/27/2022] [Indexed: 11/10/2022]
Affiliation(s)
- Dehlela Shabir
- Department of Surgery Hamad Medical Corporation Doha Qatar
| | - Malek Anbatawi
- Department of Surgery Hamad Medical Corporation Doha Qatar
| | | | | | | | | | | | - Elias Yaacoub
- Department of Computer Science and Engineering Qatar University Doha Qatar
| | - Amr Mohammed
- Department of Computer Science and Engineering Qatar University Doha Qatar
| | - Zhigang Deng
- Department of Computer Science University of Houston Houston Texas USA
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Kil I, Eidt JF, Groff RE, Singapogu RB. Assessment of open surgery suturing skill: Simulator platform, force-based, and motion-based metrics. Front Med (Lausanne) 2022; 9:897219. [PMID: 36111107 PMCID: PMC9468321 DOI: 10.3389/fmed.2022.897219] [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: 03/15/2022] [Accepted: 08/05/2022] [Indexed: 11/29/2022] Open
Abstract
Objective This paper focuses on simulator-based assessment of open surgery suturing skill. We introduce a new surgical simulator designed to collect synchronized force, motion, video and touch data during a radial suturing task adapted from the Fundamentals of Vascular Surgery (FVS) skill assessment. The synchronized data is analyzed to extract objective metrics for suturing skill assessment. Methods The simulator has a camera positioned underneath the suturing membrane, enabling visual tracking of the needle during suturing. Needle tracking data enables extraction of meaningful metrics related to both the process and the product of the suturing task. To better simulate surgical conditions, the height of the system and the depth of the membrane are both adjustable. Metrics for assessment of suturing skill based on force/torque, motion, and physical contact are presented. Experimental data are presented from a study comparing attending surgeons and surgery residents. Results Analysis shows force metrics (absolute maximum force/torque in z-direction), motion metrics (yaw, pitch, roll), physical contact metric, and image-enabled force metrics (orthogonal and tangential forces) are found to be statistically significant in differentiating suturing skill between attendings and residents. Conclusion and significance The results suggest that this simulator and accompanying metrics could serve as a useful tool for assessing and teaching open surgery suturing skill.
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Affiliation(s)
- Irfan Kil
- Department of Electrical and Computer Engineering, Clemson University, Clemson, SC, United States
| | - John F. Eidt
- Division of Vascular Surgery, Baylor Scott & White Heart and Vascular Hospital, Dallas, TX, United States
| | - Richard E. Groff
- Department of Electrical and Computer Engineering, Clemson University, Clemson, SC, United States
| | - Ravikiran B. Singapogu
- Department of Bioengineering, Clemson University, Clemson, SC, United States
- *Correspondence: Ravikiran B. Singapogu
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Soangra R, Jiang P, Haik D, Xu P, Brevik A, Peta A, Tapiero S, Landman J, John EB, Clayman R. Beyond Efficiency: Surface Electromyography Enables Further Insights into the Surgical Movements of Urologists. J Endourol 2022; 36:1355-1361. [PMID: 35726396 DOI: 10.1089/end.2022.0120] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
INTRODUCTION Surgical skill evaluation while performing minimally invasive surgeries is a highly complex task. It is important to objectively assess an individual's technical skills throughout surgical training to monitor progress and to intervene when skills are not commensurate with the year of training. The miniaturization of wireless wearable platforms integrated with sensor technology has made it possible to non-invasively assess muscle activations and movement variability during performance of minimally invasive surgical tasks. Our objective was to use electromyography to deconstruct the motions of a surgeon during robotic suturing and distinguish quantifiable movements that characterize the skill of an experienced, expert urologic surgeon from trainees. METHODS Three skill groups of participants: novice (n=11), intermediate (n=12) and expert (n=3) were enrolled in the study. A total of 12 wireless wearable sensors consisting of surface electromyograms (EMGs) and accelerometers were placed along upper extremity muscles to assess muscle activations and movement variability, respectively. Participants then performed a robotic suturing task. RESULTS EMG-based parameters: total time, dominant frequency, cumulative muscular workload (CMW were significantly different across the three skill groups. We also found nonlinear movement variability parameters such as correlation dimension, Lyapunov exponent trended differently across the three skill groups. CONCLUSIONS These findings suggest that economy of motion variables and nonlinear movement variabilities are affected by surgical experience level. Wearable sensor signal analysis could make it possible to objectively evaluate surgical skill level periodically throughout the residency training experience.
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Affiliation(s)
- Rahul Soangra
- Chapman University System, 240092, Orange, California, United States;
| | - Pengbo Jiang
- University of California Irvine, 8788, Urology, Irvine, California, United States;
| | - Daniel Haik
- University of California Irvine, 8788, Irvine, California, United States;
| | - Perry Xu
- University of California Irvine, 8788, 3800 Chapman Avenue - Suite 7200, Irvine, California, United States, 92697;
| | - Andrew Brevik
- University of California Irvine, 8788, Urology, 333 City Blvd West, Orange, California, United States, 92868.,Kansas City University of Medicine and Biosciences, 32959, Kansas City, Missouri, United States, 64106-1453;
| | - Akhil Peta
- University of California Irvine, 8788, Urology, 333 City Blvd, Suite 2170, Orange, California, United States, 92868;
| | - Shlomi Tapiero
- University of California Irvine, 8788, Urology, 333 City Blvd W, Suite 2100, Irvine, California, United States, 92697;
| | - Jaime Landman
- University of California Irvine, 8788, Urology, Orange, California, United States;
| | | | - Ralph Clayman
- University of California Irvine, 8788, Urology, Orange, California, United States;
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Zhou XH, Xie XL, Feng ZQ, Hou ZG, Bian GB, Li RQ, Ni ZL, Liu SQ, Zhou YJ. A Multilayer and Multimodal-Fusion Architecture for Simultaneous Recognition of Endovascular Manipulations and Assessment of Technical Skills. IEEE TRANSACTIONS ON CYBERNETICS 2022; 52:2565-2577. [PMID: 32697730 DOI: 10.1109/tcyb.2020.3004653] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
The clinical success of the percutaneous coronary intervention (PCI) is highly dependent on endovascular manipulation skills and dexterous manipulation strategies of interventionalists. However, the analysis of endovascular manipulations and related discussion for technical skill assessment are limited. In this study, a multilayer and multimodal-fusion architecture is proposed to recognize six typical endovascular manipulations. The synchronously acquired multimodal motion signals from ten subjects are used as the inputs of the architecture independently. Six classification-based and two rule-based fusion algorithms are evaluated for performance comparisons. The recognition metrics under the determined architecture are further used to assess technical skills. The experimental results indicate that the proposed architecture can achieve the overall accuracy of 96.41%, much higher than that of a single-layer recognition architecture (92.85%). In addition, the multimodal fusion brings significant performance improvement in comparison with single-modal schemes. Furthermore, the K -means-based skill assessment can obtain an accuracy of 95% to cluster the attempts made by different skill-level groups. These hopeful results indicate the great possibility of the architecture to facilitate clinical skill assessment and skill learning.
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Shabir D, Abdurahiman N, Padhan J, Anbatawi M, Trinh M, Balakrishnan S, Al-Ansari A, Yaacoub E, Deng Z, Erbad A, Mohammed A, Navkar NV. Preliminary design and evaluation of a remote tele-mentoring system for minimally invasive surgery. Surg Endosc 2022; 36:3663-3674. [PMID: 35246742 PMCID: PMC9001542 DOI: 10.1007/s00464-022-09164-3] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2021] [Accepted: 02/18/2022] [Indexed: 12/26/2022]
Abstract
BACKGROUND Tele-mentoring during surgery facilitates the transfer of surgical knowledge from a mentor (specialist surgeon) to a mentee (operating surgeon). The aim of this work is to develop a tele-mentoring system tailored for minimally invasive surgery (MIS) where the mentor can remotely demonstrate to the mentee the required motion of the surgical instruments. METHODS A remote tele-mentoring system is implemented that generates visual cues in the form of virtual surgical instrument motion overlaid onto the live view of the operative field. The technical performance of the system is evaluated in a simulated environment, where the operating room and the central location of the mentor were physically located in different countries and connected over the internet. In addition, a user study was performed to assess the system as a mentoring tool. RESULTS On average, it took 260 ms to send a view of the operative field of 1920 × 1080 resolution from the operating room to the central location of the mentor and an average of 132 ms to receive the motion of virtual surgical instruments from the central location to the operating room. The user study showed that it is feasible for the mentor to demonstrate and for the mentee to understand and replicate the motion of surgical instruments. CONCLUSION The work demonstrates the feasibility of transferring information over the internet from a mentor to a mentee in the form of virtual surgical instruments. Their motion is overlaid onto the live view of the operative field enabling real-time interactions between both the surgeons.
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Affiliation(s)
- Dehlela Shabir
- Department of Surgery, Surgical Research Section, Hamad General Hospital, Hamad Medical Corporation, PO Box 3050, Doha, Qatar
| | - Nihal Abdurahiman
- Department of Surgery, Surgical Research Section, Hamad General Hospital, Hamad Medical Corporation, PO Box 3050, Doha, Qatar
| | - Jhasketan Padhan
- Department of Surgery, Surgical Research Section, Hamad General Hospital, Hamad Medical Corporation, PO Box 3050, Doha, Qatar
| | - Malek Anbatawi
- Department of Surgery, Surgical Research Section, Hamad General Hospital, Hamad Medical Corporation, PO Box 3050, Doha, Qatar
| | - May Trinh
- Department of Computer Science, University of Houston, Houston, TX, USA
| | - Shidin Balakrishnan
- Department of Surgery, Surgical Research Section, Hamad General Hospital, Hamad Medical Corporation, PO Box 3050, Doha, Qatar
| | - Abdulla Al-Ansari
- Department of Surgery, Surgical Research Section, Hamad General Hospital, Hamad Medical Corporation, PO Box 3050, Doha, Qatar
| | - Elias Yaacoub
- Department of Computer Science and Engineering, Qatar University, Doha, Qatar
| | - Zhigang Deng
- Department of Computer Science, University of Houston, Houston, TX, USA
| | - Aiman Erbad
- College of Science and Engineering, Hamad Bin Khalifa University, Doha, Qatar
| | - Amr Mohammed
- Department of Computer Science and Engineering, Qatar University, Doha, Qatar
| | - Nikhil V Navkar
- Department of Surgery, Surgical Research Section, Hamad General Hospital, Hamad Medical Corporation, PO Box 3050, Doha, Qatar.
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Hannah TC, Turner D, Kellner R, Bederson J, Putrino D, Kellner CP. Neuromonitoring Correlates of Expertise Level in Surgical Performers: A Systematic Review. Front Hum Neurosci 2022; 16:705238. [PMID: 35250509 PMCID: PMC8888846 DOI: 10.3389/fnhum.2022.705238] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2021] [Accepted: 01/25/2022] [Indexed: 12/02/2022] Open
Abstract
Surgical expertise does not have a clear definition and is often culturally associated with power, authority, prestige, and case number rather than more objective proxies of excellence. Multiple models of expertise progression have been proposed including the Dreyfus model, however, they all currently require subjective evaluation of skill. Recently, efforts have been made to improve the ways in which surgical excellence is measured and expertise is defined using artificial intelligence, video recordings, and accelerometers. However, these aforementioned methods of assessment are still subjective or indirect proxies of expertise, thus uncovering the neural mechanisms that differentiate expert surgeons from trainees may enhance the objectivity of surgical expertise validation. In fact, some researchers have already suggested that their neural imaging-based expertise classification methods outperform currently used methods of surgical skill certification such as the Fundamentals of Laparoscopic Surgery (FLS) scores. Such imaging biomarkers would not only help better identify the highest performing surgeons, but could also improve residency programs by providing more objective, evidence-based feedback and developmental milestones for those in training and perhaps act as a marker of surgical potential in medical students. Despite the potential advantages of using neural imaging in the assessment of surgical expertise, this field of research remains in its infancy. This systematic review identifies studies that have applied neuromonitoring in assessing surgical skill across levels of expertise. The goals of this review are to identify (1) the strongest neural indicators of surgical expertise, (2) the limitations of the current literature on this subject, (3) the most sensible future directions for further study. We found substantial evidence that surgical expertise can be delineated by differential activation and connectivity in the prefrontal cortex (PFC) across multiple task and neuroimaging modalities. Specifically, novices tend to have greater PFC activation than experts under standard conditions in bimanual and decision-making tasks. However, under high temporal demand tasks, experts had increased PFC activation whereas novices had decreased PFC activation. Common limitations uncovered in this review were that task difficulty was often insufficient to delineate between residents and attending. Moreover, attending level involvement was also low in multiple studies which may also have contributed to this issue. Most studies did not analyze the ability of their neuromonitoring findings to accurately classify subjects by level of expertise. Finally, the predominance of fNIRS as the neuromonitoring modality limits our ability to uncover the neural correlates of surgical expertise in non-cortical brain regions. Future studies should first strive to address these limitations. In the longer term, longitudinal within-subjects design over the course of a residency or even a career will also advance the field. Although logistically arduous, such studies would likely be most beneficial in demonstrating effects of increasing surgical expertise on regional brain activation and inter-region connectivity.
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Affiliation(s)
- Theodore C. Hannah
- Department of Neurosurgery, Icahn School of Medicine at Mount Sinai, New York, NY, United States
- *Correspondence: Theodore C. Hannah,
| | | | - Rebecca Kellner
- Department of Neurosurgery, Icahn School of Medicine at Mount Sinai, New York, NY, United States
| | - Joshua Bederson
- Department of Neurosurgery, Icahn School of Medicine at Mount Sinai, New York, NY, United States
| | - David Putrino
- Department of Rehabilitation Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, United States
| | - Christopher P. Kellner
- Department of Neurosurgery, Icahn School of Medicine at Mount Sinai, New York, NY, United States
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Olivas-Alanis LH, Calzada-Briseño RA, Segura-Ibarra V, Vázquez EV, Diaz-Elizondo JA, Flores-Villalba E, Rodriguez CA. LAPKaans: Tool-Motion Tracking and Gripping Force-Sensing Modular Smart Laparoscopic Training System. SENSORS 2020; 20:s20236937. [PMID: 33291631 PMCID: PMC7730101 DOI: 10.3390/s20236937] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/11/2020] [Revised: 11/29/2020] [Accepted: 12/01/2020] [Indexed: 01/22/2023]
Abstract
Laparoscopic surgery demands highly skilled surgeons. Traditionally, a surgeon's knowledge is acquired by operating under a mentor-trainee method. In recent years, laparoscopic simulators have gained ground as tools in skill acquisition. Despite the wide range of laparoscopic simulators available, few provide objective feedback to the trainee. Those systems with quantitative feedback tend to be high-end solutions with limited availability due to cost. A modular smart trainer was developed, combining tool-tracking and force-using employing commercially available sensors. Additionally, a force training system based on polydimethylsiloxane (PDMS) phantoms for sample stiffness differentiation is presented. This prototype was tested with 39 subjects, between novices (13), intermediates (13), and experts (13), evaluating execution differences among groups in training exercises. The estimated cost is USD $200 (components only), not including laparoscopic instruments. The motion system was tested for noise reduction and position validation with a mean error of 0.94 mm. Grasping force approximation showed a correlation of 0.9975. Furthermore, differences in phantoms stiffness effectively reflected user manipulation. Subject groups showed significant differences in execution time, accumulated distance, and mean and maximum applied grasping force. Accurate information was obtained regarding motion and force. The developed force-sensing tool can easily be transferred to a clinical setting. Further work will consist on a validation of the simulator on a wider range of tasks and a larger sample of volunteers.
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Affiliation(s)
- Luis H. Olivas-Alanis
- Tecnologico de Monterrey, Escuela de Ingeniería y Ciencias, Monterrey, Nuevo León 64849, Mexico; (L.H.O.-A.); (R.A.C.-B.); (V.S.-I.); (E.V.V.)
- Laboratorio Nacional de Manufactura Aditiva y Digital (MADIT), Apodaca, Nuevo León 66629, Mexico
| | - Ricardo A. Calzada-Briseño
- Tecnologico de Monterrey, Escuela de Ingeniería y Ciencias, Monterrey, Nuevo León 64849, Mexico; (L.H.O.-A.); (R.A.C.-B.); (V.S.-I.); (E.V.V.)
| | - Victor Segura-Ibarra
- Tecnologico de Monterrey, Escuela de Ingeniería y Ciencias, Monterrey, Nuevo León 64849, Mexico; (L.H.O.-A.); (R.A.C.-B.); (V.S.-I.); (E.V.V.)
- Laboratorio Nacional de Manufactura Aditiva y Digital (MADIT), Apodaca, Nuevo León 66629, Mexico
- Tecnologico de Monterrey, Escuela de Medicina y Ciencias de la Salud, Monterrey, Nuevo León 64710, Mexico;
| | - Elisa V. Vázquez
- Tecnologico de Monterrey, Escuela de Ingeniería y Ciencias, Monterrey, Nuevo León 64849, Mexico; (L.H.O.-A.); (R.A.C.-B.); (V.S.-I.); (E.V.V.)
| | - Jose A. Diaz-Elizondo
- Tecnologico de Monterrey, Escuela de Medicina y Ciencias de la Salud, Monterrey, Nuevo León 64710, Mexico;
| | - Eduardo Flores-Villalba
- Tecnologico de Monterrey, Escuela de Ingeniería y Ciencias, Monterrey, Nuevo León 64849, Mexico; (L.H.O.-A.); (R.A.C.-B.); (V.S.-I.); (E.V.V.)
- Laboratorio Nacional de Manufactura Aditiva y Digital (MADIT), Apodaca, Nuevo León 66629, Mexico
- Correspondence: (E.F.-V.); (C.A.R.)
| | - Ciro A. Rodriguez
- Tecnologico de Monterrey, Escuela de Ingeniería y Ciencias, Monterrey, Nuevo León 64849, Mexico; (L.H.O.-A.); (R.A.C.-B.); (V.S.-I.); (E.V.V.)
- Laboratorio Nacional de Manufactura Aditiva y Digital (MADIT), Apodaca, Nuevo León 66629, Mexico
- Correspondence: (E.F.-V.); (C.A.R.)
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Kil I, Groff RE, Singapogu RB. Surgical Suturing with Depth Constraints: Image-based Metrics to Assess Skill. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2019; 2018:4146-4149. [PMID: 30441268 DOI: 10.1109/embc.2018.8513266] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
Suturing is one of the most fundamental surgical skills, requiring careful and systematic instruction for skilled performance. In this paper, we evaluate the performance of attending surgeons and surgical residents on an open surgery suturing task to examine if the introduction of different depth levels affects their performance. A vision algorithm is used to extract metrics meaningful in the assessment of suturing skill. As subjects perform a suturing task on the platform, our vision algorithm computes metrics identified to be potentially useful in assessing suturing skill: distances from optimal entry and optimal exit points, stitch length, stitch time, idle time, needle swept area, needle tip trace distance, needle tip area, and needle sway length. Preliminary experimental data from a study with 5 attending surgeons and 7 surgical residents are presented. Results demonstrate that the metrics of distance from optimal exit points, idle time, needle swept area, needle tip trace distance, needle tip area, and needle sway length are useful in quantifying the effect of depth constraints on suturing performance.
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Zhou XH, Bian GB, Xie XL, Hou ZG, Li RQ, Zhou YJ. Qualitative and Quantitative Assessment of Technical Skills in Percutaneous Coronary Intervention: In Vivo Porcine Studies. IEEE Trans Biomed Eng 2019; 67:353-364. [PMID: 31034402 DOI: 10.1109/tbme.2019.2913431] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
Abstract
OBJECTIVE Technical skill assessment plays an important role in the professional development of an interventionalist in percutaneous coronary intervention (PCI). However, most of the traditional assessment methods are time consuming and subjective. This paper aims to develop objective assessment techniques. METHODS In this study, a natural-behavior-based assessment framework is proposed to qualitatively and quantitatively assess technical skills in PCI. In vivo porcine studies were conducted to deliver a medical guidewire to two target coronaries of left circumflex arteries by six novice and four expert interventionalists. Simultaneously, four types of natural behaviors (i.e., hand motion, proximal force, muscle activity, and finger motion) were acquired from the subjects' dominant hand and arm. The features extracted from the behaviors of different skill-level groups were compared using the Mann-Whitney U-test for effective behavior selection. The effective ones were further applied in the Gaussian-mixture-model-based qualitative assessment and Mahalanobis-distance-based quantitative assessment. RESULTS The qualitative assessment achieves an accuracy of 92% to distinguish the novice and expert attempts, which is significantly higher than that of using single guidewire motions. Furthermore, the quantitative assessment can assign objective and effective scores for all attempts, indicating high correlation ( R = 0.9225) to those obtained by traditional methods. CONCLUSION The objective, effective, and comprehensive assessment of technical skills can be provided by qualitatively and quantitatively analyzing interventionalists' natural behaviors in PCI. SIGNIFICANCE This paper suggests a novel approach for the technical skill assessment and the promising results demonstrate the great importance and effectiveness of the proposed method for promoting the development of objective assessment techniques.
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Zhou XH, Bian GB, Xie XL, Hou ZG, Qu X, Guan S. Analysis of Interventionalists' Natural Behaviors for Recognizing Motion Patterns of Endovascular Tools During Percutaneous Coronary Interventions. IEEE TRANSACTIONS ON BIOMEDICAL CIRCUITS AND SYSTEMS 2019; 13:330-342. [PMID: 30640627 DOI: 10.1109/tbcas.2019.2892411] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/09/2023]
Abstract
Many robotic platforms can indeed reduce radiation exposure to clinicians during percutaneous coronary intervention (PCI), however, interventionalists' natural manipulations are rarely involved in robot-assisted PCI. This requires more attention to analyze interventionalists' natural behaviors during conventional PCI. In this study, four types of natural behavior (i.e., muscle activity, hand motion, proximal force, and finger motion) were synchronously acquired from ten subjects while performing six typical types of guidewire manipulation. These behaviors are evaluated by a hidden Markov model (HMM) based analysis framework for relevant behavior selection. Relevant behaviors are further used as the input of two HMM-based classification frameworks to recognize guidewire motion patterns. Experimental results show that under the basic classification framework (BCF), 91.01% and 93.32% recognition accuracies can be achieved by using all behaviors and relevant behaviors, respectively. Furthermore, the hierarchical classification framework can significantly enhance the recognition ability of relevant behaviors with an accuracy of 96.39%. These promising results demonstrate great potential of proposed methods for promoting the future design of human-robot interfaces in robot-assisted PCI.
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Valsamis EM, Chouari T, O'Dowd-Booth C, Rogers B, Ricketts D. Learning curves in surgery: variables, analysis and applications. Postgrad Med J 2018; 94:525-530. [PMID: 30209180 DOI: 10.1136/postgradmedj-2018-135880] [Citation(s) in RCA: 40] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2018] [Revised: 07/23/2018] [Accepted: 08/05/2018] [Indexed: 01/01/2023]
Abstract
Learning curves graphically represent the relationship between learning effort and learning outcome. Learning curves are increasingly used in research, the design of randomised controlled trials, the assessment of competency, healthcare education and training programme design. In this review we have outlined the principles behind plotting learning curves, described the common methods used to analyse learning curves, how to interpret learning curves, the multitude of learning models, their applications and potential pitfalls, and the importance of a mathematically rigorous approach to learning curve analytics.
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Affiliation(s)
| | - Tarak Chouari
- Trauma and Orthopaedics Department, Brighton and Sussex University Hospitals, Brighton, UK
| | | | - Benedict Rogers
- Trauma and Orthopaedics Department, Brighton and Sussex University Hospitals, Brighton, UK
| | - David Ricketts
- Trauma and Orthopaedics Department, Brighton and Sussex University Hospitals, Brighton, UK
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