Erratum: Robotics as a Tool for Training and Assessment of Surgical Skill

In interventional radiology, physicians require high haptic sensitivity and fine motor skills development because of the limited real-time visual feedback of the surgical site. The transfer of this type of surgical skill to novices is a challenging issue. This paper presents a study on the design of a biopsy procedure learning system. Our methodology, based on a task-centered design approach, aims to bring out new design rules for virtual learning environments. A new collaborative haptic training paradigm is introduced to support human-haptic interaction in a virtual environment. The interaction paradigm supports haptic communication between two distant users to teach a surgical skill. In order to evaluate this paradigm, a user experiment was conducted. Sixty volunteer medical students participated in the study to assess the influence of the teaching method on their performance in a biopsy procedure task. The results show that to transfer the skills, the combination of haptic communication with verbal and visual communications improves the novices’ performance compared to conventional teaching methods. Furthermore, the results show that, depending on the teaching method, participants developed different needle insertion profiles. We conclude that our interaction paradigm facilitates expert-novice haptic communication and improves skills transfer; and new skills acquisition depends on the availability of different communication channels between experts and novices. Our findings indicate that the traditional fellowship methods in surgery should evolve to an off-patient collaborative environment that will continue to support visual and verbal communication, but also haptic communication, in order to achieve a better and more complete skills training.

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Low versus high level of physical resemblance in simulation for the acquisition of basic surgical skill: a meta-analysis

BMJ Simulation and Technology Enhanced Learning ◽

10.1136/bmjstel-2020-000797 ◽

2021 ◽

pp. bmjstel-2020-000797

Author(s):

Fabrizio Consorti ◽

Gianmarco Panzera

Keyword(s):

Undergraduate Students ◽

Scoring System ◽

Meta Analysis ◽

Random Effect ◽

Random Effect Model ◽

Surgical Skill ◽

Undergraduate Medical Students ◽

Study Selection ◽

High Level ◽

Physical Resemblance

BackgroundMany studies explored the use of simulation in basic surgical education, with a variety of devices, contexts and outcomes, with sometimes contradictory results.ObjectivesThe objectives of this meta-analysis were to focus the effect that the level of physical resemblance in a simulation has on the development of basic surgical skill in undergraduate medical students and to provide a foundation for the design and implementation of a simulation, with respect to its effectiveness and alignment with the learning outcomes.Study selectionWe searched PubMed and Scopus database for comparative randomised studies between simulations with a different level of resemblance. The result was synthesised as the standardised mean difference, under a random effect model.FindingsWe selected 12 out of 2091 retrieved studies, reporting on 373 undergraduate students (mean of subjects 15.54±6.89). The outcomes were the performance of simple skills and the time to complete a task. Two studies reported a scoring system; seven studies reported time for a task; and three studies reported both. The total number of measures included in the meta-analysis was 456 for score and 504 for time. The pooled effect size did not show any significant advantage in a simulation of a high level of physical resemblance over a lower level, both for the scoring system (−0.19, 95% CI −0.44 to 0.06) and for time (−0.14, 95% CI −0.54 to 0.27).ConclusionSimulations with a low level of physical resemblance showed the same effect as the simulation using a higher level of resemblance on the development of basic surgical skills in undergraduate students.

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Effect of endoscopic surgical skill qualification system for laparoscopic multivisceral resection: Japanese multicenter analysis

Surgical Endoscopy ◽

10.1007/s00464-021-08605-9 ◽

2021 ◽

Author(s):

Hiroki Katayama ◽

Tetsuro Tominaga ◽

Takashi Nonaka ◽

Hideo Wada ◽

Kazuo To ◽

...

Keyword(s):

Multivisceral Resection ◽

Surgical Skill ◽

Multicenter Analysis

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The Efficacy of Transcranial Direct Current Stimulation in Enhancing Surgical Skill Acquisition: A Preliminary Meta-Analysis of Randomized Controlled Trials

Brain Sciences ◽

10.3390/brainsci11060707 ◽

2021 ◽

Vol 11 (6) ◽

pp. 707

Author(s):

Chao-Ming Hung ◽

Bing-Yan Zeng ◽

Bing-Syuan Zeng ◽

Cheuk-Kwan Sun ◽

Yu-Shian Cheng ◽

...

Keyword(s):

Randomized Controlled Trials ◽

Skill Acquisition ◽

Direct Current ◽

Transcranial Direct Current Stimulation ◽

Meta Analysis ◽

Controlled Trials ◽

Surgical Skill ◽

Sham Control ◽

Surgical Performance ◽

Randomized Controlled

The application of transcranial direct current stimulation (tDCS) to targeted cortices has been found to improve in skill acquisition; however, these beneficial effects remained unclear in fine and complicated skill. The aim of the current meta-analysis was to investigate the association between tDCS application and the efficacy of surgical performance during surgical skill training. We included randomized controlled trials (RCTs) investigating the efficacy of tDCS in enhancing surgical skill acquisition. This meta-analysis was conducted under a random-effect model. Six RCTs with 198 participants were included. The main result revealed that tDCS was associated with significantly better improvement in surgical performance than the sham control (Hedges’ g = 0.659, 95% confidence intervals (95%CIs) = 0.383 to 0.935, p < 0.001). The subgroups of tDCS over the bilateral prefrontal cortex (Hedges’ g = 0.900, 95%CIs = 0.419 to 1.382, p < 0.001) and the primary motor cortex (Hedges’ g = 0.599, 95%CIs = 0.245 to 0.953, p = 0.001) were both associated with significantly better improvements in surgical performance. The tDCS application was not associated with significant differences in error scores or rates of local discomfort compared with a sham control. This meta-analysis supported the rationale for the tDCS application in surgical training programs to improve surgical skill acquisition.

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Nikaidoh Procedure: The Wet Lab Trainee’s Perspective

World Journal for Pediatric and Congenital Heart Surgery ◽

10.1177/2150135120957644 ◽

2021 ◽

Vol 12 (1) ◽

pp. 124-127

Author(s):

César Castillo Romero ◽

Gabriella Ricciardi

Keyword(s):

Learning Curve ◽

Congenital Heart ◽

Surgical Skill ◽

Computer Screen ◽

Simulation Based ◽

Wet Lab ◽

Congenital Surgery

Congenital heart surgeons’ training is complex and challenging. The learning curve is long and the increasing complexity of pathologies is demanding. In order to develop adequate surgical-skill competencies, “in vivo” and simulation-based practicing are paramount. Simulation can be performed either on a computer screen or animal hearts and prosthetic models. In this article, we illustrate a porcine Wet Lab simulation for the Nikaidoh operation to point out its potential advantage to learn complex congenital surgery procedures.

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Formative Feedback Generation in a VR-based Dental Surgical Skill Training Simulator

Journal of Biomedical Informatics ◽

10.1016/j.jbi.2020.103659 ◽

2020 ◽

pp. 103659

Author(s):

Myat Su Yin ◽

Peter Haddawy ◽

Siriwan Suebnukarn ◽

Farin Kulapichitr ◽

Phattanapon Rhienmora ◽

...

Keyword(s):

Skill Training ◽

Formative Feedback ◽

Surgical Skill ◽

Training Simulator ◽

Surgical Skill Training

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Automation of surgical skill assessment using a three-stage machine learning algorithm

Scientific Reports ◽

10.1038/s41598-021-84295-6 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Joël L. Lavanchy ◽

Joel Zindel ◽

Kadir Kirtac ◽

Isabell Twick ◽

Enes Hosgor ◽

...

Keyword(s):

Machine Learning ◽

Learning Algorithm ◽

Surgical Skills ◽

Adverse Outcomes ◽

Machine Learning Algorithms ◽

Surgical Instruments ◽

Skill Assessment ◽

Surgical Skill ◽

Surgical Skill Assessment ◽

Motion Features

AbstractSurgical skills are associated with clinical outcomes. To improve surgical skills and thereby reduce adverse outcomes, continuous surgical training and feedback is required. Currently, assessment of surgical skills is a manual and time-consuming process which is prone to subjective interpretation. This study aims to automate surgical skill assessment in laparoscopic cholecystectomy videos using machine learning algorithms. To address this, a three-stage machine learning method is proposed: first, a Convolutional Neural Network was trained to identify and localize surgical instruments. Second, motion features were extracted from the detected instrument localizations throughout time. Third, a linear regression model was trained based on the extracted motion features to predict surgical skills. This three-stage modeling approach achieved an accuracy of 87 ± 0.2% in distinguishing good versus poor surgical skill. While the technique cannot reliably quantify the degree of surgical skill yet it represents an important advance towards automation of surgical skill assessment.

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