Basic Surgical Tasks Research Articles

WP8.13 - Validating Remote Basic Surgical Skills Certification: A Randomised Controlled Trial

Abstract Aims Simulation based training comprises a fundamental component of modern surgical education, necessitating the development and advancement of affordable, functional, and validated simulators. This study explored the effectiveness of using the Inovus Basic Surgical Skills (BSS) simulator in improving core surgical skills amongst junior trainees. Methods This was a multicentre, RCT between August 2022 – November 2022 involving junior trainees yet to complete a validated basic surgical skills course. All participants completed an initial series of basic surgical tasks, including a range of suturing and knot-tying techniques. Three parameters (objective scoring tool, time taken for task completion, self-assessment questionnaire) were used to evaluate each surgical skill. Subsequently, participants were randomly allocated to the intervention group (BSS simulator) for at home training, or the control group receiving no further training. Results 5 domains were assessed for suturing tasks through respect for tissue, motion and efficiency, visuo-spatial, suture quality and time taken. Knot-tying was assessed by quality and time taken. Independent samples t-test revealed no significant difference between control and intervention groups (p>0.05) despite improvement in task quality and reduced completion time. Self-assessment questionnaires revealed a significant (p<0.05) improvement in confidence performing more complex tasks in intervention groups. Conclusions Study results demonstrate an improvement in both the quality of basic surgical skills and reduced completion time with use of the BSS simulator. Participants report increased confidence post-intervention following use of the BSS simulator. Despite the limitation of a small sample size, these findings underpin the potential benefits of BSS simulator use for surgical education amongst trainees, increasing accessibility to surgical training through an exceptionally realistic remote training environment.

Correlation of Self-Assessed Manual Dexterity and Actual Performance in Surgical Simulation – One Year Follow Up

Abstract Background Simulation training has proved to be beneficial in many fields of professions including medicine and might be included already during medical school. Aims We evaluated if medical students rate their manual dexterity as good or above average and if this self-assessment (SA) correlates with the actual performance in a surgical simulation. Methods Basic surgical tasks have been performed by medical students in the 3rd and 4th year for a total of 4 rounds on 2 identical Lap-Sim® surgical simulators. Three questionnaires, before and after the simulation as well as follow up after 1 year had to be answered. Descriptive statistics stratified by career goal (surgical/ undecided/ nonsurgical) for outcomes of simulation parameters are reported. Associations between surgical career goal and SA of manual dexterity with three performance parameters (tissue damage, time and path length) are estimated in linear regression models. Results Out of 102 participating students 41 stated surgery as their career goal, 18 a nonsurgical field, and 43 were undecided. 87.8% of the surgical group rated their dexterity as good or above average (86.1% in the undecided group), 88.9% of the nonsurgical group as below average or good. A positive association of SA with tissue damage has been found statistically significant (p=0.033). One year after the simulation (n=97) 13.4% changed their career goal from undecided to surgical, whereas 8.2% changed from undecided to non-surgical. 92.7% wanted to have simulation training integrated into the curriculum. Conclusion A correlation of self-assessed manual dexterity has been found in the surgical group for tissue damage, but not for time and path length. Simulation training might help students in decision making for a surgical field and therefore recruit future surgeons, which could be shown in the one-year follow-up.

Transfer of acquired practical skills from dry lab into live surgery using the avatera robotic system: An experimental study

ObjectiveTo evaluate the transfer of the practical skills of robot-assisted surgery acquired in the dry-lab into a real live experimental setting for performing upper and lower urinary tract surgeries. Material and methodsAn in vivo experimental study design was utilized. Six urology trainees and fellows; two 2nd year trainees with no previous exposure to laparoscopic surgery (Group 1), two 4th year residents with medium exposure to laparoscopic surgery (Group 2) and two fellows trained to perform laparoscopic surgeries (Group 3) performed ureteral reimplantation into the bladder, pyeloplasty, and radical nephrectomy on three female pigs under general anesthesia. Prior to performing the requested procedures, each participant completed 10−14 h dry-lab robotic training acquiring skills in basic surgical tasks, such as suturing, cutting and needle passage. The recorded variables were the successful completion of the procedures, the console time, and the time to perform different steps and major complications. ResultsAll procedures were completed successfully by all groups except the pyeloplasty by group 1 which was complicated by bleeding from the renal vein, and the procedure was abandoned. Group 3 achieved shorter console time for all successfully completed procedures and for separate surgical steps compared to all groups, followed by Group 2. The slowest group for all procedures and steps analyzed was Group 3. ConclusionsAlthough further clinical evidence is needed, the robotic-assisted urological procedures and the most challenging steps could be performed safely and effectively after proper training in the dry lab under mentor supervision according to our study.

Transferencia de habilidades prácticas desde el laboratorio a cirugía in vivo utilizando el sistema robótico Avatera: Estudio experimental

ObjectiveTo evaluate the transfer of the practical skills of robot-assisted surgery acquired in the dry-lab into a real live experimental setting for performing upper and lower urinary tract surgeries. Material and methodsAn in vivo experimental study design was utilized. Six urology trainees and fellows; two 2nd year trainees with no previous exposure to laparoscopic surgery (Group 1), two 4th year residents with medium exposure to laparoscopic surgery (Group 2) and two fellows trained to perform laparoscopic surgeries (Group 3) performed ureteral reimplantation into the bladder, pyeloplasty, and radical nephrectomy on three female pigs under general anesthesia. Prior to performing the requested procedures, each participant completed 10-14 hours dry-lab robotic training acquiring skills in basic surgical tasks, such as suturing, cutting and needle passage. The recorded variables were the successful completion of the procedures, the console time, and the time to perform different steps and major complications. ResultsAll procedures were completed successfully by all groups except the pyeloplasty by Group 1 which was complicated by bleeding from the renal vein, and the procedure was abandoned. Group 3 achieved shorter console time for all successfully completed procedures and for separate surgical steps compared to all groups, followed by Group 2. The slowest group for all procedures and steps analyzed was Group 3. ConclusionsAlthough further clinical evidence is needed, the robotic-assisted urological procedures and the most challenging steps could be performed safely and effectively after proper training in the dry lab under mentor supervision according to our study.

Toward Correcting Anxious Movements Using Haptic Cues on the Da Vinci Surgical Robot.

Surgical movements have an important stylistic quality that individuals without formal surgical training can use to identify expertise. In our prior work, we sought to characterize quantitative metrics associated with surgical style and developed a near-real-time detection framework for stylistic deficiencies using a commercial haptic device. In this paper, we implement bimanual stylistic detection on the da Vinci Research Kit (dVRK) and focus on one stylistic deficiency, "Anxious", which may describe movements under stressful conditions. Our goal is to potentially correct these "Anxious" movements by exploring the effects of three different types of haptic cues (time-variant spring, damper, and spring-damper feedback) on performance during a basic surgical training task using the da Vinci Research Kit (dVRK). Eight subjects were recruited to complete peg transfer tasks using a randomized order of haptic cues and with baseline trials between each task. Overall, all cues lead to a significant improvement over baseline economy of volume and time-variant spring haptic cues lead to significant improvements in reducing the classified "Anxious" movements and also corresponded with significantly lower path length and economy of volume for the non-dominant hand. This work is the first step in evaluating our stylistic detection model on a surgical robot and could lay the groundwork for future methods to actively and adaptively reduce the negative effect of stress in the operating room.

MP41-13 DOES QUALITY ASSURED ELEARNING PROVIDE ADEQUATE PREPARATION FOR ROBOTIC SURGICAL SKILLS; A PROSPECTIVE, RANDOMIZED AND MULTI-CENTER STUDY

You have accessJournal of UrologyCME1 May 2022MP41-13 DOES QUALITY ASSURED ELEARNING PROVIDE ADEQUATE PREPARATION FOR ROBOTIC SURGICAL SKILLS; A PROSPECTIVE, RANDOMIZED AND MULTI-CENTER STUDY Stefano Puliatti, Marco Amato, Rui Farinha, Artur Paludo, Giuseppe Rosiello, Ruben De Groote, Andrea Mari, Lorenzo Bianchi, Pietro Piazza, Ben Van Cleynenbreugel, Elio Mazzone, Simon Meganck, Léonore Maertens, Louise Callens, Filippo Migliorini, Saverio Forte, Bernardo Rocco, Salvatore Micali, Alexandre Mottrie, and Anthony Gallagher Stefano PuliattiStefano Puliatti More articles by this author , Marco AmatoMarco Amato More articles by this author , Rui FarinhaRui Farinha More articles by this author , Artur PaludoArtur Paludo More articles by this author , Giuseppe RosielloGiuseppe Rosiello More articles by this author , Ruben De GrooteRuben De Groote More articles by this author , Andrea MariAndrea Mari More articles by this author , Lorenzo BianchiLorenzo Bianchi More articles by this author , Pietro PiazzaPietro Piazza More articles by this author , Ben Van CleynenbreugelBen Van Cleynenbreugel More articles by this author , Elio MazzoneElio Mazzone More articles by this author , Simon MeganckSimon Meganck More articles by this author , Léonore MaertensLéonore Maertens More articles by this author , Louise CallensLouise Callens More articles by this author , Filippo MiglioriniFilippo Migliorini More articles by this author , Saverio ForteSaverio Forte More articles by this author , Bernardo RoccoBernardo Rocco More articles by this author , Salvatore MicaliSalvatore Micali More articles by this author , Alexandre MottrieAlexandre Mottrie More articles by this author , and Anthony GallagherAnthony Gallagher More articles by this author View All Author Informationhttps://doi.org/10.1097/JU.0000000000002607.13AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookLinked InTwitterEmail Abstract INTRODUCTION AND OBJECTIVE: In particular after the onset of the COVID-19 pandemic, there was a precipitous rush to implement virtual and online learning strategies in surgery and medicine. It is essential to understand whether this approach is sufficient and adequate to allow the development of robotic basic surgical skills. The main aim of the authors was to verify if the quality assured eLearning is sufficient to prepare individuals to perform a basic surgical robotic task. METHODS: A prospective, randomized and multi-center study conducted in September 2020 in the ORSI Academy, International surgical robotic training center. 47 participants with no experience but a special interest in robotic surgery, were matched and randomized into 4 groups who underwent a didactic preparation with different formats before carrying out a robotic suturing and anastomosis task. Didactic preparation methods, ranged from a complete eLearning path to peer-reviewed published manuscripts describing the suturing, knot tying and task assessment metrics. RESULTS: The primary outcome was the percentage of trainees who demonstrated the quantitatively defined proficiency benchmark after learning to complete an assisted but unaided robotic vesico-urethral anastomosis task. The quantitatively defined benchmark was based on the objectively assessed performance (i.e., procedure steps completed, errors and critical errors) of experienced robotic surgeons for a proficiency based progression (PBP) training course. None of the trainees in this study demonstrated the proficiency benchmarks in completing the robotic surgery task (Figure 1a-c). CONCLUSIONS: Quality assured online learning is insufficient preparation for robotic suturing and knot tying anastomosis skills. Source of Funding: No © 2022 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 207Issue Supplement 5May 2022Page: e724 Advertisement Copyright & Permissions© 2022 by American Urological Association Education and Research, Inc.MetricsAuthor Information Stefano Puliatti More articles by this author Marco Amato More articles by this author Rui Farinha More articles by this author Artur Paludo More articles by this author Giuseppe Rosiello More articles by this author Ruben De Groote More articles by this author Andrea Mari More articles by this author Lorenzo Bianchi More articles by this author Pietro Piazza More articles by this author Ben Van Cleynenbreugel More articles by this author Elio Mazzone More articles by this author Simon Meganck More articles by this author Léonore Maertens More articles by this author Louise Callens More articles by this author Filippo Migliorini More articles by this author Saverio Forte More articles by this author Bernardo Rocco More articles by this author Salvatore Micali More articles by this author Alexandre Mottrie More articles by this author Anthony Gallagher More articles by this author Expand All Advertisement PDF DownloadLoading ...

The impact of teacher’s presence on learning basic surgical tasks with virtual reality headset among medical students

ABSTRACT Background The aim of this study was to investigate whether the presence of a teacher affects learning related outcomes in teaching basic surgical tasks with a Virtual Reality (VR) headset. Methods 26 fourth-year medical studentsparticipated in a voluntary exercise. Students practiced basic surgical procedure exercises using the VR4HEALTHCARE application in VR with OCULUS Rift S glasses. 12 students performed the exercises under the guidance of a teacher and 14 without the teacher present. After the exercise, the groups filled out a feedback form. Statistical analysis was performed using IBM SPSS Statistics 25.0 software using the Mann-Whitney U test and multivariate analysis of variance. Results The most important data collected related to whether the student learned something new and whether VR adds value to medical education. Ratings were based on a scale of 0–10 (0 = worst, 10 = best). When the teacher was present, on average, the students felt that they were learning something new and gave an average rating of 7.8 ± 1.8, and when the teacher was not present 5.3 ± 2.6 (p = 0.003). VR added value to teaching with a rating of 7.8 ± 1.7 when the teacher was present and 5.5 ± 3.0 when not present (p = 0.045). This study also analyzed specific use of VR for abscess incision, suturing and insertion of a suprapubic catheter. Discussion When a teacher was present VR added value to teaching and the usefulness and usability of VR was experienced more positively. The student should also have adequate knowledge of the subject to be taught before VR training. Conclusions VR adds value to teaching, but VR exercises may not completely replace high-quality traditional teaching methods. Consequently, it is important to determine the differences between VR and traditional teaching methods and how to combine these methods in the future.

Objective Measurement of Impact of Bench Laparoscopic Training in Novices

Background: We are currently in the era of laparoscopic surgery. It has gained popularity in the last few decades because of its well- known advantages. Laparoscopy requires different skills from those of open surgery. In a paradigm shift, learning basic surgical skills is now performed more in the skills laboratory than in the operation theater. However, there is a lack of reliable training and assessment tools for laparoscopic surgical skills. This study aimed to objectively assess the effect of bench laparoscopic training in novices.
 Methods: This prospective study was conducted at the Clinical Skills Centre of Bangalore Medical College and Research Institute (BMCRI) in Bangalore, India. Sixty interns with no previous experience in laparoscopy were included. They underwent supervised training on the box trainer for 3 days, 2 hours a day, in basic surgical tasks, including pointing dots, joining straight lines, joining curved lines, picking objects, peg transfer, and circle cut. All participants were assessed objectively in a virtual reality (VR) simulator before and after training. The objective outcomes measured were time taken, distance traveled, and error scores given by the VR simulator metrics.
 Results: The novices showed statistically significant improvement in all the tasks after the training compared with their skill levels before the training.
 Conclusion: Structured short-term training significantly improves basic laparoscopic surgery skills.

Camera-Robot Calibration for the da Vinci® Robotic Surgery System.

The development of autonomous or semi-autonomous surgical robots stands to improve the performance of existing teleoperated equipment, but requires fine hand-eye calibration between the free-moving endoscopic camera and patient-side manipulator arms (PSMs). A novel method of solving this problem for the da Vinci® robotic surgical system and kinematically similar systems is presented. First, a series of image-processing and optical-tracking operations are performed to compute the coordinate transformation between the endoscopic camera view frame and an optical-tracking marker permanently affixed to the camera body. Then, the kinematic properties of the PSM are exploited to compute the coordinate transformation between the kinematic base frame of the PSM and an optical marker permanently affixed thereto. Using these transformations, it is then possible to compute the spatial relationship between the PSM and the endoscopic camera using only one tracker snapshot of the two markers. The effectiveness of this calibration is demonstrated by successfully guiding the PSM end effector to points of interest identified through the camera. Additional tests on a surgical task, namely grasping a surgical needle, are also performed to validate the proposed method. The resulting visually-guided robot positioning accuracy is better than the earlier hand-eye calibration results reported in the literature for the da Vinci® system, while supporting intraoperative update of the calibration and requiring only devices that are already commonly used in the surgical environment.

Design and Realization of a Robotic Manipulator for Minimally Invasive Surgery With Replaceable Surgical Tools

Minimally invasive surgery is a useful but challenging surgical technique. Several robotic manipulators have been realized in the past three decades to overcome its difficulties. However, a majority of these manipulators lack two important features. First, the ability to provide a right-angled tool entry (with respect to the surrounding skin tissue) for entry-ports located on the far-side of the patient's body, and second, rapid change of the surgical tools. To solve these issues, this article presents synthesis of a new surgical manipulator that ensures a right-angled tool entry—in order to reduce postoperative pain in the patient's skin tissue—through its novel design. We describe the design, perform kinematic analysis, and validate the robotic manipulator's ability to maintain a remote center of motion. The proposed manipulator is equipped with a quick-release mechanism to facilitate rapid change of the surgical instruments during robot-assisted surgery. Contrary to the state-of-the-art, the proposed manipulator does not require extra passive arms to provide a proper tool entry-angle. The manipulator's performance is experimentally evaluated by performing tool-insertion, pegs-transfer, and path-following tasks in a master–slave teleoperation setting. It is demonstrated that the manipulator can perform basic surgical tasks, such as pick-and-place and object manipulation, with a high success rate.

Multimodal Physiological Signals for Workload Prediction in Robot-assisted Surgery

Monitoring surgeon workload during robot-assisted surgery can guide allocation of task demands, adapt system interfaces, and assess the robotic system's usability. Current practices for measuring cognitive load primarily rely on questionnaires that are subjective and disrupt surgical workflow. To address this limitation, a computational framework is demonstrated to predict user workload during telerobotic surgery. This framework leverages wireless sensors to monitor surgeons’ cognitive load and predict their cognitive states. Continuous data across multiple physiological modalities (e.g., heart rate variability, electrodermal, and electroencephalogram activity) were simultaneously recorded for twelve surgeons performing surgical skills tasks on the validated da Vinci Skills Simulator. These surgical tasks varied in difficulty levels, e.g., requiring varying visual processing demand and degree of fine motor control. Collected multimodal physiological signals were fused using independent component analysis, and the predicted results were compared to the ground-truth workload level. Results compared performance of different classifiers, sensor fusion schemes, and physiological modality (i.e., prediction with single vs. multiple modalities). It was found that our multisensor approach outperformed individual signals and can correctly predict cognitive workload levels 83.2% of the time during basic and complex surgical skills tasks.

Towards inductive learning of surgical task knowledge: a preliminary case study of the peg transfer task

Abstract Autonomy in robotic surgery will significantly improve the quality of interventions in terms of safety and recovery time for the patient, and reduce fatigue of surgeons and hospital costs. A key requirement for such autonomy is the ability of the surgical system to encode and reason with commonsense task knowledge, and to adapt to variations introduced by the surgical scenarios and the individual patients. However, it is difficult to encode all the variability in surgical scenarios and in the anatomy of individual patients a priori, and new knowledge often needs to be acquired and merged with the existing knowledge. At the same time, it is not possible to provide a large number of labeled training examples in the robotic surgery. This paper presents a framework based on inductive logic programming and answer set semantics for incrementally learning domain knowledge from a limited number of executions of basic surgical tasks. As an illustrative example, we focus on the peg transfer task, and learn state constraints and the preconditions of actions starting from different levels of prior knowledge. We do so using a small dataset comprising human and robotic executions with the da Vinci surgical robot in a challenging simulated scenario.

Evaluating the Mental Workload During Robot-Assisted Surgery Utilizing Network Flexibility of Human Brain

Mental Workload (MWL) is traditionally evaluated by psychophysiological signals using spectral analysis and event-related potentials. Robot-assisted Surgery (RAS) is a complex task that involves human-robot interaction, multitasking, quick and appropriate reactions to various stimuli and unforeseen circumstances, as well as frequent switches between surgical subtasks. There is a lack of standardized methodology for objectively monitoring a surgeon's MWL during RAS. In this study, we propose an innovative framework, using dynamic functional brain network measurements and a deep convolutional neural network, to assess MWL. A model was developed and validated using Electroencephalogram (EEG) data from 22 trainees who performed basic surgical tasks, as well as four surgical fellows and an expert surgeon who carried out cystectomies and prostatectomies. The resulting accuracies of the MWL classification into low, intermediate and high were 93%, 89%, and 91% respectively. The proposed method can be used for continually monitoring mental workload levels in an objective fashion.

Sample Entropy of Speed Power Spectrum as a Measure of Laparoscopic Surgical Instrument Trajectory Smoothness.

In this study the complexity of the speed power spectrum is assessed as a metric for measuring trajectory smoothness. There are a variety of published methods for analyzing trajectory smoothness but many lack validity. This preliminary study took an information theoretic approach to assess trajectory smoothness by applying the sample entropy measure to the speed power spectrum of simulated and experimental trajectories. The complexity measurements of the speed power spectrum were compared to a traditional jerk-based measure of trajectory smoothness, namely $\log $-dimensionless jerk. The approach was first tested on basic simulated shape tracings with varying locations of sporadic movement, simulated as Gaussian noise. This method was duplicated in an experimental setting with the same shapes and locations of sporadic movement by capturing the trace trajectories using an electromagnetic motion tracking system. Finally, this approach was applied to kinematic data of laparoscopic surgical instrument tips, captured over 105 iterations of a basic surgical task. Analysis from all three testing scenarios showed that there is a statistically significant linear correlation between $\log $-dimensionless jerk and the sample entropy of speed power spectra.

Sensory-Glove-Based Open Surgery Skill Evaluation

Manual dexterity is one of the most important surgical skills, and yet there are limited instruments to evaluate this ability objectively. In this paper, we propose a system designed to track surgeons’ hand movements during simulated open surgery tasks and to evaluate their manual expertise. Eighteen participants, grouped according to their surgical experience, performed repetitions of two basic surgical tasks, namely single interrupted suture and simple running suture. Subjects’ hand movements were measured with a sensory glove equipped with flex and inertial sensors, tracking flexion/extension of hand joints, and wrist movement. The participants’ level of experience was evaluated discriminating manual performances using linear discriminant analysis, support vector machines, and artificial neural network classifiers. Artificial neural networks showed the best performance, with a median error rate of 0.61% on the classification of single interrupted sutures and of 0.57% on simple running sutures. Strategies to reduce sensory glove complexity and increase its comfort did not affect system performances substantially.

Automated surgical skill assessment in RMIS training.

Manual feedback in basic robot-assisted minimally invasive surgery (RMIS) training can consume a significant amount of time from expert surgeons' schedule and is prone to subjectivity. In this paper, we explore the usage of different holistic features for automated skill assessment using only robot kinematic data and propose a weighted feature fusion technique for improving score prediction performance. Moreover, we also propose a method for generating 'task highlights' which can give surgeons a more directed feedback regarding which segments had the most effect on the final skill score. We perform our experiments on the publicly available JHU-ISI Gesture and Skill Assessment Working Set (JIGSAWS) and evaluate four different types of holistic features from robot kinematic data-sequential motion texture (SMT), discrete Fourier transform (DFT), discrete cosine transform (DCT) and approximate entropy (ApEn). The features are then used for skill classification and exact skill score prediction. Along with using these features individually, we also evaluate the performance using our proposed weighted combination technique. The task highlights are produced using DCT features. Our results demonstrate that these holistic features outperform all previous Hidden Markov Model (HMM)-based state-of-the-art methods for skill classification on the JIGSAWS dataset. Also, our proposed feature fusion strategy significantly improves performance for skill score predictions achieving up to 0.61 average spearman correlation coefficient. Moreover, we provide an analysis on how the proposed task highlights can relate to different surgical gestures within a task. Holistic features capturing global information from robot kinematic data can successfully be used for evaluating surgeon skill in basic surgical tasks on the da Vinci robot. Using the framework presented can potentially allow for real-time score feedback in RMIS training and help surgical trainees have more focused training.

Kinematic and ergonomic assessment of laparoendoscopic single-site surgical instruments during simulator training tasks

While laparoendoscopic single-site surgery (LESS) appears to be feasible and safe, instrument triangulation, tissue handling, and other bimanual tasks are difficult even for experienced surgeons. Novel technologies emerged to overcome LESS' procedural and ergonomic difficulties of “tunnel vision” and “instrument clashing.” Surgeon kinematics, self-reported workload and upper body discomfort were used to compare straight, bent and two articulating instruments while performing two basic surgical tasks in a LESS simulator. All instruments resulted in bilateral elevation and rotation of the shoulders, excessive forearm motion and flexion and ulnar deviation of wrists. Surgeons' adopted non-neutral upper extremity postures and performed excessive joint excursions to compensate for reduced freedom of movement at the single insertion site and to operate the instrument mechanisms. LESS' cosmetic benefits continue to impact laparoscopic surgery and by enabling performance through improved instruments, ergonomic improvement for LESS can reduce negative impact on surgeon well-being and patient safety.

Pedagogic Approach in the Surgical Learning: The First Period of "Assistant Surgeon" May Improve the Learning Curve for Laparoscopic Robotic-Assisted Hysterectomy.

Hysterectomy is the most frequent surgery done with robotic assistance in the world and has been widely studied since its emergence. The surgical outcomes of the robotic hysterectomy are similar to those obtained with other minimally invasive hysterectomy techniques (laparoscopic and vaginal) and appear as a promising surgical technique in gynecology surgery. The aim of this study was to observe the learning curve of robot-assisted hysterectomy in a French surgical center, and was to evaluate the impact of the surgical mentoring. We retrospectively collected the data from the files of the robot-assisted hysterectomies with the Da Vinci® Surgical System performed between March 2010 and June 2014 at the Foch hospital in Suresnes (France). We first studied the operative time according to the number of cases, independently of the surgeon to determine two periods: the initial learning phase (Phase 1) and the control of surgical skills phase (Phase 2). The phase was defined by mastering the basic surgical tasks. Secondarily, we compared these two periods for operative time, blood losses, body mass index (BMI), days of hospitalizations, and uterine weight. We, finally, studied the difference of the learning curve between an experimented surgeon (S1) who practiced first the robot-assisted hysterectomies and a less experimented surgeon (S2) who first assisted S1 and then operated on his own patients. A total of 154 robot-assisted hysterectomies were analyzed. Twenty procedures were necessary to access to the control of surgical skills phase. There was a significant decrease of the operative time between the learning phase (156.8 min) compared to the control of surgical skills phase (125.8 min, p = 0.003). No difference between these two periods for blood losses, BMI, days of hospitalizations and uterine weight was demonstrated. The learning curve of S1 showed 20 procedures to master the robot-assisted hysterectomies with a significant decrease of the operative time, while the learning curve of S2 showed no improvement in operative time with respect to case number. Twenty robot-assisted hysterectomies are necessary to achieve control of surgical skills. The companionship to learn robotic surgery seems also promising, by improving the learning phase for this surgical technique.

Analysis of the Structure of Surgical Activity for a Suturing and Knot-Tying Task.

BackgroundSurgical tasks are performed in a sequence of steps, and technical skill evaluation includes assessing task flow efficiency. Our objective was to describe differences in task flow for expert and novice surgeons for a basic surgical task.MethodsWe used a hierarchical semantic vocabulary to decompose and annotate maneuvers and gestures for 135 instances of a surgeon’s knot performed by 18 surgeons. We compared counts of maneuvers and gestures, and analyzed task flow by skill level.ResultsExperts used fewer gestures to perform the task (26.29; 95% CI = 25.21 to 27.38 for experts vs. 31.30; 95% CI = 29.05 to 33.55 for novices) and made fewer errors in gestures than novices (1.00; 95% CI = 0.61 to 1.39 vs. 2.84; 95% CI = 2.3 to 3.37). Transitions among maneuvers, and among gestures within each maneuver for expert trials were more predictable than novice trials.ConclusionsActivity segments and state flow transitions within a basic surgical task differ by surgical skill level, and can be used to provide targeted feedback to surgical trainees.

Assessment of a Manipulator Device for NOTES With Basic Surgical Skill Tests

Advanced complex surgery performed with the natural orifice translumenal endoscopic surgery technique requires use of a multitasking platform. The aim of this study is to evaluate the basic functionality of a prototype multitasking platform "EndoSAMURAI" with the use of a biosimulation model and ex vivo porcine stomach. We compared the performance of basic surgical skill tasks between the EndoSAMURAI and standard laparoscopic instrumentation. Basic surgical tasks include cutting, dissection, and suturing and knot tying. Main outcome measurements were the time to complete each task and leak pressure to evaluate the quality of the suturing and knot tying. Although it took longer to perform all basic surgical tasks with the EndoSAMURAI than with laparoscopic instrumentation, all tasks could be performed precisely and with an accuracy comparable to that of the laparoscopic technique. Leak pressures of the gastric closure site between both techniques were also comparable.