Assessment of Surgeons' Stress Levels with Digital Sensors during Robot-Assisted Surgery: An Experimental Study.

Robot-Assisted Minimally Invasive Surgery (RAMIS) marks a paradigm shift in surgical procedures, enhancing precision and ergonomics. Concurrently it introduces complex stress dynamics and ergonomic challenges regarding the human-robot interface and interaction. This study explores the stress-related aspects of RAMIS, using the da Vinci XI Surgical System and the Sea Spikes model as a standard skill training phantom to establish a link between technological advancement and human factors in RAMIS environments. By employing different physiological and kinematic sensors for heart rate variability, hand movement tracking, and posture analysis, this research aims to develop a framework for quantifying the stress and ergonomic loads applied to surgeons. Preliminary findings reveal significant correlations between stress levels and several of the skill-related metrics measured by external sensors or the SURG-TLX questionnaire. Furthermore, early analysis of this preliminary dataset suggests the potential benefits of applying machine learning for surgeon skill classification and stress analysis. This paper presents the initial findings, identified correlations, and the lessons learned from the clinical setup, aiming to lay down the cornerstones for wider studies in the fields of clinical situation awareness and attention computing.

Robot-assisted surgery and artificial intelligence-based tumour diagnostics: social preferences with a representative cross-sectional survey.

BACKGROUND: The aim of this study was to assess social preferences for two different advanced digital health technologies and investigate the contextual dependency of the preferences. METHODS: A cross-sectional online survey was performed among the general population of Hungary aged 40 years and over. Participants were asked to imagine that they needed a total hip replacement surgery and to indicate whether they would prefer a traditional or a robot-assisted (RA) hip surgery. To better understand preferences for the chosen method, the willingness to pay (WTP) method was used. The same assessment was conducted for preferences between a radiologist's and AI-based image analysis in establishing the radiological diagnosis of a suspected tumour. Respondents' electronic health literacy was assessed with the eHEALS questionnaire. Descriptive methods were used to assess sample characteristics and differences between subgroups. Associations were investigated with correlation analysis and multiple linear regressions. RESULTS: Altogether, 1400 individuals (53.7% female) with a mean age of 58.3 (SD = 11.1) years filled in the survey. RA hip surgery was chosen by 762 (54.4%) respondents, but only 470 (33.6%) chose AI-based medical image evaluation. Those who opted for the digital technology had significantly higher educational levels and electronic health literacy (eHEALS). The majority of respondents were willing to pay to secure their preferred surgical (surgeon 67.2%, robot-assisted: 68.8%) and image assessment (radiologist: 70.9%; AI: 77.4%) methods, reporting similar average amounts in the first (p = 0.677), and a significantly higher average amount for radiologist vs. AI in the second task (p = 0.001). The regression showed a significant association between WTP and income, and in the hip surgery task, it also revealed an association with the type of intervention chosen. CONCLUSIONS: Individuals with higher education levels seem to accept the advanced digital medical technologies more. However, the greater openness for RA surgery than for AI image assessment highlights that social preferences may depend considerably on the medical situation and the type of advanced digital technology. WTP results suggest rather firm preferences in the great majority of the cases. Determinants of preferences and real-world choices of affected patients should be further investigated in future studies.

Sensor-Based Measurement Method to Support the Assessment of Robot-Assisted Radiofrequency Ablation.

Digital surgery technologies, such as interventional robotics and sensor systems, not only improve patient care but also aid in the development and optimization of traditional invasive treatments and methods. Atrial Fibrillation (AF) is the most common cardiac arrhythmia with critical clinical relevance today. Delayed intervention can lead to heart failure, stroke, or sudden cardiac death. Although many advances have been made in the field of radiofrequency (RF) catheter ablation (CA), it can be further developed by incorporating sensor technology to improve its efficacy and safety. Automation can be utilized to shorten the duration of RF ablation, provided that the interactions between the tissue and the RF tools are well understood and adequately modeled. Further research is needed to develop the optimal catheter design. This paper describes the systematic methodology developed to support robot-assisted RF CA characterization measurements. The article describes the custom instruments developed for the experiments, particularly the contact force limiter, the measurement procedure, and the evaluation of the results, as enablers for new results. The aim was to establish an objective, repeatable, robust measurement method and adjacent procedure.

Augmented or Mixed Reality Enhanced Head-Mounted Display Navigation for In Vivo Spine Surgery: A Systematic Review of Clinical Outcomes.

BACKGROUND: This research paper provides a systematic literature review (SLR) on the current status of augmented-reality head-mounted devices (AR-HMDs) that guide and navigate spine surgeries and pedicle screw placement. METHODS: Embase, Scopus, PubMed, Cochrane Library and IEEE Xplore databases were screened for the systematic literature search to collect and statistically analyze live patient clinical, procedural and user experience data. Multi-level Poisson and binominal models were used for analysis. RESULTS: In vivo patient data, only the clinically widely used Gertzbein-Robbins Scale, were published as an outcome in the recent heterogeneous literature. The statistical analysis supports the hypothesis that using AR-HMDs has the same clinical outcomes as using more expensive robot-assisted surgical (RAS) systems. CONCLUSIONS: AR-HMD-guided pedicle screw insertion is reaching its technology readiness, providing similar benefits to RAS. Further meta-analysis is expected in the future from higher case-numbered and standardized randomized clinical trials.

Evidence-based hand hygiene: Liquid or gel handrub, does it matter?

BACKGROUND: Recent studies put under scrutiny the prevailing hand hygiene guidelines, which incorporate quantitative parameters regarding handrub volume and hand size. Understanding the criticality of complete (i.e., efficient) hand hygiene in healthcare, objectivization of hand hygiene related parameters are paramount, including the formulation of the ABHR. Complete coverage can be achieved with optimal Alcohol-Based Hand Rub (ABHR) provided. The literature is limited regarding ABHR formulation variances to antimicrobial efficiency and healthcare workers' preference, while public data on clinically relevant typical application differences is not available. This study was designed and performed to compare gel and liquid format ABHRs (the two most popular types in Europe) by measuring several parameters, including application time, spillage and coverage. METHODOLOGY: Senior medical students were invited, and randomly assigned to receive pre-determined ABHR volumes (1.5 or 3 ml). All the 340 participants were given equal amounts of gel and liquid on two separate hand hygiene occasions, which occurred two weeks apart. During the hand hygiene events, by employing a digital, fully automated system paired with fluorescent-traced ABHRs, disinfectant hand coverage was objectively investigated. Furthermore, hand coverage in relation to the participants' hand sizes was also calculated. Additional data collection was performed regarding volume differences and their effect on application time, participants' volume awareness (consciousness) and disinfectant spillage during the hand hygiene events. RESULTS: The 1.5 ml ABHR volume (commonly applied in healthcare settings) is insufficient in either formulation, as the non-covered areas exceeded significant (5%+) of the total hand surface area. 3 ml, on the contrary, resulted in almost complete coverage (uncovered areas remained below 1.5%). Participants typically underestimated the volume which they needed to apply. While the liquid ABHR spreads better in the lower, 1.5 ml volume compared to the gel, the latter was easier handled at larger volume. Drying times were 30/32 s (gel and liquid formats, respectively) when 1.5 ml handrub was applied, and 40/42 s when 3 ml was used. As the evaporation rates of the ABHR used in the study are similar to those available on the market, one can presume that the results presented in the study apply for most WHO conform ABHRs. CONCLUSION: The results show that applying 1.5 ml volume was insufficient, as large part of the hand surface remained uncovered (7.0 ± 0.7% and 5.8 ± 1.0% of the hand surface in the case of gel and liquid, respectively) When 3 ml handrub was applied drying times were 40 and 42 s (gel and liquid, respectively), which is a very long time in daily clinical practice. It looks like we cannot find a volume that fits for everyone. Personalized, hand size based ABHR volumes may be the solution to find an optimal balance between maximize coverage and minimise spillage and drying time. 3 ml can be a good volume for those who have medium size hands. Large handed people should use more handrub to reach appropriate coverage, while small-handed ones may apply less to avoid massive spillage and not to take unrealistically long to dry.

Next in Surgical Data Science: Autonomous Non-Technical Skill Assessment in Minimally Invasive Surgery Training.

Background: It is well understood that surgical skills largely define patient outcomes both in Minimally Invasive Surgery (MIS) and Robot-Assisted MIS (RAMIS). Non-technical surgical skills, including stress and distraction resilience, decision-making and situation awareness also contribute significantly. Autonomous, technologically supported objective skill assessment can be efficient tools to improve patient outcomes without the need to involve expert surgeon reviewers. However, autonomous non-technical skill assessments are unstandardized and open for more research. Recently, Surgical Data Science (SDS) has become able to improve the quality of interventional healthcare with big data and data processing techniques (capture, organization, analysis and modeling of data). SDS techniques can also help to achieve autonomous non-technical surgical skill assessments. Methods: An MIS training experiment is introduced to autonomously assess non-technical skills and to analyse the workload based on sensory data (video image and force) and a self-rating questionnaire (SURG-TLX). A sensorized surgical skill training phantom and adjacent training workflow were designed to simulate a complicated Laparoscopic Cholecystectomy task; the dissection of the cholecyst's peritonial layer and the safe clip application on the cystic artery in an uncomfortable environment. A total of 20 training sessions were recorded from 7 subjects (3 non-medicals, 2 residents, 1 expert surgeon and 1 expert MIS surgeon). Workload and learning curves were studied via SURG-TLX. For autonomous non-technical skill assessment, video image data with tracked instruments based on Channel and Spatial Reliability Tracker (CSRT) and force data were utilized. An autonomous time series classification was achieved by a Fully Convolutional Neural Network (FCN), where the class labels were provided by SURG-TLX. Results: With unpaired t-tests, significant differences were found between the two groups (medical professionals and control) in certain workload components (mental demands, physical demands, and situational stress, p<0.0001, 95% confidence interval, p<0.05 for task complexity). With paired t-tests, the learning curves of the trials were also studied; the task complexity resulted in a significant difference between the first and the second trials. Autonomous non-technical skill classification was based on the FCN by applying the tool trajectories and force data as input. This resulted in a high accuracy (85%) on temporal demands classification based on the z component of the used forces and 75% accuracy for classifying mental demands/situational stress with the x component of the used forces validated with Leave One Out Cross-Validation. Conclusions: Non-technical skills and workload components can be classified autonomously based on measured training data. SDS can be effective via automated non-technical skill assessment.

Sensor-Based Automated Detection of Electrosurgical Cautery States.

In computer-assisted surgery, it is typically required to detect when the tool comes into contact with the patient. In activated electrosurgery, this is known as the energy event. By continuously tracking the electrosurgical tools' location using a navigation system, energy events can help determine locations of sensor-classified tissues. Our objective was to detect the energy event and determine the settings of electrosurgical cautery-robustly and automatically based on sensor data. This study aims to demonstrate the feasibility of using the cautery state to detect surgical incisions, without disrupting the surgical workflow. We detected current changes in the wires of the cautery device and grounding pad using non-invasive current sensors and an oscilloscope. An open-source software was implemented to apply machine learning on sensor data to detect energy events and cautery settings. Our methods classified each cautery state at an average accuracy of 95.56% across different tissue types and energy level parameters altered by surgeons during an operation. Our results demonstrate the feasibility of automatically identifying energy events during surgical incisions, which could be an important safety feature in robotic and computer-integrated surgery. This study provides a key step towards locating tissue classifications during breast cancer operations and reducing the rate of positive margins.

Performance and Capability Assessment in Surgical Subtask Automation.

Robot-Assisted Minimally Invasive Surgery (RAMIS) has reshaped the standard clinical practice during the past two decades. Many believe that the next big step in the advancement of RAMIS will be partial autonomy, which may reduce the fatigue and the cognitive load on the surgeon by performing the monotonous, time-consuming subtasks of the surgical procedure autonomously. Although serious research efforts are paid to this area worldwide, standard evaluation methods, metrics, or benchmarking techniques are still not formed. This article aims to fill the void in the research domain of surgical subtask automation by proposing standard methodologies for performance evaluation. For that purpose, a novel characterization model is presented for surgical automation. The current metrics for performance evaluation and comparison are overviewed and analyzed, and a workflow model is presented that can help researchers to identify and apply their choice of metrics. Existing systems and setups that serve or could serve as benchmarks are also introduced and the need for standard benchmarks in the field is articulated. Finally, the matter of Human-Machine Interface (HMI) quality, robustness, and the related legal and ethical issues are presented.

Robot-assisted Minimally Invasive Surgery in the age of surgical data science / Robot-asszisztált Minimál Invazív Sebészeti Rendszerek a sebészeti adattudomány korában a sebészeti adattudomány korában

With the continuous development of information technology, robotics and data science will certainly have a similar impact on invasive medicine over the next 20 years as it has had on manufacturing technology in the recent decades. Early image-guided systems and surgical robots were employed in the operating room primarily for their accuracy and reliability, as they allowed for faster and safer interventions with minimal tissue damage, targeting especially orthopedics and neurosurgery. On the other hand, a real global breakthrough came with the teleoperated da Vinci Surgical System, ideal for soft tissue procedures. The success and dominance of the da Vinci has dimmed the dozens of other surgical robots already on the market. It partially originated from the teleoperation concept of Robot-Assisted Minimally Invasive Surgery, where the full control of the robotic tools is always maintained by the human operator via the console. Nevertheless, the availability of data at large brings new possibilities, e.g., the in-view integration of preoperative data, data fusion based on surgical navigation, and error compensation have become increasingly available in prototypes. Surgical decision support and the elimination/eviction of potential errors also became increasingly important in telesurgical applications. Appropriate algorithms for handling distortions, delays, and other, even malicious, interference attempts during communication are essential. The concept of robotic telesurgery originates from NASA, and even as of today they are actively exploring the additional possibilities offered by cutting-edge technology to improve surgical systems using data science methods. In the not so distant future, even soft tissue interventions will be performed by autonomous robots. The aim of this article is to present the reader the basic concepts of this modern interdisciplinary field named Computer-Integrated Surgery, and to introduce the most important robots and robotic systems. We provide an overview of the different forms of telesurgery and describe the idea and the complexity of data-driven interventions.

Endoscopic Image-Based Skill Assessment in Robot-Assisted Minimally Invasive Surgery.

Objective skill assessment-based personal performance feedback is a vital part of surgical training. Either kinematic-acquired through surgical robotic systems, mounted sensors on tooltips or wearable sensors-or visual input data can be employed to perform objective algorithm-driven skill assessment. Kinematic data have been successfully linked with the expertise of surgeons performing Robot-Assisted Minimally Invasive Surgery (RAMIS) procedures, but for traditional, manual Minimally Invasive Surgery (MIS), they are not readily available as a method. 3D visual features-based evaluation methods tend to outperform 2D methods, but their utility is limited and not suited to MIS training, therefore our proposed solution relies on 2D features. The application of additional sensors potentially enhances the performance of either approach. This paper introduces a general 2D image-based solution that enables the creation and application of surgical skill assessment in any training environment. The 2D features were processed using the feature extraction techniques of a previously published benchmark to assess the attainable accuracy. We relied on the JHU-ISI Gesture and Skill Assessment Working Set dataset-co-developed by the Johns Hopkins University and Intuitive Surgical Inc. Using this well-established set gives us the opportunity to comparatively evaluate different feature extraction techniques. The algorithm reached up to 95.74% accuracy in individual trials. The highest mean accuracy-averaged over five cross-validation trials-for the surgical subtask of Knot-Tying was 83.54%, for Needle-Passing 84.23% and for Suturing 81.58%. The proposed method measured well against the state of the art in 2D visual-based skill assessment, with more than 80% accuracy for all three surgical subtasks available in JIGSAWS (Knot-Tying, Suturing and Needle-Passing). By introducing new visual features-such as image-based orientation and image-based collision detection-or, from the evaluation side, utilising other Support Vector Machine kernel methods, tuning the hyperparameters or using other classification methods (e.g., the boosted trees algorithm) instead, classification accuracy can be further improved. We showed the potential use of optical flow as an input for RAMIS skill assessment, highlighting the maximum accuracy achievable with these data by evaluating it with an established skill assessment benchmark, by evaluating its methods independently. The highest performing method, the Residual Neural Network, reached means of 81.89%, 84.23% and 83.54% accuracy for the skills of Suturing, Needle-Passing and Knot-Tying, respectively.

Non-Technical Skill Assessment and Mental Load Evaluation in Robot-Assisted Minimally Invasive Surgery.

BACKGROUND: Sensor technologies and data collection practices are changing and improving quality metrics across various domains. Surgical skill assessment in Robot-Assisted Minimally Invasive Surgery (RAMIS) is essential for training and quality assurance. The mental workload on the surgeon (such as time criticality, task complexity, distractions) and non-technical surgical skills (including situational awareness, decision making, stress resilience, communication, leadership) may directly influence the clinical outcome of the surgery. METHODS: A literature search in PubMed, Scopus and PsycNet databases was conducted for relevant scientific publications. The standard PRISMA method was followed to filter the search results, including non-technical skill assessment and mental/cognitive load and workload estimation in RAMIS. Publications related to traditional manual Minimally Invasive Surgery were excluded, and also the usability studies on the surgical tools were not assessed. RESULTS: 50 relevant publications were identified for non-technical skill assessment and mental load and workload estimation in the domain of RAMIS. The identified assessment techniques ranged from self-rating questionnaires and expert ratings to autonomous techniques, citing their most important benefits and disadvantages. CONCLUSIONS: Despite the systematic research, only a limited number of articles was found, indicating that non-technical skill and mental load assessment in RAMIS is not a well-studied area. Workload assessment and soft skill measurement do not constitute part of the regular clinical training and practice yet. Meanwhile, the importance of the research domain is clear based on the publicly available surgical error statistics. Questionnaires and expert-rating techniques are widely employed in traditional surgical skill assessment; nevertheless, recent technological development in sensors and Internet of Things-type devices show that skill assessment approaches in RAMIS can be much more profound employing automated solutions. Measurements and especially big data type analysis may introduce more objectivity and transparency to this critical domain as well. SIGNIFICANCE: Non-technical skill assessment and mental load evaluation in Robot-Assisted Minimally Invasive Surgery is not a well-studied area yet; while the importance of this domain from the clinical outcome's point of view is clearly indicated by the available surgical error statistics.

A large-scale investigation of alcohol-based handrub (ABHR) volume: hand coverage correlations utilizing an innovative quantitative evaluation system.

BACKGROUND: Current hand hygiene guidelines do not provide recommendations on a specific volume for the clinical hand rubbing procedure. According to recent studies volume should be adjusted in order to achieve complete coverage. However, hand size is a parameter that highly influences the hand coverage quality when using alcohol-based handrubs (ABHR). The purpose of this study was to establish a quantitative correlation between applied ABHR volume and achieved hand coverage. METHOD: ABHR based hand hygiene events were evaluated utilizing a digital health device, the Semmelweis hand hygiene system with respect to coverage achieved on the skin surface. Medical students and surgical residents (N = 356) were randomly selected and given predetermined ABHR volumes. Additionally, hand sizes were calculated using specialized software developed for this purpose. Drying time, ABHR volume awareness, as well spillage awareness were documented for each hand hygiene event. RESULTS: Hand coverage achieved during a hand hygiene event strongly depends on the applied ABHR volume. At a 1 ml dose, the uncovered hand area was approximately 7.10%, at 2 ml it decreased to 1.68%, and at 3 ml it further decreased to 1.02%. The achieved coverage is strongly correlated to hand size, nevertheless, a 3 ml applied volume proved sufficient for most hand hygiene events (84%). When applying a lower amount of ABHR (1.5 ml), even people with smaller hands failed to cover their entire hand surface. Furthermore, a 3 ml volume requires more than the guideline prescribed 20-30 s to dry. In addition, results suggest that drying time is not only affected by hand size, but perhaps other factors may be involved as well (e.g., skin temperature and degree of hydration). ABHR volumes of 3.5 ml or more were inefficient, as the disinfectant spilled while the additional rubbing time did not improve hand coverage. CONCLUSIONS: Hand sizes differ a lot among HCWs. After objectively measuring participants, the surface of the smallest hand was just over half compared to the largest hand (259 cm2 and 498 cm2, respectively). While a 3 ml ABHR volume is reasonable for medium-size hands, the need for an optimized volume of handrub for each individual is critical, as it offers several advantages. Not only it can ensure adequate hand hygiene quality, but also prevent unnecessary costs. Bluntly increasing the volume also increases spillage and therefore waste of disinfectant in the case of smaller hands. In addition, adherence could potentially decrease due to the required longer drying time, therefore, adjusting the dosage according to hand size may also increase the overall hand hygiene compliance.

Toward a standard ontology of surgical process models.

PURPOSE: The development of common ontologies has recently been identified as one of the key challenges in the emerging field of surgical data science (SDS). However, past and existing initiatives in the domain of surgery have mainly been focussing on individual groups and failed to achieve widespread international acceptance by the research community. To address this challenge, the authors of this paper launched a European initiative-OntoSPM Collaborative Action-with the goal of establishing a framework for joint development of ontologies in the field of SDS. This manuscript summarizes the goals and the current status of the international initiative. METHODS: A workshop was organized in 2016, gathering the main European research groups having experience in developing and using ontologies in this domain. It led to the conclusion that a common ontology for surgical process models (SPM) was absolutely needed, and that the existing OntoSPM ontology could provide a good starting point toward the collaborative design and promotion of common, standard ontologies on SPM. RESULTS: The workshop led to the OntoSPM Collaborative Action-launched in mid-2016-with the objective to develop, maintain and promote the use of common ontologies of SPM relevant to the whole domain of SDS. The fundamental concept, the architecture, the management and curation of the common ontology have been established, making it ready for wider public use. CONCLUSION: The OntoSPM Collaborative Action has been in operation for 24 months, with a growing dedicated membership. Its main result is a modular ontology, undergoing constant updates and extensions, based on the experts' suggestions. It remains an open collaborative action, which always welcomes new contributors and applications.

[Tools for laparoscopic skill development - available trainers and simulators]. / A laparoszkópos készségfejlesztés eszközei ­ elérheto trénerek és szimulátorok.

The laparoscopic minimally invasive surgical technique is widely employed on a global scale. However, the efficient and ethical teaching of this technique requires equipment for surgical simulation. These educational devices are present on the market in the form of box trainers and virtual reality simulators, or some combination of those. In this article, we present a systematic overview of commercially available surgical simulators describing the most important features of each product. Our overview elaborates on box trainers and virtual reality simulators, and also touches on surgical robotics simulators, together with operating room workflow simulators, for the sake of completeness. Apart from presenting educational tools, we evaluated the literature of laparoscopic surgical education and simulation, to provide a complete picture of the unfolding trends in this field. Orv Hetil. 2017; 158(40): 1570-1576.

[Objective surgery -- advanced robotic devices and simulators used for surgical skill assessment]. / Objektív sebészet -- robotok és szimulátorok használata a sebészeti képességek felmérésére.

Robotic assistance became a leading trend in minimally invasive surgery, which is based on the global success of laparoscopic surgery. Manual laparoscopy requires advanced skills and capabilities, which is acquired through tedious learning procedure, while da Vinci type surgical systems offer intuitive control and advanced ergonomics. Nevertheless, in either case, the key issue is to be able to assess objectively the surgeons' skills and capabilities. Robotic devices offer radically new way to collect data during surgical procedures, opening the space for new ways of skill parameterization. This may be revolutionary in MIS training, given the new and objective surgical curriculum and examination methods. The article reviews currently developed skill assessment techniques for robotic surgery and simulators, thoroughly inspecting their validation procedure and utility. In the coming years, these methods will become the mainstream of Western surgical education.

[Robotic surgery]. / Robotsebészet.

Due to the fast spread of laparoscopic cholecystectomy, surgical procedures have been changed essentially. The new techniques applied for both abdominal and thoracic procedures provided the possibility for minimally invasive access with all its advantages. Robots - originally developed for industrial applications - were retrofitted for laparoscopic procedures. The currently prevailing robot-assisted surgery is ergonomically more advantageous for the surgeon, as well as for the patient through the more precise preparative activity thanks to the regained 3D vision. The gradual decrease of costs of robotic surgical systems and development of new generations of minimally invasive devices may lead to substantial changes in routine surgical procedures.

[New possibilities in practical education of surgery]. / Új lehetoségek a sebészet gyakorlati oktatásában.

The fast spread of laparoscopic surgery in the surgical community also required introduction of new methods of surgical education of these techniques. Training boxes applied for this reason meant a considerable help. The technique of the virtual reality introduced simulation, which is a new possibility in education. For the first time in the history of surgery we can measure medical students' or residents' dexterity and one can get acquainted with a surgical procedure in the form of "serious games". By application of the up-to-date imaging methods we can plan the movements of the surgeon's hand even before the planned operation, practice and repeating can contribute to the safety of the real procedure. Open surgical procedures can be practiced on plastic phantoms mimicking human anatomy and the use of interactive touch devices and e-learning can also contribute to practical education of surgery.

[Laparoscopic skills training -- novel methods]. / Laparoszkópos készségek fejlesztése - új módszerek.

INTRODUCTION: Simulation for skill training has a long history in surgery. Initially, surgeons practiced on animals or human cadavers, which is costly and raises ethical questions. Emerging hygienic requirements lead to the development of "artificial" phantoms, on which suturing, anastomoses and other elements of the procedure could be practiced on. Similarly, in minimal invasive surgery surgeons need extensive practice to learn the correct techniques, and to acquire sufficient skills. Laparoscopy requires specific training devices, ranging from animal models to virtual reality simulators. AIM: This work focused on physical simulators, development of affordable phantoms and adjoin tasks for a personal laparoscopic training box. Authors described five new tasks that were added to the classical curriculum. METHODS: The tasks included leading the line, ligation, preparation-ligation, leading a string and camera handling. Data was were derived from a trial with 30 participants. They were categorized into 3 groups: laymen, general practitioner residents (non-surgeons) and surgery residents. Subjective assessment of the new tasks was performed using a 20-points questionnaire (NASA Task Load Index). Participants were asked about the usefulness, mental, physical, temporal demand, performance, effort and frustration. Accomplishment time was also recorded for each task (as well as the number or errors, where applicable). In addition, 10 consecutive task execution sessions were recorded, and in some cases, users' performance was tracked over a follow-up period of several days. RESULTS: Participants considered the tasks suitable and relevant for education, and also approved them for self-education purposes. The three groups showed statistically significant differences in performance, based on their average completion time. CONCLUSION: The follow-up studies showed continuous progress in the completion of individual tasks.

[In vitro development of laparoscopic skills]. / A laparoscopos készségek in vitro fejlesztése.

Minimally invasive surgery is a popular alternative to open surgical procedures. Laparoscopic surgeries require highly skilled surgeons with solid theoretical background and significant amount of practice. Pelvitrainers or simulators provide a good opportunity for practicing and developing laparoscopic skills. Laparoscopic training of medical students of the Semmelweis University is performed at the Institute of Experimental Surgery and Surgical Techniques on Apollo pelvitrainers. The trainer, the performed exercises and the time limits have to be validated by several measurements. Statistical evaluation of the results provides a possibility for the numerical evaluation of surgical skills as well as validating the usability of the pelvitrainer. In our study we tested the peg transfer exercise in pelvitrainers on four groups with different surgical background and level of expertise, complete novices (50 persons), medical students (326 persons), surgical residents (15), and experienced surgeons (4), respectively. A time limit of 240 s was defined for novices and 100 s for professionals. During the evaluation of the results the average time and the number of errors were calculated. The mean completion time of amateurs was 365.7 ± 130 s (mean ± standard deviation), with 2.57 errors. The performance of medical students was characterized by 159.3 ± 61.1 s average time with 1.21 errors, the completion time of residents was 257.9 ± 75.7 s with 1.13 error points, and 117.2 ± 29.1 s for the surgeons. These data show significant differences between the group, except between the results of medical students and surgeons. We plan to extend this study with the inclusion of more, experienced surgeons.