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Development and validation of a porcine organ model for training in essential laparoscopic surgical skills.

ABSTRACT: OBJECTIVES:To develop a wet laboratory training model for learning core laparoscopic surgical skills and evaluating learners' competency level outside the operating room. METHODS:Participants completed three tasks (task 1: tissue dissection around the aorta; task 2: tissue dissection and division of the renal artery; task 3: renal parenchymal closure). Each performance was video recorded and subsequently evaluated by two experts, according to the Global Operative Assessment of Laparoscopic Skills and task-specific metrics that we developed (Assessment Sheet of Laparoscopic Skills in Wet Lab score). Mean scores were used for analyses. The subjective mental workload was also assessed (NASA Task Load Index). RESULTS:The 54 participants included 32 urologists, eight young trainees and 14 medical students. A total of 13 participants were categorized as experts (?50 laparoscopic surgeries), eight as intermediates (10-49) and 33 as novices (0-9). There were significant differences in the Global Operative Assessment of Laparoscopic Skills and Assessment Sheet of Laparoscopic Skills in Wet Lab scores among the three groups in all three tasks. Higher NASA Task Load Index scores were observed in novices, and there were significant differences in tasks 1 (Kruskal-Wallis test, P = 0.0004) and 2 (P = 0.0002), and marginal differences in task 3 (P = 0.0745) among the three groups. CONCLUSIONS:Our training model has good construct validity, and differences in the NASA Task Load Index score reflect previous laparoscopic surgical experiences. Our findings show the ability to assess both laparoscopic surgical skills and mental workloads, which could help educators comprehend trainees' level outside the operating room. Given the decreasing opportunity to carry out pure laparoscopic surgeries because of the dissemination of robotic surgery, especially in urology, our model can offer practical training opportunities.

SUBMITTER: Higuchi M

PROVIDER: S-EPMC7589398 | biostudies-literature | 2020 Oct

REPOSITORIES: biostudies-literature

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Development and validation of a porcine organ model for training in essential laparoscopic surgical skills.

Higuchi Madoka M Abe Takashige T Hotta Kiyohiko K Morita Ken K Miyata Haruka H Furumido Jun J Iwahara Naoya N Kon Masafumi M Osawa Takahiro T Matsumoto Ryuji R Kikuchi Hiroshi H Kurashima Yo Y Murai Sachiyo S Aydin Abdullatif A Raison Nicholas N Ahmed Kamran K Khan Muhammad Shamim MS Dasgupta Prokar P Shinohara Nobuo N

International journal of urology : official journal of the Japanese Urological Association 20200803 10

<h4>Objectives</h4>To develop a wet laboratory training model for learning core laparoscopic surgical skills and evaluating learners' competency level outside the operating room.<h4>Methods</h4>Participants completed three tasks (task 1: tissue dissection around the aorta; task 2: tissue dissection and division of the renal artery; task 3: renal parenchymal closure). Each performance was video recorded and subsequently evaluated by two experts, according to the Global Operative Assessment of Lap ...[more]

PMID: 32743896

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Project description:BackgroundOur aim was to build a skill assessment system, providing objective feedback to trainees based on the motion metrics of laparoscopic surgical instruments.MethodsParticipants performed tissue dissection around the aorta (tissue dissection task) and renal parenchymal closure (parenchymal-suturing task), using swine organs in a box trainer under a motion capture (Mocap) system. Two experts assessed the recorded movies, according to the formula of global operative assessment of laparoscopic skills (GOALS: score range, 5-25), and the mean scores were utilized as objective variables in the regression analyses. The correlations between mean GOALS scores and Mocap metrics were evaluated, and potential Mocap metrics with a Spearman's rank correlation coefficient value exceeding 0.4 were selected for each GOALS item estimation. Four regression algorithms, support vector regression (SVR), principal component analysis (PCA)-SVR, ridge regression, and partial least squares regression, were utilized for automatic GOALS estimation. Model validation was conducted by nested and repeated k-fold cross validation, and the mean absolute error (MAE) was calculated to evaluate the accuracy of each regression model.ResultsForty-five urologic, 9 gastroenterological, and 3 gynecologic surgeons, 4 junior residents, and 9 medical students participated in the training. In both tasks, a positive correlation was observed between the speed-related parameters (e.g., velocity, velocity range, acceleration, jerk) and mean GOALS scores, with a negative correlation between the efficiency-related parameters (e.g., task time, path length, number of opening/closing operations) and mean GOALS scores. Among the 4 algorithms, SVR showed the highest accuracy in the tissue dissection task ([Formula: see text]), and PCA-SVR in the parenchymal-suturing task ([Formula: see text]), based on 100 iterations of the validation process of automatic GOALS estimation.ConclusionWe developed a machine learning-based GOALS scoring system in wet lab training, with an error of approximately 1-2 points for the total score, and motion metrics that were explainable to trainees. Our future challenges are the further improvement of onsite GOALS feedback, exploring the educational benefit of our model and building an efficient training program.

Project description:ImportanceDeep learning-based automatic surgical instrument recognition is an indispensable technology for surgical research and development. However, pixel-level recognition with high accuracy is required to make it suitable for surgical automation.ObjectiveTo develop a deep learning model that can simultaneously recognize 8 types of surgical instruments frequently used in laparoscopic colorectal operations and evaluate its recognition performance.Design, setting, and participantsThis quality improvement study was conducted at a single institution with a multi-institutional data set. Laparoscopic colorectal surgical videos recorded between April 1, 2009, and December 31, 2021, were included in the video data set. Deep learning-based instance segmentation, an image recognition approach that recognizes each object individually and pixel by pixel instead of roughly enclosing with a bounding box, was performed for 8 types of surgical instruments.Main outcomes and measuresAverage precision, calculated from the area under the precision-recall curve, was used as an evaluation metric. The average precision represents the number of instances of true-positive, false-positive, and false-negative results, and the mean average precision value for 8 types of surgical instruments was calculated. Five-fold cross-validation was used as the validation method. The annotation data set was split into 5 segments, of which 4 were used for training and the remainder for validation. The data set was split at the per-case level instead of the per-frame level; thus, the images extracted from an intraoperative video in the training set never appeared in the validation set. Validation was performed for all 5 validation sets, and the average mean average precision was calculated.ResultsIn total, 337 laparoscopic colorectal surgical videos were used. Pixel-by-pixel annotation was manually performed for 81 760 labels on 38 628 static images, constituting the annotation data set. The mean average precisions of the instance segmentation for surgical instruments were 90.9% for 3 instruments, 90.3% for 4 instruments, 91.6% for 6 instruments, and 91.8% for 8 instruments.Conclusions and relevanceA deep learning-based instance segmentation model that simultaneously recognizes 8 types of surgical instruments with high accuracy was successfully developed. The accuracy was maintained even when the number of types of surgical instruments increased. This model can be applied to surgical innovations, such as intraoperative navigation and surgical automation.

Project description:IntroductionOnline teaching has rapidly emerged as a viable alternative to traditional face-to-face education. How to teach surgical skills in the online environment, however, has not yet been fully established nor evaluated.MethodsAn international 1-day online surgical skills course consisting of lectures, pre-recorded virtual workshops, live demonstrations and along with surgical skills teaching in breakout rooms was organised. Based on existing learning theories, new methods were developed to deliver skills teaching online. Simultaneously, traditional in-person surgical skills teaching was also conducted and used as a benchmark. Skills development was assessed by trained demonstrators and self-reported competency scores were compared between the online and face-to-face event.Results553 delegates from 20 different countries attended the online course. Of these, 64 were trained in breakout rooms with a 1:5 demonstrator-to-delegate ratio whilst the remaining 489 delegates participated in didactic skills development sessions. In a separate face-to-face course, 20 delegates were trained with traditional methods. Demonstrators rated the competency of delegates for suturing, tendon repair and vascular anastomosis. There was no significant difference in the competency ratings of delegates receiving online teaching or face-to-face teaching (p = 0.253, p = 0.084, p = 1.00, respectively). The development of the same skills to "articulation" were not different between formats (p = 0.841, p = 0.792, p = 1.00, respectively). Post course self-rated competency scores improved for all technical skills (p < 0.001). Small group sessions, both online and face-to-face, received higher satisfaction ratings compared to large group sessions in terms of clarity of instructions, answers to questions and demonstrator feedback. Overall feedback on teaching quality, however, was equivalent across both groups.DiscussionOnline teaching of surgical skills for early training years is an appropriate alternative to face-to-face teaching.

Project description:BackgroundVirtual reality (VR) surgery training has become a trend in clinical education. Many research papers validate the effectiveness of VR-based surgical simulators in training medical students. However, most existing articles employ subjective methods to study the residents' surgical skills improvement. Few of them investigate how to improve the surgery skills on specific dimensions substantially.MethodsOur paper resorts to physiological approaches to objectively study the quantitative influence and performance analysis of VR laparoscopic surgical training system for medical students. Fifty-one participants were recruited from a pool of medical students. They conducted four pre and post experiments in the training box. They were trained on VR-based laparoscopic surgery simulators (VRLS) in the middle of pre and post experiments. Their operation and physiological data (heart rate and electroencephalogram) are recorded during the pre and post experiments. The physiological data is used to compute cognitive load and flow experience quantitatively. Senior surgeons graded their performance using newly designed hybrid standards for fundamental tasks and Global operative assessment of laparoscopic skills (GOALS) standards for colon resection tasks. Finally, the participants were required to fill the questionnaires about their cognitive load and flow experience.ResultsAfter training on VRLS, the time of the experimental group to complete the same task could drop sharply (p < 0.01). The performance scores are enhanced significantly (p < 0.01). The performance and cognitive load computed from EEG are negatively correlated (p < 0.05).ConclusionThe results show that the VRLS could highly improve medical students' performance and enable the participants to obtain flow experience with a lower cognitive load. Participants' performance is negatively correlated with cognitive load through quantitative physiological analysis. This might provide a new way of assessing skill acquirement.

Dataset Information

Development and validation of a porcine organ model for training in essential laparoscopic surgical skills.

Publications

Development and validation of a porcine organ model for training in essential laparoscopic surgical skills.

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