Surgical Tasks Research Articles

Development of a Classification System for Live Surgical Feedback

Live feedback in the operating room is essential in surgical training. Despite the role this feedback plays in developing surgical skills, an accepted methodology to characterize the salient features of feedback has not been defined. To quantify the intraoperative feedback provided to trainees during live surgical cases and propose a standardized deconstruction for feedback. In this qualitative study using a mixed methods analysis, surgeons at a single academic tertiary care hospital were audio and video recorded in the operating room from April to October 2022. Urological residents, fellows, and faculty attending surgeons involved in robotic teaching cases during which trainees had active control of the robotic console for at least some portion of a surgery were eligible to voluntarily participate. Feedback was time stamped and transcribed verbatim. An iterative coding process was performed using recordings and transcript data until recurring themes emerged. Feedback in audiovisual recorded surgery. The primary outcomes were the reliability and generalizability of a feedback classification system in characterizing surgical feedback. Secondary outcomes included assessing the utility of our system. In 29 surgical procedures that were recorded and analyzed, 4 attending surgeons, 6 minimally invasive surgery fellows, and 5 residents (postgraduate years, 3-5) were involved. For the reliability of the system, 3 trained raters achieved moderate to substantial interrater reliability in coding cases using 5 types of triggers, 6 types of feedback, and 9 types of responses (prevalence-adjusted and bias-adjusted κ range: a 0.56 [95% CI, 0.45-0.68] minimum for triggers to a 0.99 [95% CI, 0.97-1.00] maximum for feedback and responses). For the generalizability of the system, 6 types of surgical procedures and 3711 instances of feedback were analyzed and coded with types of triggers, feedback, and responses. Significant differences in triggers, feedback, and responses reflected surgeon experience level and surgical task being performed. For example, as a response, attending surgeons took over for safety concerns more often for fellows than residents (prevalence rate ratio [RR], 3.97 [95% CI, 3.12-4.82]; P = .002), and suturing involved more errors that triggered feedback than dissection (RR, 1.65 [95% CI, 1.03-3.33]; P = .007). For the utility of the system, different combinations of trainer feedback had associations with rates of different trainee responses. For example, technical feedback with a visual component was associated with an increased rate of trainee behavioral change or verbal acknowledgment responses (RR, 1.11 [95% CI, 1.03-1.20]; P = .02). These findings suggest that identifying different types of triggers, feedback, and responses may be a feasible and reliable method for classifying surgical feedback across several robotic procedures. Outcomes suggest that a system that can be generalized across surgical specialties and for trainees of different experience levels may help galvanize novel surgical education strategies.

Artificial intelligence enabled smart digital eye wearables.

Smart eyewear is a head-worn wearable device that is evolving as the next phase of ubiquitous wearables. Although their applications in healthcare are being explored, they have the potential to revolutionize teleophthalmology care. This review highlights their applications in ophthalmology care and discusses future scope. Smart eyewear equips advanced sensors, optical displays, and processing capabilities in a wearable form factor. Rapid technological developments and the integration of artificial intelligence are expanding their reach from consumer space to healthcare applications. This review systematically presents their applications in treating and managing eye-related conditions. This includes remote assessments, real-time monitoring, telehealth consultations, and the facilitation of personalized interventions. They also serve as low-vision assistive devices to help visually impaired, and can aid physicians with operational and surgical tasks. Wearables such as smart eyewear collects rich, continuous, objective, individual-specific data, which is difficult to obtain in a clinical setting. By leveraging sophisticated data processing and artificial intelligence based algorithms, these data can identify at-risk patients, recognize behavioral patterns, and make timely interventions. They promise cost-effective and personalized treatment for vision impairments in an effort to mitigate the global burden of eye-related conditions and aging.

Mask ventilation using volume-targeted neonatal ventilator for neonatal resuscitation: a randomised cross-over simulation study

ObjectiveTo use simulations to compare a novel mask ventilation method using a neonatal ventilator, with mask ventilation using a T-piece resuscitator, to study human factors prior to clinical testing.DesignProspective randomised...

Benchtop simulation of the retrosigmoid approach: Validation of a surgical simulator and development of a task-specific outcome measure score

BackgroundNeurosurgical training is changing globally. Reduced working hours and training opportunities, increased patient safety expectations, and the impact of COVID-19 have reduced operative exposure. Benchtop simulators enable trainees to develop surgical skills in a controlled environment. We aim to validate a high-fidelity simulator model (RetrosigmoidBox, UpSurgeOn) for the retrosigmoid approach to the cerebellopontine angle (CPA). MethodsNovice and expert Neurosurgeons and Ear, Nose, and Throat surgeons performed a surgical task using the model – identification of the trigeminal nerve. Experts completed a post-task questionnaire examining face and content validity. Construct validity was assessed through scoring of operative videos employing Objective Structured Assessment of Technical Skills (OSATS) and a novel Task-Specific Outcome Measure score. ResultsFifteen novice and five expert participants were recruited. Forty percent of experts agreed or strongly agreed that the brain tissue looked real. Experts unanimously agreed that the RetrosigmoidBox was appropriate for teaching. Statistically significant differences were noted in task performance between novices and experts, demonstrating construct validity. Median total OSATS score was 14/25 (IQR 10–19) for novices and 22/25 (IQR 20–22) for experts (p < 0.05). Median Task-Specific Outcome Measure score was 10/20 (IQR 7–17) for novices compared to 19/20 (IQR 18.5–19.5) for experts (p < 0.05). ConclusionThe RetrosigmoidBox benchtop simulator has a high degree of content and construct validity and moderate face validity. The changing landscape of neurosurgical training mean that simulators are likely to become increasingly important in the delivery of high-quality education. We demonstrate the validity of a Task-Specific Outcome Measure score for performance assessment of a simulated approach to the CPA.

Tool-tissue force segmentation and pattern recognition for evaluating neurosurgical performance

Surgical data quantification and comprehension expose subtle patterns in tasks and performance. Enabling surgical devices with artificial intelligence provides surgeons with personalized and objective performance evaluation: a virtual surgical assist. Here we present machine learning models developed for analyzing surgical finesse using tool-tissue interaction force data in surgical dissection obtained from a sensorized bipolar forceps. Data modeling was performed using 50 neurosurgery procedures that involved elective surgical treatment for various intracranial pathologies. The data collection was conducted by 13 surgeons of varying experience levels using sensorized bipolar forceps, SmartForceps System. The machine learning algorithm constituted design and implementation for three primary purposes, i.e., force profile segmentation for obtaining active periods of tool utilization using T-U-Net, surgical skill classification into Expert and Novice, and surgical task recognition into two primary categories of Coagulation versus non-Coagulation using FTFIT deep learning architectures. The final report to surgeon was a dashboard containing recognized segments of force application categorized into skill and task classes along with performance metrics charts compared to expert level surgeons. Operating room data recording of > 161 h containing approximately 3.6 K periods of tool operation was utilized. The modeling resulted in Weighted F1-score = 0.95 and AUC = 0.99 for force profile segmentation using T-U-Net, Weighted F1-score = 0.71 and AUC = 0.81 for surgical skill classification, and Weighted F1-score = 0.82 and AUC = 0.89 for surgical task recognition using a subset of hand-crafted features augmented to FTFIT neural network. This study delivers a novel machine learning module in a cloud, enabling an end-to-end platform for intraoperative surgical performance monitoring and evaluation. Accessed through a secure application for professional connectivity, a paradigm for data-driven learning is established.

Effects of Optical See-Through Head-Mounted Display Use for Simulated Laparoscopic Surgery

This study investigated the influence of using optical see-through head-mounted displays (OST-HMDs) as a replacement for conventional monitors for an extended duration, as required for laparoscopic surgery. We developed the experiment scenario including two displays (surgical field and patient vitals) in three different combinations of the HMD and conventional monitors. An experiment was conducted to compare the task performance, situation awareness, physical discomfort, and perceived workload in the three different display settings. From this study, it was shown that even though the OST-HMD resulted in higher physical discomfort and lower self-perceived performance rating, the surgical task performance, and situation awareness were not worse, and in some conditions even better than conventional monitors. The main obstacle to using OST-HMDs to replace conventional monitors in laparoscopic surgery would be the weight of the HMDs and the head discomfort caused by wearing them for a long time.

Integrated planning and control of robotic surgical instruments for task autonomy

Agile maneuvers are essential for robot-enabled complex tasks such as surgical procedures. Prior explorations on surgery autonomy are limited to feasibility study of completing a single task without systematically addressing generic manipulation safety across different tasks. We present an integrated planning and control framework for 6-DoF robotic instruments for pipeline automation of surgical tasks. We leverage the geometry of a robotic instrument and propose the nodal state space to represent the robot state in SE (3) space. Each elementary robot motion could be encoded by regulation of the state parameters via a dynamical system. This theoretically ensures that every in-process trajectory is globally feasible and stably reached to an admissible target, and the controller is of closed-form without computing 6-DoF inverse kinematics. Then, to plan the motion steps reliably, we propose an interactive (instant) goal state of the robot that transforms manipulation planning through desired path constraints into a goal-varying manipulation (GVM) problem. We detail how GVM could adaptively and smoothly plan the procedure (could proceed or rewind the process as needed) based on on-the-fly situations under dynamic or disturbed environment. Finally, we extend the above policy to characterize complete pipelines of various surgical tasks. Simulations show that our framework could smoothly solve twisted maneuvers while avoiding collisions. Physical experiments using the da Vinci Research Kit validates the capability of automating individual tasks including tissue debridement, dissection, and wound suturing. The results confirm good task-level consistency and reliability compared to state-of-the-art automation algorithms.

Toward Continuum Robot Tentacles for Lung Interventions: Exploring Folding Support Disks.

Toward the future goal of creating a lung surgery system featuring multiple tentacle-like robots, we present a new folding concept for continuum robots that enables them to squeeze through openings smaller than the robot's nominal diameter (e.g., the narrow space between adjacent ribs). This is facilitated by making the disks along the robot's backbone foldable. We also demonstrate that such a robot can feature not only straight, but also curved tendon routing paths, thereby achieving a diverse family of conformations. We find that the foldable robot performs comparably, from a kinematic perspective, to an identical non-folding continuum robot at varying deployment lengths. This work paves the way for future applications with a continuum robot that can fold and fit through smaller openings, with the potential to reduce invasiveness during surgical tasks.

Surgical Instrument Detection Algorithm Based on Improved YOLOv7x.

The counting of surgical instruments is an important task to ensure surgical safety and patient health. However, due to the uncertainty of manual operations, there is a risk of missing or miscounting instruments. Applying computer vision technology to the instrument counting process can not only improve efficiency, but also reduce medical disputes and promote the development of medical informatization. However, during the counting process, surgical instruments may be densely arranged or obstruct each other, and they may be affected by different lighting environments, all of which can affect the accuracy of instrument recognition. In addition, similar instruments may have only minor differences in appearance and shape, which increases the difficulty of identification. To address these issues, this paper improves the YOLOv7x object detection algorithm and applies it to the surgical instrument detection task. First, the RepLK Block module is introduced into the YOLOv7x backbone network, which can increase the effective receptive field and guide the network to learn more shape features. Second, the ODConv structure is introduced into the neck module of the network, which can significantly enhance the feature extraction ability of the basic convolution operation of the CNN and capture more rich contextual information. At the same time, we created the OSI26 data set, which contains 452 images and 26 surgical instruments, for model training and evaluation. The experimental results show that our improved algorithm exhibits higher accuracy and robustness in surgical instrument detection tasks, with F1, AP, AP50, and AP75 reaching 94.7%, 91.5%, 99.1%, and 98.2%, respectively, which are 4.6%, 3.1%, 3.6%, and 3.9% higher than the baseline. Compared to other mainstream object detection algorithms, our method has significant advantages. These results demonstrate that our method can more accurately identify surgical instruments, thereby improving surgical safety and patient health.

Automated colonoscopy withdrawal phase duration estimation using cecum detection and surgical tasks classification.

Colorectal cancer is the third most common type of cancer with almost two million new cases worldwide. They develop from neoplastic polyps, most commonly adenomas, which can be removed during colonoscopy to prevent colorectal cancer from occurring. Unfortunately, up to a quarter of polyps are missed during colonoscopies. Studies have shown that polyp detection during a procedure correlates with the time spent searching for polyps, called the withdrawal time. The different phases of the procedure (cleaning, therapeutic, and exploration phases) make it difficult to precisely measure the withdrawal time, which should only include the exploration phase. Separating this from the other phases requires manual time measurement during the procedure which is rarely performed. In this study, we propose a method to automatically detect the cecum, which is the start of the withdrawal phase, and to classify the different phases of the colonoscopy, which allows precise estimation of the final withdrawal time. This is achieved using a Resnet for both detection and classification trained with two public datasets and a private dataset composed of 96 full procedures. Out of 19 testing procedures, 18 have their withdrawal time correctly estimated, with a mean error of 5.52 seconds per minute per procedure.

COMPASS: a formal framework and aggregate dataset for generalized surgical procedure modeling.

We propose a formal framework for the modeling and segmentation of minimally invasive surgical tasks using a unified set of motion primitives (MPs) to enable more objective labeling and the aggregation of different datasets. We model dry-lab surgical tasks as finite state machines, representing how the execution of MPs as the basic surgical actions results in the change of surgical context, which characterizes the physical interactions among tools and objects in the surgical environment. We develop methods for labeling surgical context based on video data and for automatic translation of context to MP labels. We then use our framework to create the COntext and Motion Primitive Aggregate Surgical Set (COMPASS), including six dry-lab surgical tasks from three publicly available datasets (JIGSAWS, DESK, and ROSMA), with kinematic and video data and context and MP labels. Our context labeling method achieves near-perfect agreement between consensus labels from crowd-sourcing and expert surgeons. Segmentation of tasks to MPs results in the creation of the COMPASS dataset that nearly triples the amount of data for modeling and analysis and enables the generation of separate transcripts for the left and right tools. The proposed framework results in high quality labeling of surgical data based on context and fine-grained MPs. Modeling surgical tasks with MPs enables the aggregation of different datasets and the separate analysis of left and right hands for bimanual coordination assessment. Our formal framework and aggregate dataset can support the development of explainable and multi-granularity models for improved surgical process analysis, skill assessment, error detection, and autonomy.

Uncertainty-Aware Self-Supervised Learning for Cross-Domain Technical Skill Assessment in Robot-Assisted Surgery

Objective technical skill assessment is crucial for effective training of new surgeons in robot-assisted surgery. With advancements in surgical training programs in both physical and virtual environments, it is imperative to develop generalizable methods for automatically assessing skills. In this paper, we propose a novel approach for skill assessment by transferring domain knowledge from labeled kinematic data to unlabeled data. Our approach leverages labeled data from common surgical training tasks such as Suturing, Needle Passing, and Knot Tying to jointly train a model with both labeled and unlabeled data. Pseudo labels are generated for the unlabeled data through an iterative manner that incorporates uncertainty estimation to ensure accurate labeling. We evaluate our method on a virtual reality simulated training task (Ring Transfer) using data from the da Vinci Research Kit (dVRK). The results show that trainees with robotic assistance have significantly higher expert probability compared to these without any assistance, p<0.05, which aligns with previous studies showing the benefits of robotic assistance in improving training proficiency. Our method offers a significant advantage over other existing works as it does not require manual labeling or prior knowledge of the surgical training task for robot-assisted surgery.

Physiological Metrics of Surgical Difficulty and Multi-Task Requirement during Robotic Surgery Skills.

Previous studies in robotic-assisted surgery (RAS) have studied cognitive workload by modulating surgical task difficulty, and many of these studies have relied on self-reported workload measurements. However, contributors to and their effects on cognitive workload are complex and may not be sufficiently summarized by changes in task difficulty alone. This study aims to understand how multi-task requirement contributes to the prediction of cognitive load in RAS under different task difficulties. Multimodal physiological signals (EEG, eye-tracking, HRV) were collected as university students performed simulated RAS tasks consisting of two types of surgical task difficulty under three different multi-task requirement levels. EEG spectral analysis was sensitive enough to distinguish the degree of cognitive workload under both surgical conditions (surgical task difficulty/multi-task requirement). In addition, eye-tracking measurements showed differences under both conditions, but significant differences of HRV were observed in only multi-task requirement conditions. Multimodal-based neural network models have achieved up to 79% accuracy for both surgical conditions.

VALIDATION OF A NOVEL DEVICE FOR DIGITALLY ENHANCED HANDS-ON SURGICAL TRAINING OF INTRAMEDULLARY NAIL DISTAL INTERLOCKING

Freehand distal interlocking of intramedullary nails is technical demanding and prone to handling issues. It requires the surgeon to precisely place a screw through the nail under x-ray. If not performed accurately it can be a time consuming and radiation expensive procedure. The aims of this study were to assess construct and face validity of a new training device for distal interlocking of intramedullary nails.53 participants (29 novices and 24 experts) were included. Construct validity was evaluated by comparing simulator metrics (number of x-rays, nail hole roundness, drill tip position and accuracy of the drilled hole) between experts and novices. Face validity was evaluated by means of a questionnaire concerning training potential and quality of simulated reality using a 7-point Likert scale (range 1-7).Mean realism of the training device was rated 6.3 (range 4-7) and mean training potential as well as need for distal interlocking training was rated 6.5 (range 5-7) with no significant differences between experts and novices, p≥0.236. All participants stated that the simulator is useful for procedural training of distal nail interlocking, 96% would like to have it at their institution and 98% would recommend it to their colleagues. Total number of x-rays were significantly higher for novices (20.9±6.4 vs. 15.5±5.3), p=0.003. Successful task completion (hit the virtual nail hole with the drill) was significantly higher in experts (p=0.04; novices hit: n=12; 44,4%; experts hit: n=19; 83%).The evaluated training device for distal interlocking of intramedullary nails yielded high scores in terms of training capability and realism. Furthermore, construct validity was established as it reliably discriminates between experts and novices. Participants see a high further training potential as the system may be easily adapted to other surgical task requiring screw or pin position with the help of x-rays.

Suture Failure of Duodenum after Surgery for Complications of Duodenal Ulcer

INTRODUCTION: The incidence of failure of intestinal sutures in the early postoperative period reaches 26%. The failure of the duodenal sutures leads to formation of high duodenal fistulas, the treatment of which is one of the most complicated surgical tasks. The article describes a clinical case of failure of duodenal suture after surgery for a duodenal ulcer complicated with bleeding. The failure of the duodenal suture could be provoked by hypoproteinemia and anemia, despite the attempts of their compensation. The presented case demonstrates complexity of the clinical course of this pathology and the possibility of successful application of draining surgery with creation of a controllable duodenal fistula, the subsequent healing of which led to recovery of the patient. To create the controllable duodenal fistula, we used a method of external-internal triple drainage of the duodenal zone (retrograde duodenostomy, gastroenteroanastomosis with a short loop, nasogastral probe). At the initial stage, after surgery, the patient received full parenteral feeding, after restoration of the intestinal peristalsis, feeding was continued through a nasointestinal probe. Infusion therapy, transfusion of blood components were conducted, drugs suppressing gastric and pancreatic secretion, were used. On the 35th postoperative day, the discharge through the duodenal fistula and the nasogastral probe significantly decreased, and completely stopped on the 54th day (immediately after X-ray of stomach with barium sulfate as contrast substance). The patient was discharged with improvement for outpatient follow-up at the place of residence on the 60th day after surgery. In the follow-up period no complaints, good appetite, rapid gaining of weight, complete rehabilitation. In one-year follow-up, no long-term complications and consequences for health were observed.&#x0D; CONCLUSION: A choice of the surgical treatment method of duodenal suture failure remains a subject of discussion. In the presented clinical case, draining surgery with creation of a controllable duodenal fistula proved to be effective. The complete closure of the residual fistula of the duodenum was facilitated by stomach X-ray with use of barium sulfate.

Automating Surgical Peg Transfer: Calibration With Deep Learning Can Exceed Speed, Accuracy, and Consistency of Humans

Peg transfer is a well-known surgical training task in the Fundamentals of Laparoscopic Surgery (FLS). While human surgeons teleoperate robots such as the da Vinci to perform this task with high speed and accuracy, it is challenging to automate. This paper presents a novel system and control method using a da Vinci Research Kit (dVRK) surgical robot and a Zivid depth sensor, and a human subjects study comparing performance on three variants of the peg-transfer task: unilateral, bilateral without handovers, and bilateral with handovers. The system combines 3D printing, depth sensing, and deep learning for calibration with a new analytic inverse kinematics model and time-minimized motion controller. In a controlled study of 3384 peg transfer trials performed by the system, an expert surgical resident, and 9 volunteers, results suggest that the system achieves accuracy on par with the experienced surgical resident and is significantly faster and more consistent than the surgical resident and volunteers. The system also exhibits the highest consistency and lowest collision rate. To our knowledge, this is the first autonomous system to achieve “superhuman” performance on a standardized surgical task. All data is available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://sites.google.com/view/surgicalpegtransfer</uri> Note to Practitioners—This paper presents a new approach to calibrating cable-driven robots based on a combination of 3D printing, depth sensing, inverse kinematics, convex optimization, and deep learning. The approach is applied to calibrating the da Vinci, commercial surgical-assist robot, to automate a standard “pick and place” task. Experiments suggest that the resulting system matches human surgical expert performance in speed and accuracy and significantly outperforms humans in terms of consistency. All details on the system including CAD models, code, and user study data are available online.

PD30-05 AI-BASED VIDEO FEEDBACK TO IMPROVE NOVICE PERFORMANCE ON A ROBOTIC SUTURING TASK

You have accessJournal of UrologyCME1 Apr 2023PD30-05 AI-BASED VIDEO FEEDBACK TO IMPROVE NOVICE PERFORMANCE ON A ROBOTIC SUTURING TASK Jasper A. Laca, Dani Kiyasseh, Rafal Kocielnik, Taseen F. Haque, Runzhuo Ma, Anima Anandkumar, and Andrew J. Hung Jasper A. LacaJasper A. Laca More articles by this author , Dani KiyassehDani Kiyasseh More articles by this author , Rafal KocielnikRafal Kocielnik More articles by this author , Taseen F. HaqueTaseen F. Haque More articles by this author , Runzhuo MaRunzhuo Ma More articles by this author , Anima AnandkumarAnima Anandkumar More articles by this author , and Andrew J. HungAndrew J. Hung More articles by this author View All Author Informationhttps://doi.org/10.1097/JU.0000000000003316.05AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookLinked InTwitterEmail Abstract INTRODUCTION AND OBJECTIVE: Automated skills assessment can provide surgical trainees with objective, personalized feedback during training. Here, we measure the efficacy of artificial-intelligence (AI)-based feedback on a robotic suturing task. METHODS: 42 participants (no surgical experience) were randomized to control group (CG) or feedback group (FG) and video-recorded while completing 2 rounds (R1 and R2) of suturing tasks on a daVinci surgical robot. Participants were assessed on needle handling (NH) and needle driving (ND), and feedback was provided via a visual interface after R1 (Fig). For CG: participants were presented with randomly-selected video clips from R1. For FG: participants were informed of their AI-based skill assessment and presented with specific video clips from R1. Following the feedback, participants completed R2 where they repeated the suturing task. The ground-truth annotation of skill assessments of all suturing tasks was performed retrospectively by 3 blinded raters using a previously-validated tool (End-To-End Assessment of Suturing Expertise (EASE)). These ground-truth annotations were compared to AI-based assessments. Participants from each group were further labeled as under-performers (UP) or innate performers (IP), based on a median split of EASE scores from R1. Generalized estimating equation was used to compare performance between groups. RESULTS: Human raters achieved intraclass correlation coefficients of 0.84 for manual assessments (p<0.001). AI scoring achieved an AUC of 0.67 and 0.80 for NH and ND, respectively, using human scores as ground truth. By AI scores: R1 performance was statistically similar between CG (n=20) and FG (n=22) (p>0.05). Observing the median difference across rounds (R1 vs. R2), FG had a significantly larger improvement in NH skill (0.21 vs 0, p=0.032) and a larger improvement in ND skill (0.13 vs.-0.06, p=0.3) when compared to CG. All IP exhibited similar improvements across rounds, regardless of feedback (p>0.05). In contrast, UP in the FG improved more than CG counterparts in NH (p=0.015), suggesting that feedback was particularly valuable for UP. CONCLUSIONS: AI-based feedback facilitates the acquisition of robotic technical skills by surgical trainees, especially UPs. Future research will extend AI-based feedback to additional suturing skills, surgical tasks, and experience groups. Source of Funding: Research reported in this publication was supported by the National Cancer Institute of the National Institutes of Health under Award Number R01CA251579. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health © 2023 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 209Issue Supplement 4April 2023Page: e832 Advertisement Copyright & Permissions© 2023 by American Urological Association Education and Research, Inc.MetricsAuthor Information Jasper A. Laca More articles by this author Dani Kiyasseh More articles by this author Rafal Kocielnik More articles by this author Taseen F. Haque More articles by this author Runzhuo Ma More articles by this author Anima Anandkumar More articles by this author Andrew J. Hung More articles by this author Expand All Advertisement PDF downloadLoading ...

PD30-07 DECONSTRUCTING AND QUANTIFYING LIVE SURGICAL FEEDBACK IN THE OPERATING ROOM

You have accessJournal of UrologyCME1 Apr 2023PD30-07 DECONSTRUCTING AND QUANTIFYING LIVE SURGICAL FEEDBACK IN THE OPERATING ROOM Elyssa Y. Wong, Cherine H. Yang, Istabraq S. Dalieh, Daniela C. Sotelo, Jasper A. Laca, Runzhuo Ma, Timothy N. Chu, Rafal Kocielnik, Mitchell G. Goldenberg, Jamal A. Nabhani, Steven Cen, and Andrew J. Hung Elyssa Y. WongElyssa Y. Wong More articles by this author , Cherine H. YangCherine H. Yang More articles by this author , Istabraq S. DaliehIstabraq S. Dalieh More articles by this author , Daniela C. SoteloDaniela C. Sotelo More articles by this author , Jasper A. LacaJasper A. Laca More articles by this author , Runzhuo MaRunzhuo Ma More articles by this author , Timothy N. ChuTimothy N. Chu More articles by this author , Rafal KocielnikRafal Kocielnik More articles by this author , Mitchell G. GoldenbergMitchell G. Goldenberg More articles by this author , Jamal A. NabhaniJamal A. Nabhani More articles by this author , Steven CenSteven Cen More articles by this author , and Andrew J. HungAndrew J. Hung More articles by this author View All Author Informationhttps://doi.org/10.1097/JU.0000000000003316.07AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookLinked InTwitterEmail Abstract INTRODUCTION AND OBJECTIVE: Feedback is essential in surgical training. While all trainees meet case minimums, the feedback they receive can vary in content and quality. We capture the intraoperative feedback provided to trainees during live urologic robotic cases and propose a standardized deconstruction for feedback. METHODS: Eight attending surgeons and nine trainees were prospectively recorded during robotic surgeries at our institution from April-August 2022. Endoscopic video was captured directly from the surgeon console. Dialogue between experts and trainees was recorded with clip-on microphones. A schematic flow for feedback and a classification system for Triggers, Feedback Elements, and Outcomes was developed (Figure 1a) and inter-rater reliability between three trained raters was calculated using prevalence-adjusted and bias-adjusted kappa (PABAK). To examine differences in these classification categories by trainee experience level, we first used generalized estimating equations to estimate weighted average rates across residents and fellows. Next, we used rate ratio (RR) to compare differences in these categories between surgical tasks (dissection vs suturing). RESULTS: Raters had moderate agreement in identifying Trigger, Feedback, and Outcome categories (PABAK 0.6-1.0). In total, 25 robotic teaching cases yielded 3,067 unique lines of feedback. A clear trigger was identified most of the time (59.6%, 95% Confidence Interval [CI] 55.4%, 64.2%). The most frequent trigger was trainee error (28.3%, 95% CI [24.4%, 32.8%]). Most feedback was purely technical (52.3%, 95% CI [46.3%, 59.1%]). The most often combined feedback elements were anatomic and technical components (20.3%, CI [17.3%, 23.8%]). Trainees received more anatomic feedback when performing dissection (RR 2.1, 95% CI [1.7, 2.5]) and more technical feedback when suturing (RR 1.1, 95% CI [1.0, 1.3]) (Figure 1b). Notably, experts repeated feedback less frequently for fellows than residents (RR 0.6, 95% CI [0.1, 1.1]). CONCLUSIONS: We developed a novel framework for feedback that not only captures the dynamic interaction between trainee and expert but accounts for resulting changes in behavior. Investigation into the relationships between Triggers, Feedback, and Outcomes may reveal avenues for optimizing feedback and skill acquisition in different teaching scenarios. Source of Funding: None © 2023 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 209Issue Supplement 4April 2023Page: e833 Advertisement Copyright & Permissions© 2023 by American Urological Association Education and Research, Inc.MetricsAuthor Information Elyssa Y. Wong More articles by this author Cherine H. Yang More articles by this author Istabraq S. Dalieh More articles by this author Daniela C. Sotelo More articles by this author Jasper A. Laca More articles by this author Runzhuo Ma More articles by this author Timothy N. Chu More articles by this author Rafal Kocielnik More articles by this author Mitchell G. Goldenberg More articles by this author Jamal A. Nabhani More articles by this author Steven Cen More articles by this author Andrew J. Hung More articles by this author Expand All Advertisement PDF downloadLoading ...

MP68-11 THE INFLOW-OPERA STUDY. INVESTIGATIONS IN FLOW STATE MEASUREMENTS WITH EEG DURING SURGICAL PERFORMANCE

You have accessJournal of UrologyCME1 Apr 2023MP68-11 THE INFLOW-OPERA STUDY. INVESTIGATIONS IN FLOW STATE MEASUREMENTS WITH EEG DURING SURGICAL PERFORMANCE Uwe Bieri, Melanie Baumgartner, Cao Tri, Anne-Raphaëlle Richoz, Daniel Eberli, and Cédric Poyet Uwe BieriUwe Bieri More articles by this author , Melanie BaumgartnerMelanie Baumgartner More articles by this author , Cao TriCao Tri More articles by this author , Anne-Raphaëlle RichozAnne-Raphaëlle Richoz More articles by this author , Daniel EberliDaniel Eberli More articles by this author , and Cédric PoyetCédric Poyet More articles by this author View All Author Informationhttps://doi.org/10.1097/JU.0000000000003331.11AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookLinked InTwitterEmail Abstract INTRODUCTION AND OBJECTIVE: Flow is the optimal mental state where individuals experience the highest focus and are fully absorbed in a task. Furthermore, research has shown that Flow predicts performance outcomes in several disciplines. Brain-Computer interfaces and Heart Rate sensors have shown approaches to distinguish Flow with electroencephalography (EEG) and Heart Rate (HR). Individuals who performed significantly better in specific tasks and were in Flow showed increased theta activity (4-7 HZ) in their EEG spectrum and lower HR. Performance outcome is crucial in surgery, and achieving Flow could enhance physicians' performance significantly. Our study aims to investigate these markers during virtual reality (VR) surgery training to provide objective measurements of peak performance. METHODS: We conducted the study with 20 urologic surgeons. We used a wireless EEG headband to record theta activity. HR was measured with a wireless HR sensor. The VR training was performed on the intuitive DaVinci Skills Simulator for robotic-assisted surgery. After completing the surgical tasks, the Flow Short Scale (FSS) was applied to measure the subjective Flow experience. Two VR tasks were performed on the simulator followed by the FSS. This procedure was repeated three times. Our primary objective was to investigate differences between high and low performers, their EEG output, HR and FSS scores. To test the different models, we conducted Generalized Estimating Equations Models (Wald test). Additionally, Spearman's Rank Correlation was computed to detect associations between the variables. RESULTS: We found that high performers had a significantly higher theta activity than low performers with W(2)=7.29, p=.026. In Addition, we observed a significantly lower HR among high performers than among low performers, with W(1)=9.86, p=.007(MD=10.21, SD=3.33). Furthermore, we detected a positive correlation between VR surgery scores and subjective flow experience within high performers with r(10) = 0.63, p=.048. CONCLUSIONS: Our findings indicate that surgeons who achieved higher performances showed more theta activity in their EEG spectrum and lower HR than surgeons with lower performance during VR surgeries. These findings highlight the importance of the ability to calm one's physiology as a predictor of performance outcomes. These results may lead to specific bio- and neurofeedback training protocols for surgeons to enhance their performance, accelerate the learning curve and reduce their arousal level during challenging intraoperative situations. Source of Funding: UZH Sc. Foundation © 2023 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 209Issue Supplement 4April 2023Page: e957 Advertisement Copyright & Permissions© 2023 by American Urological Association Education and Research, Inc.MetricsAuthor Information Uwe Bieri More articles by this author Melanie Baumgartner More articles by this author Cao Tri More articles by this author Anne-Raphaëlle Richoz More articles by this author Daniel Eberli More articles by this author Cédric Poyet More articles by this author Expand All Advertisement PDF downloadLoading ...

MP68-06 MOTION CAPTURE SYSTEM FOR OBJECTIVE ASSESSMENT OF TECHNICAL SKILLS DURING URETEROSCOPY

You have accessJournal of UrologyCME1 Apr 2023MP68-06 MOTION CAPTURE SYSTEM FOR OBJECTIVE ASSESSMENT OF TECHNICAL SKILLS DURING URETEROSCOPY Brittany Berk, Mahdi Ebnali-Heidari, Christian Miccile, Roger Dias, and Daniel Wollin Brittany BerkBrittany Berk More articles by this author , Mahdi Ebnali-HeidariMahdi Ebnali-Heidari More articles by this author , Christian MiccileChristian Miccile More articles by this author , Roger DiasRoger Dias More articles by this author , and Daniel WollinDaniel Wollin More articles by this author View All Author Informationhttps://doi.org/10.1097/JU.0000000000003331.06AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookLinked InTwitterEmail Abstract INTRODUCTION AND OBJECTIVE: Prior studies have demonstrated success with the use of motion analysis technology to assess surgical psychomotor skills. This study aims to provide preliminary evidence on the feasibility of using motion capture (mocap) system for objective assessment of a surgeon’s technical skills during ureteroscopic procedures. METHODS: Motion data of two participants, an attending urologist and a novice with no prior surgical training, were collected using the Rokoko smart suit with a sampling rate of 100 frames per second. This mocap system consists of 19 sensors; 9 Degree of Freedom (DoF) inertial measurement units each connected via wires to a hub for wireless communication (Figure 1). After data collection and preprocessing, data were analyzed to extract kinematic information from multiple joints including the wrist, elbow, and shoulder during a simulated ureteroscopic procedure. RESULTS: These data show that the distribution of x, y, and z locations of specific body part sensors can be visualized to investigate the variables associated with movement including speed, acceleration, the economy of movement, and smoothness. We found a distinct pattern in joint kinematics such as wrist, elbow flexion, extension, and pronation of the expert when compared to our novice participant. Interestingly, for most metrics, the expert showed a larger range of motion compared to the novice (Figure 1). To further investigate how these fluctuations are reflected in the frequency domains, we used Power Spectral Density (PSD) analysis, displaying the distribution of power of motion signals across distinct frequency bands. We found a higher power of motion in the signal associated with the expert left wrist flexion motion, indicating more energy spent on this motion pattern compared to the novice participant. CONCLUSIONS: In this study, we demonstrate that a mocap system is a feasible method for understanding surgeon motion patterns during ureteroscopic procedures. With additional participants and more complex simulated surgical tasks, we expect to utilize this framework to better delineate the ways that urologic surgeons utilize endoscopic equipment and to define metrics for the assessment of efficient kinematics related to surgical quality. This technology may also prove useful in urological surgical training in the future. Source of Funding: None © 2023 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 209Issue Supplement 4April 2023Page: e954 Advertisement Copyright & Permissions© 2023 by American Urological Association Education and Research, Inc.MetricsAuthor Information Brittany Berk More articles by this author Mahdi Ebnali-Heidari More articles by this author Christian Miccile More articles by this author Roger Dias More articles by this author Daniel Wollin More articles by this author Expand All Advertisement PDF downloadLoading ...