Using objective robotic automated performance metrics and task-evoked pupillary response to distinguish surgeon expertise.

Crowdsourcing from the general population is an efficient, inexpensive method of surgical performance evaluation. In this study, we compared the discriminatory ability of experts and crowdsourced evaluators (the Crowd) to detect differences in robotic automated performance metrics (APMs). APMs (instrument motion tracking and events data directly from the robot system) of anterior vesico-urethral anastomoses (VUAs) of robotic radical prostatectomies were captured by the dVLogger (Intuitive Surgical). Crowdsourced evaluators and four expert surgeons evaluated video footage using the Global Evaluative Assessment of Robotic Skills (GEARS) (individual domains and total score). Cases were then stratified into performance groups (high versus low quality) for each evaluator based on GEARS. APMs from each group were compared using the Mann-Whitney U test. 25 VUAs performed by 11 surgeons were evaluated. The Crowd displayed moderate correlation with averaged expert scores for all GEARS domains (r > 0.58, p < 0.01). Bland-Altman analysis showed a narrower total GEARS score distribution by the Crowd compared to experts. APMs compared amongst performance groups for each evaluator showed that through GEARS scoring, the most common differentiated metric by evaluators was the velocity of the dominant instrument arm. The Crowd outperformed two out of four expert evaluators by discriminating differences in three APMs using total GEARS scores. The Crowd assigns a narrower range of GEARS scores compared to experts but maintains overall agreement with experts. The discriminatory ability of the Crowd at discerning differences in robotic movements (via APMs) through GEARS scoring is quite refined, rivaling that of expert evaluators.

Virtual reality (VR) and augmented reality (AR) systems are increasingly available to neurosurgeons. These systems may provide opportunities for technical rehearsal and assessments of surgeon performance. The assessment of neurosurgeon skill in VR and AR environments and the validity of VR and AR feedback has not been systematically reviewed. A systematic review following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines was conducted through MEDLINE and PubMed. Studies published in English between January 1990 and February 2021 describing the use of VR or AR to quantify surgical technical performance of neurosurgeons without the use of human raters were included. The types and categories of automated performance metrics (APMs) from each of these studies were recorded. Thirty-three VR studies were included in the review; no AR studies met inclusion criteria. VR APMs were categorized as either distance to target, force, kinematics, time, blood loss, or volume of resection. Distance and time were the most well-studied APM domains, although all domains were effective at differentiating surgeon experience levels. Distance was successfully used to track improvements with practice. Examining volume of resection demonstrated that attending surgeons removed less simulated tumor but preserved more normal tissue than trainees. More recently, APMs have been used in machine learning algorithms to predict level of training with a high degree of accuracy. Key limitations to enhanced-reality systems include limited AR usage for automated surgical assessment and lack of external and longitudinal validation of VR systems. VR has been used to assess surgeon performance across a wide spectrum of domains. The VR environment can be used to quantify surgeon performance, assess surgeon proficiency, and track training progression. AR systems have not yet been used to provide metrics for surgeon performance assessment despite potential for intraoperative integration. VR-based APMs may be especially useful for metrics that are difficult to assess intraoperatively, including blood loss and extent of resection.

Introduction: Flexible ureteroscopy (fURS) is the most common procedure for treatment of urolithiasis. We previously utilized kinematic evaluations of simulated fURS to demonstrate that certain body movements are associated with efficient ureteroscopic manipulation for complex tasks. In this study, we incorporated computer vision to create an efficiency score using the ureteroscope travel distance (DIST), task time (TIME), spectral arc length (SPARC), and percentage of purposeful wall collisions (COLL). The goal is a simulation-based system that can abstract these automated performance metrics (APMs) to differentiate between novice and expert ureteroscope handling. Methods: A ureteroscopic simulation box was used. Body kinematics, task time, and ureteroscopic movements were analyzed using a motion capture system and video camera. Optical flow computer vision was used to track the ureteroscope. DIST, TIME, and SPARC were automatically calculated. Wall collisions were automatically captured and independently judged by two authors; an algorithm was developed to automatically determine the COLL variable. A mixed-effects model was used to aggregate these variables and distinguish between surgeons' first and final task attempts. Normalized values of these metrics were added to create a composite ureteroscopic efficiency score (CUES). Results: Twelve urologists completed the simulated tasks. The COLL assessment algorithm determined beneficial wall collisions with an accuracy of 77%. Normalized values of TIME, DIST, SPARC, and COLL were combined to create a composite ureteroscopic efficiency score (CUES). Compared with the first attempt, both the second and third attempts showed statistically significant improvements in CUES. The ROC-AUC score reached 0.86, suggesting excellent discrimination between attempts. There was also a statistically significant difference in CUES when comparing resident and attending performance. Conclusions: APMs can be abstracted using computer vision and artificial intelligence; an aggregate composite score (CUES) may be a promising method for evaluation of ureteroscopic efficiency.

To the best of our knowledge, this is the first reported cadaveric feasibility study of leader-follower type robotic-assisted middle cerebral artery (MCA)-radial artery-internal carotid artery anastomosis in the neurovascular surgery field using the da Vinci Xi system (da Vinci Surgical System; Intuitive Surgical, Inc.). Vascular suturing is a necessary skill in neurosurgery; however, the learning curve for deep and high-flow bypasses is severely low. Thus, robot-assisted surgery has been introduced. Here, we describe the surgical workflow adaptations of vascular anastomosis using the da Vinci system to assess the feasibility of robot-assisted anastomoses of the radial and middle cerebral arteries. Two fresh cadaver heads were studied using the da Vinci Xi Surgical System with 0° and 30° stereoscopic endoscopes to visualize the neuroanatomy. The da Vinci Xi Surgical System was used throughout the anastomosis of the MCA and intracarotid artery. The optic nerve, optic chiasm, carotid artery, and oculomotor nerve were visualized using standard microdissection techniques. The Sylvian fissure was exposed from the proximal Sylvian membrane to the distal MCA. Using black diamond microforceps and Potts scissors, suturing was achieved on the radial artery-middle cerebral artery using 8-0 Prolene and on the radial artery-internal carotid artery using 7-0 Prolene. A bypass of the MCA-radial artery-internal carotid artery can be achieved using the da Vinci Xi Surgical System in cadaver models. This system provides experts and less experienced neurosurgeons with stable bypass techniques for both superficial and deep-seated arteries. However, further studies are needed to evaluate the safety and benefits of the da Vinci Xi Surgical System for bypass procedures.

To investigate the applications of artificial intelligence (AI) in diagnosis, treatment and outcome predictionin urologic diseases and evaluate its advantages over traditional models and methods. A literature search was performed after PROSPERO registration (CRD42018103701) and in compliance with Preferred Reported Items for Systematic Reviews and Meta-Analyses (PRISMA) methods. Articles between 1994 and 2018 using the search terms "urology", "artificial intelligence", "machine learning" were included and categorized by the application of AI in urology. Review articles, editorial comments, articles with no full-text access, and nonurologic studies were excluded. Initial search yielded 231 articles, but after excluding duplicates and following full-text review and examination of article references, only 111 articles were included in the final analysis. AI applications in urology include: utilizing radiomic imaging or ultrasonic echo data to improve or automate cancer detection or outcome prediction, utilizing digitized tissue specimen images to automate detection of cancer on pathology slides, and combining patient clinical data, biomarkers, or gene expression to assist disease diagnosis or outcome prediction. Some studies employed AI to plan brachytherapy and radiation treatments while others used video based or robotic automated performance metrics to objectively evaluate surgical skill. Compared to conventional statistical analysis, 71.8% of studies concluded that AI is superior in diagnosis and outcome prediction. AI has been widely adopted in urology. Compared to conventional statistics AI approaches are more accurate in prediction and more explorative for analyzing large data cohorts. With an increasing library of patient data accessible to clinicians, AI may help facilitate evidence-based and individualized patient care.

The R.E.N.A.L. Nephrometry Score: A Comprehensive Standardized System for Quantitating Renal Tumor Size, Location and Depth

Minimally invasive surgical techniques, including laparoscopic and robotic surgery, have profoundly impacted surgical practice by improving precision, reducing recovery times, and minimizing complications. However, these modalities differ in their cognitive demands and skill acquisition requirements, which can influence the learning curve and operative performance. To assess and evaluate this variability across these modalities, a functional near-infrared spectroscopy (fNIRS) system is used as an objective method for monitoring cognitive activity in surgical trainees. fNIRS can provide insights and further our understanding of the mental demands of different surgical techniques and their association with varying task complexity. This study seeks to assess the influence of surgical modality (laparoscopy vs. robotic surgery) and task complexity (pick and place (PP) vs. knot tying (KT)) on cognitive workload through fNIRS. We compare real-world and simulation-based training environments to determine changes in brain activation patterns and task performance. A total of twenty-six surgical trainees (general and gynecologic surgery residents and specialists) participated in this study. Participants completed standardized laparoscopic and robotic surgical tasks at varying levels of complexity while their cognitive workload was measured using fNIRS. This study included both simulation-based training and real-world surgical environments. Hemodynamic responses in the prefrontal cortex (PFC), task completion times, and performance metrics were analyzed. Laparoscopic surgery elicited higher activity changes in the prefrontal cortex, indicating increased cognitive demand compared with robotic surgery, particularly for complex tasks like knot tying. Task complexity significantly influenced mental load, with more intricate procedures eliciting greater neural activation. Real-world training resulted in higher cognitive engagement than simulation, emphasizing the gap between simulated and actual surgical performance. Cognitive workload was lower and significantly different during robotic surgery than during laparoscopy, potentially due to its ergonomic advantages and enhanced motor control. Simulation-based training effectively prepares surgeons, but the cognitive workload results indicate that it may not fully replicate real-world surgical environments. These findings reveal the importance of cognitive workload assessment in surgical education and suggest that incorporating neuroimaging techniques such as fNIRS into training programs could enhance skill acquisition and performance.

The da Vinci Xi surgical system (Intuitive Surgical, Sunnyvale, CA, USA) cannot give tactile feedback to surgeons. This shortcoming may increase the risk of left internal thoracic artery (LITA) injury during its harvest. We utilized Firefly Fluorescence Imaging (Firefly) to assess LITA quality in robot-assisted minimally invasive direct coronary artery bypass (R-MIDCAB). We retrospectively reviewed clinical records and intraoperative videos of 30 consecutive patients who underwent R-MIDCAB with LITA-left anterior descending (LAD) coronary bypass. All patients had post-harvest assessment of LITA blood flow by Firefly with 1 mL (2.5 mg/mL) of indocyanine green injection through a central line. Twenty-seven of the patients were male, mean age was 67.7 ± 10.7 years. In post-harvest assessment performed before transection of the distal LITA, blood flow in LITA was well visualized in 28 patients. In the remaining 2 patients, 1 had dissection and the other had severe spasm of the LITA. Firefly was also useful for locating LITA and LAD and for assessing blood flow of the graft after anastomosis. Time required for each Firefly assessment was approximately 20 seconds. There were no side effects or complications due to Firefly intraoperatively and postoperatively. Twenty-six patients had postoperative coronary computed tomography; LITA patency rate was 100% (26/26). Firefly is fast, simple, and effective for locating and assessing flow in LITA and LAD before and after anastomosis in R-MIDCAB.

Nerve-sparing Robot-assisted Retroperitoneal Lymph Node Dissection: The Monoblock Technique

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BackgroundSurgical residents underutilize opportunities for traditional laparoscopic simulation training. Serious gaming may increase residents’ motivation to practice laparoscopic skills. However, little is known about the effectiveness of serious gaming for laparoscopic skills training.ObjectiveThe aim of this study was to establish construct validity for the laparoscopic serious game Underground.MethodsAll study participants completed 2 levels of Underground. Performance for 2 novel variables (time and error) was compared between novices (n=65, prior experience <10 laparoscopic procedures), intermediates (n=26, prior experience 10-100 laparoscopic procedures), and experts (n=20, prior experience >100 laparoscopic procedures) using analysis of covariance. We corrected for gender and video game experience.ResultsControlling for gender and video game experience, the effects of prior laparoscopic experience on the time variable differed significantly (F2,106=4.77, P=.01). Both experts and intermediates outperformed novices in terms of task completion speed; experts did not outperform intermediates. A similar trend was seen for the rate of gameplay errors. Both gender (F1,106=14.42, P<.001 in favor of men) and prior video game experience (F1,106=5.20, P=.03 in favor of experienced gamers) modulated the time variable.ConclusionsWe established construct validity for the laparoscopic serious game Underground. Serious gaming may aid laparoscopic skills development. Previous gaming experience and gender also influenced Underground performance. The in-game performance metrics were not suitable for statistical evaluation. To unlock the full potential of serious gaming for training, a more formal approach to performance metric development is needed.

During surgical training, it is important for the surgeon develops good motor skills throughout his training. For this reason, various surgical training systems have been developed to enhance these skills. However, one of the great challenges in these training systems is being able to objectively measure the ability and performance of the main surgical tasks, where currently only a global measurement is obtained once the task is completed. In this work, a temporal evaluation scheme is proposed, that is, an evaluation of local surgical performance at different time intervals during the training of typical tasks (knot-tying, needle-passing and suturing). The goal is to automatically classify expert (experience >100 hrs) and non-expert (experience <10 hrs) surgeons according to their performance during training, based on three classifiers: K-Nearest Neighborhood, Random Forest, and Support Vector Machine Unlike other previously reported methods, this work proposes a new evaluation scheme based on segments or time intervals, which can be an indicator of the surgeon's local performance during a robotic surgical task, without the need for direct labeling of the data at the segment level. The classification performance from obtained results was in accuracy 83% to 100%, 88% to 100% of AUC-ROC, and 88% to 100% of F1-Score in the final test between experts and non-experts surgeons, where the Support Vector Machine classifier presented the best performance. These results suggest that this proposed method by time intervals could be used in various surgical trainers to evaluate the local performance of a surgeon during trainingand thus be able to provide a tool for the quantitative visualization of opportunities to improve surgical skills.Clinical relevance- We consider that the proposed method to carry out a local performance evaluation during surgical training can provide useful information in the learning and improvement of surgical skills.

We aim to develop quantitative performance metrics and a deep learning model to objectively assess surgery skills between the novice and the expert surgeons for arthroscopic rotator cuff surgery. These proposed metrics can be used to give the surgeon an objective and a quantitative self-assessment platform. Ten shoulder arthroscopic rotator cuff surgeries were performed by two novices, and fourteen were performed by two expert surgeons. These surgeries were statistically analyzed. Two existing evaluation systems: Basic Arthroscopic Knee Skill Scoring System (BAKSSS) and the Arthroscopic Surgical Skill Evaluation Tool (ASSET), were used to validate our proposed metrics. In addition, a deep learning-based model called Automated Arthroscopic Video Evaluation Tool (AAVET) was developed toward automating quantitative assessments. The results revealed that novice surgeons used surgical tools approximately 10% less effectively and identified and stopped bleeding less swiftly. Our results showed a notable difference in the performance score between the experts and novices, and our metrics successfully identified these at the task level. Moreover, the F1-scores of each class are found as 78%, 87%, and 77% for classifying cases with no-tool, electrocautery, and shaver tool, respectively. We have constructed quantitative metrics that identified differences in the performances of expert and novice surgeons. Our ultimate goal is to validate metrics further and incorporate these into our virtual rotator cuff surgery simulator (ViRCAST), which has been under development. The initial results from AAVET show that the capability of the toolbox can be extended to create a fully automated performance evaluation platform.

Revisiting sylvian fissure dissection - A preliminary investigation into surgical process modelling for evaluating surgical proficiency.

Background and Objectives:Handedness, or the inherent dominance of one hand's dexterity over the other's, is a factor in open surgery but has an unknown importance in robot-assisted surgery. We sought to examine whether the robotic surgery platform could eliminate the effect of inherent hand preference.Methods:Residents from the Urology and Obstetrics/Gynecology departments were enrolled. Ambidextrous and left-handed subjects were excluded. After completing a questionnaire, subjects performed three tasks modified from the Fundamentals of Laparoscopic Surgery curriculum. Tasks were performed by hand and then with the da Vinci robotic surgical system (Intuitive Surgical, Sunnyvale, California). Participants were randomized to begin with using either the left or the right hand, and then switch. Left:right ratios were calculated from scores based on time to task completion. Linear regression analysis was used to determine the significance of the impact of surgical technique on hand dominance.Results:Ten subjects were enrolled. The mean difference in raw score performance between the right and left hands was 12.5 seconds for open tasks and 8 seconds for robotic tasks (P < .05). Overall left-right ratios were found to be 1.45 versus 1.12 for the open and robot tasks, respectively (P < .05). Handedness significantly differed between robotic and open approaches for raw time scores (P < .0001) and left-right ratio (P = .03) when controlling for the prior tasks completed, starting hand, prior robotic experience, and comfort level. These findings remain to be validated in larger cohorts.Conclusion:The robotic technique reduces hand dominance in surgical trainees across all task domains. This finding contributes to the known advantages of robotic surgery.