Simulation training in urology.

PURPOSE OF REVIEW: This review outlines recent innovations in simulation technology as it applies to urology. It is essential for the next generation of urologists to attain a solid foundation of technical and nontechnical skills, and simulation technology provides a variety of safe, controlled environments to acquire this baseline knowledge. RECENT FINDINGS: With a focus on urology, this review first outlines the evidence to support surgical simulation, then discusses the strides being made in the development of 3D-printed models for surgical skill training and preoperative planning, virtual reality models for different urologic procedures, surgical skill assessment for simulation, and integration of simulation into urology residency curricula. SUMMARY: Simulation continues to be an integral part of the journey towards the mastery of skills necessary for becoming an expert urologist. Clinicians and researchers should consider how to further incorporate simulation technology into residency training and help future generations of urologists throughout their career.

Exploring the Use of Artificial Intelligence in the Management of Prostate Cancer.

PURPOSE OF REVIEW: This review aims to explore the current state of research on the use of artificial intelligence (AI) in the management of prostate cancer. We examine the various applications of AI in prostate cancer, including image analysis, prediction of treatment outcomes, and patient stratification. Additionally, the review will evaluate the current limitations and challenges faced in the implementation of AI in prostate cancer management. RECENT FINDINGS: Recent literature has focused particularly on the use of AI in radiomics, pathomics, the evaluation of surgical skills, and patient outcomes. AI has the potential to revolutionize the future of prostate cancer management by improving diagnostic accuracy, treatment planning, and patient outcomes. Studies have shown improved accuracy and efficiency of AI models in the detection and treatment of prostate cancer, but further research is needed to understand its full potential as well as limitations.

Tailored Feedback Based on Clinically Relevant Performance Metrics Expedites the Acquisition of Robotic Suturing Skills-An Unblinded Pilot Randomized Controlled Trial.

PURPOSE: Previously, we identified 8 objective suturing performance metrics highly predictive of urinary continence recovery after robotic-assisted radical prostatectomy. Here, we aimed to test the feasibility of providing tailored feedback based upon these clinically relevant metrics and explore the impact on the acquisition of robotic suturing skills. MATERIALS AND METHODS: Training surgeons were recruited and randomized to a feedback group or a control group. Both groups completed a baseline, midterm and final dry laboratory vesicourethral anastomosis (VUA) and underwent 4 intervening training sessions each, consisting of 3 suturing exercises. Eight performance metrics were recorded during each exercise: 4 automated performance metrics (derived from kinematic and system events data of the da Vinci® Robotic System) representing efficiency and console manipulation competency, and 4 suturing technical skill scores. The feedback group received tailored feedback (a visual diagram+verbal instructions+video examples) based on these metrics after each session. Generalized linear mixed model was used to compare metric improvement (Δ) from baseline to the midterm and final VUA. RESULTS: Twenty-three participants were randomized to the feedback group (11) or the control group (12). Demographic data and baseline VUA metrics were comparable between groups. The feedback group showed greater improvement than the control group in aggregate suturing scores at midterm (mean Δ feedback group 4.5 vs Δ control group 1.1) and final VUA (Δ feedback group 5.3 vs Δ control group 4.9). The feedback group also showed greater improvement in the majority of the included metrics at midterm and final VUA. CONCLUSIONS: Tailored feedback based on specific, clinically relevant performance metrics is feasible and may expedite the acquisition of robotic suturing skills.

Robotic and robot-assisted skull base neurosurgery: systematic review of current applications and future directions.

OBJECTIVE: The utility of robotic instrumentation is expanding in neurosurgery. Despite this, successful examples of robotic implementation for endoscopic endonasal or skull base neurosurgery remain limited. Therefore, the authors performed a systematic review of the literature to identify all articles that used robotic systems to access the sella or anterior, middle, or posterior cranial fossae. METHODS: A systematic review of MEDLINE and PubMed in accordance with PRISMA guidelines performed for articles published between January 1, 1990, and August 1, 2021, was conducted to identify all robotic systems (autonomous, semiautonomous, or surgeon-controlled) used for skull base neurosurgical procedures. Cadaveric and human clinical studies were included. Studies with exclusively otorhinolaryngological applications or using robotic microscopes were excluded. RESULTS: A total of 561 studies were identified from the initial search, of which 22 were included following full-text review. Transoral robotic surgery (TORS) using the da Vinci Surgical System was the most widely reported system (4 studies) utilized for skull base and pituitary fossa procedures; additionally, it has been reported for resection of sellar masses in 4 patients. Seven cadaveric studies used the da Vinci Surgical System to access the skull base using alternative, non-TORS approaches (e.g., transnasal, transmaxillary, and supraorbital). Five cadaveric studies investigated alternative systems to access the skull base. Six studies investigated the use of robotic endoscope holders. Advantages to robotic applications in skull base neurosurgery included improved lighting and 3D visualization, replication of more traditional gesture-based movements, and the ability for dexterous movements ordinarily constrained by small operative corridors. Limitations included the size and angulation capacity of the robot, lack of drilling components preventing fully robotic procedures, and cost. Robotic endoscope holders may have been particularly advantageous when the use of a surgical assistant or second surgeon was limited. CONCLUSIONS: Robotic skull base neurosurgery has been growing in popularity and feasibility, but significant limitations remain. While robotic systems seem to have allowed for greater maneuverability and 3D visualization, their size and lack of neurosurgery-specific tools have continued to prevent widespread adoption into current practice. The next generation of robotic technologies should prioritize overcoming these limitations.

An Objective Assessment of Performance during Robotic Partial Nephrectomy: Validation and Correlation of Automated Performance Metrics with Intraoperative Outcomes.

PURPOSE: Automated performance metrics provide a novel approach to the assessment of surgical performance. Herein, we present a construct validation of automated performance metrics during robotic assisted partial nephrectomy. MATERIALS AND METHODS: Automated performance metrics (instrument motion tracking/system events) and synchronized surgical videos from da Vinci® Si systems during robotic assisted partial nephrectomy were recorded using a system data recorder. Each case was segmented into 7 steps: colon mobilization, ureteral identification/dissection, hilar dissection, exposure of tumor within Gerota's fascia, intraoperative ultrasound/tumor scoring, tumor excision, and renorrhaphy. Automated performance metrics from each step were compared between expert (≥150 cases) and trainee (<150 cases) surgeons by Mann-Whitney U test (continuous variables) and Pearson's chi-squared test (categorical variables). Clinical outcomes were collected prospectively and correlated to automated performance metrics and R.E.N.A.L. (radius, exophytic/endophytic, nearness of tumor to collecting system, anterior/posterior, location relative to polar line) nephrometry score by Spearman's correlation coefficients (r). RESULTS: A total of 50 robotic assisted partial nephrectomy cases were included for analysis, performed by 7 expert and 10 trainee surgeons. Automated performance metric profiles significantly differed between experts and novices in the initial 5 steps (p <0.05). Specifically, experts exhibited faster dominant instrument movement and greater dominant instrument usage (bimanual dexterity) than trainees in select steps (p ≤0.045). Automated performance metrics during tumor excision and renorrhaphy were significantly correlated with R.E.N.A.L. score (r ≥0.364; p ≤0.041). These included metrics related to instrument efficiency, task duration, and dominant instrument use. CONCLUSIONS: Experts are more efficient and directed in their movement during robotic assisted partial nephrectomy. Automated performance metrics during key steps correlate with objective measures of tumor complexity and may serve as predictors of clinical outcomes. These data help establish a standardized metric for surgeon assessment and training during robotic assisted partial nephrectomy.

A Novel Dissection Gesture Classification to Characterize Robotic Dissection Technique for Renal Hilar Dissection.

PURPOSE: Deconstruction of robotic surgical gestures into semantic vocabulary yields an effective tool for surgical education. In this study we disassembled tissue dissection into basic gestures, created a classification system, and showed its ability to distinguish between experts and novices. MATERIALS AND METHODS: Videos of renal hilum preparation during robotic assisted partial nephrectomies were manually reviewed to identify all discrete surgical movements. Identified dissection movements were classified into distinct gestures based on the consensus of 6 expert surgeons. This classification system was then employed to compare expert and novice dissection patterns during the renal hilum preparation. RESULTS: A total of 40 robotic renal hilum preparation videos were reviewed, representing 16 from 6 expert surgeons (100 or more robotic cases) and 24 from 13 novice surgeons (fewer than 100 robotic cases). Overall 9,819 surgical movements were identified, including 5,667 dissection movements and 4,152 supporting movements. Nine distinct dissection gestures were identified and classified into the 3 categories of single blunt dissection (spread, peel/push, hook), single sharp dissection (cold cut, hot cut and burn dissect) and combination gestures (pedicalize, 2-hand spread, and coagulate then cut). Experts completed 5 of 9 dissection gestures more efficiently than novices (p ≤0.033). In consideration of specific anatomical locations, experts used more peel/push and less hot cut while dissecting the renal vein (p <0.001), and used more pedicalize while dissecting the renal artery (p <0.001). CONCLUSIONS: Using this novel dissection gesture classification system, key differences in dissection patterns can be found between experts/novices. This comprehensive classification of dissection gestures may be broadly applied to streamline surgical education.

Multi-institutional validation of a perfused robot-assisted partial nephrectomy procedural simulation platform utilizing clinically relevant objective metrics of simulators (CROMS).

OBJECTIVE: To conduct a multi-institutional validation of a high-fidelity, perfused, inanimate, simulation platform for robot-assisted partial nephrectomy (RAPN) using incorporated clinically relevant objective metrics of simulation (CROMS), applying modern validity standards. MATERIALS AND METHODS: Using a combination of three-dimensional (3D) printing and hydrogel casting, a RAPN model was developed from the computed tomography scan of a patient with a 4.2-cm, upper-pole renal tumour (RENAL nephrometry score 7×). 3D-printed casts designed from the patient's imaging were used to fabricate and register hydrogel (polyvinyl alcohol) components of the kidney, including the vascular and pelvicalyceal systems. After mechanical and anatomical verification of the kidney phantom, it was surrounded by other relevant hydrogel organs and placed in a laparoscopic trainer. Twenty-seven novice and 16 expert urologists, categorized according to caseload, from five academic institutions completed the simulation. RESULTS: Clinically relevant objective metrics of simulators, operative complications, and objective performance ratings (Global Evaluative Assessment of Robotic Skills [GEARS]) were compared between groups using Wilcoxon rank-sum (continuous variables) and parametric chi-squared (categorical variables) tests. Pearson and point-biserial correlation coefficients were used to correlate GEARS scores to each CROMS variable. Post-simulation questionnaires were used to obtain subjective supplementation of realism ratings and training effectiveness. RESULTS: Expert ratings demonstrated the model's superiority to other procedural simulations in replicating procedural steps, bleeding, tissue texture and appearance. A significant difference between groups was demonstrated in CROMS [console time (P < 0.001), warm ischaemia time (P < 0.001), estimated blood loss (P < 0.001)] and GEARS (P < 0.001). Six major intra-operative complications occurred only in novice simulations. GEARS scores highly correlated with the CROMS. CONCLUSIONS: This perfused, procedural model offers an unprecedented realistic simulation platform, which incorporates objective, clinically relevant and procedure-specific performance metrics.

How the use of the artificial intelligence could improve surgical skills in urology: state of the art and future perspectives.

PURPOSE OF REVIEW: As technology advances, surgical training has evolved in parallel over the previous decade. Training is commonly seen as a way to prepare surgeons for their day-to-day work; however, more importantly, it allows for certification of skills to ensure maximum patient safety. This article reviews advances in the use of machine learning and artificial intelligence for improvements of surgical skills in urology. RECENT FINDINGS: Six studies have been published, which met the inclusion criteria. All articles assessed the application of artificial intelligence in improving surgical training. Different approaches were taken, such as using machine learning to identify and classify suturing gestures, creating automated objective evaluation reports, and determining surgical technical skill levels to predict clinical outcomes. The articles illustrated the continuously growing role of artificial intelligence to address the difficulties currently present in evaluating urological surgical skills. SUMMARY: Artificial intelligence allows us to efficiently analyze the surmounting data related to surgical training and use it to come to conclusions that normally would require human intelligence. Although these metrics have been shown to predict surgeon expertise and surgical outcomes, evidence is still scarce regarding their ability to directly improve patient outcomes. Considering this, current active research is growing on the topic of deep learning-based computer vision to provide automated metrics needed for real-time surgeon feedback.

Innovations in Urologic Surgical Training.

PURPOSE OF REVIEW: This review aims to summarize innovations in urologic surgical training in the past 5 years. RECENT FINDINGS: Many assessment tools have been developed to objectively evaluate surgical skills and provide structured feedback to urologic trainees. A variety of simulation modalities (i.e., virtual/augmented reality, dry-lab, animal, and cadaver) have been utilized to facilitate the acquisition of surgical skills outside the high-stakes operating room environment. Three-dimensional printing has been used to create high-fidelity, immersive dry-lab models at a reasonable cost. Non-technical skills such as teamwork and decision-making have gained more attention. Structured surgical video review has been shown to improve surgical skills not only for trainees but also for qualified surgeons. Research and development in urologic surgical training has been active in the past 5 years. Despite these advances, there is still an unfulfilled need for a standardized surgical training program covering both technical and non-technical skills.

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

PURPOSE: In this study, we investigate the ability of automated performance metrics (APMs) and task-evoked pupillary response (TEPR), as objective measures of surgeon performance, to distinguish varying levels of surgeon expertise during generic robotic surgical tasks. Additionally, we evaluate the association between APMs and TEPR. METHODS: Participants completed ten tasks on a da Vinci Xi Surgical System (Intuitive Surgical, Inc.), each representing a surgical skill type: EndoWrist® manipulation, needle targeting, suturing/knot tying, and excision/dissection. Automated performance metrics (instrument motion tracking, EndoWrist® articulation, and system events data) and TEPR were recorded by a systems data recorder (Intuitive Surgical, Inc.) and Tobii Pro Glasses 2 (Tobii Technologies, Inc.), respectively. The Kruskal-Wallis test determined significant differences between groups of varying expertise. Spearman's rank correlation coefficient measured associations between APMs and TEPR. RESULTS: Twenty-six participants were stratified by robotic surgical experience: novice (no prior experience; n = 9), intermediate (< 100 cases; n = 9), and experts (≥ 100 cases; n = 8). Several APMs differentiated surgeon experience including task duration (p < 0.01), time active of instruments (p < 0.03), linear velocity of instruments (p < 0.04), and angular velocity of dominant instrument (p < 0.04). Task-evoked pupillary response distinguished surgeon expertise for three out of four task types (p < 0.04). Correlation trends between APMs and TEPR revealed that expert surgeons move more slowly with high cognitive workload (ρ < - 0.60, p < 0.05), while novices move faster under the same cognitive experiences (ρ > 0.66, p < 0.05). CONCLUSIONS: Automated performance metrics and TEPR can distinguish surgeon expertise levels during robotic surgical tasks. Furthermore, under high cognitive workload, there can be a divergence in robotic movement profiles between expertise levels.

Comparison of clinical outcomes and automated performance metrics in robot-assisted radical prostatectomy with and without trainee involvement.

PURPOSE: In this study, we investigate the effect of trainee involvement on surgical performance, as measured by automated performance metrics (APMs), and outcomes after robot-assisted radical prostatectomy (RARP). METHODS: We compared APMs (instrument tracking, EndoWrist® articulation, and system events data) and clinical outcomes for cases with varying resident involvement. Four of 12 standardized RARP steps were designated critical ("cardinal") steps. Comparison 1: cases where the attending surgeon performed all four cardinal steps (Group A) and cases where a trainee was involved in at least one cardinal step (Group B). Comparison 2, where Group A is split into Groups C and D: cases where attending performs the whole case (Group C) vs. cases where a trainee performed at least one non-cardinal step (Group D). Mann-Whitney U and Chi-squared tests were used for comparisons. RESULTS: Comparison 1 showed significant differences in APM profiles including camera movement time, third instrument usage, dominant instrument moving time, velocity, articulation, as well as non-dominant instrument moving time and articulation (all favoring Group A p < 0.05). There was a significant difference in re-admission rates (10.9% in Group A vs 0% in Group B, p < 0.02), but not for post-operative outcomes. Comparison 2 demonstrated a significant difference in dominant instrument articulation (p < 0.05) but not in post-operative outcomes. CONCLUSIONS: Trainee involvement in RARP is safe. The degree of trainee involvement does not significantly affect major clinical outcomes. APM profiles are less efficient when trainees perform at least one cardinal step but not during non-cardinal steps.

Machine learning in the optimization of robotics in the operative field.

PURPOSE OF REVIEW: The increasing use of robotics in urologic surgery facilitates collection of 'big data'. Machine learning enables computers to infer patterns from large datasets. This review aims to highlight recent findings and applications of machine learning in robotic-assisted urologic surgery. RECENT FINDINGS: Machine learning has been used in surgical performance assessment and skill training, surgical candidate selection, and autonomous surgery. Autonomous segmentation and classification of surgical data have been explored, which serves as the stepping-stone for providing real-time surgical assessment and ultimately, improve surgical safety and quality. Predictive machine learning models have been created to guide appropriate surgical candidate selection, whereas intraoperative machine learning algorithms have been designed to provide 3-D augmented reality and real-time surgical margin checks. Reinforcement-learning strategies have been utilized in autonomous robotic surgery, and the combination of expert demonstrations and trial-and-error learning by the robot itself is a promising approach towards autonomy. SUMMARY: Robot-assisted urologic surgery coupled with machine learning is a burgeoning area of study that demonstrates exciting potential. However, further validation and clinical trials are required to ensure the safety and efficacy of incorporating machine learning into surgical practice.

Objective Assessment of Robotic Surgical Technical Skill: A Systematic Review.

PURPOSE: Robotic surgeries, especially in urology, have grown exponentially during the last decade. Various skills assessment tools have been developed. We reviewed the current status, the current challenges and the future needs of robotic evaluations with a focus on urological applications. MATERIALS AND METHODS: According to PRISMA (Preferred Reporting Items for Systematic Review and Meta-Analysis) criteria 2 paired investigators screened the PubMed®, Scopus® and Web of Science® databases for all full text, English language articles published between 2006 and 2018 using the query (evaluation OR assessment) AND (robot-assisted surgery OR robotic surgery) AND (surgical performance OR surgical skill) AND training. The research design, validity and reliability of each study were ascertained and analyzed. RESULTS: A total of 259 studies were identified, of which 109 were included in the final analysis. We grouped the studies into 2 categories, including manual and automated assessments. Manual evaluation included global skill, procedure specific and error based assessments. For automated assessment we summarized evaluations derived from robotic instrument kinematic tracking data, systems events and surgical video data, and we explored those associations with various domains by manual evaluation. We further reviewed the current progress in automated surgical segmentation and skill evaluation with machine learning and deep learning. Concerns remain regarding efficient and effective surgeon training and credentialing. CONCLUSIONS: No universally accepted robotic skills assessment currently exists. The purpose of assessment (training or credentialing) may dictate whether manual or automated surgeon assessment is more suitable. Moving forward, assessment tools must be objective and efficient to facilitate the training and credentialing of competent surgeons.

Effect of surgeon experience and bony pelvic dimensions on surgical performance and patient outcomes in robot-assisted radical prostatectomy.

OBJECTIVES: To evaluate the effects of surgeon experience, body habitus, and bony pelvic dimensions on surgeon performance and patient outcomes after robot-assisted radical prostatectomy (RARP). PATIENTS, SUBJECTS AND METHODS: The pelvic dimensions of 78 RARP patients were measured on preoperative magnetic resonance imaging and computed tomography by three radiologists. Surgeon automated performance metrics (APMs [instrument motion tracking and system events data, i.e., camera movement, third-arm swap, energy use]) were obtained by a systems data recorder (Intuitive Surgical, Sunnyvale, CA, USA) during RARP. Two analyses were performed: Analysis 1, examined effects of patient characteristics, pelvic dimensions and prior surgeon RARP caseload on APMs using linear regression; Analysis 2, the effects of patient body habitus, bony pelvic measurement, and surgeon experience on short- and long-term outcomes were analysed by multivariable regression. RESULTS: Analysis 1 showed that while surgeon experience affected the greatest number of APMs (P < 0.044), the patient's body mass index, bony pelvic dimensions, and prostate size also affected APMs during each surgical step (P < 0.043, P < 0.046, P < 0.034, respectively). Analysis 2 showed that RARP duration was significantly affected by pelvic depth (ß = 13.7, P = 0.039) and prostate volume (ß = 0.5, P = 0.024). A wider and shallower pelvis was less likely to result in a positive margin (odds ratio 0.25, 95% confidence interval [CI] 0.09-0.72). On multivariate analysis, urinary continence recovery was associated with surgeon's prior RARP experience (hazard ratio [HR] 2.38, 95% CI 1.18-4.81; P = 0.015), but not on pelvic dimensions (HR 1.44, 95% CI 0.95-2.17). CONCLUSION: Limited surgical workspace, due to a narrower and deeper pelvis, does affect surgeon performance and patient outcomes, most notably in longer surgery time and an increased positive margin rate.

Experts vs super-experts: differences in automated performance metrics and clinical outcomes for robot-assisted radical prostatectomy.

OBJECTIVES: To evaluate automated performance metrics (APMs) and clinical data of experts and super-experts for four cardinal steps of robot-assisted radical prostatectomy (RARP): bladder neck dissection; pedicle dissection; prostate apex dissection; and vesico-urethral anastomosis. SUBJECTS AND METHODS: We captured APMs (motion tracking and system events data) and synchronized surgical video during RARP. APMs were compared between two experience levels: experts (100-750 cases) and super-experts (2100-3500 cases). Clinical outcomes (peri-operative, oncological and functional) were then compared between the two groups. APMs and outcomes were analysed for 125 RARPs using multi-level mixed-effect modelling. RESULTS: For the four cardinal steps selected, super-experts showed differences in select APMs compared with experts (P < 0.05). Despite similar PSA and Gleason scores, super-experts outperformed experts clinically with regard to peri-operative outcomes, with a greater lymph node yield of 22.6 vs 14.9 nodes, respectively (P < 0.01), less blood loss (125 vs 130 mL, respectively; P < 0.01), and fewer readmissions at 30 days (1% vs 13%, respectively; P = 0.02). A similar but nonsignificant trend was seen for oncological and functional outcomes, with super-experts having a lower rate of biochemical recurrence compared with experts (5% vs 15%, respectively; P = 0.13) and a higher continence rate at 3 months (36% vs 18%, respectively; P = 0.14). CONCLUSION: We found that experts and super-experts differed significantly in select APMs for the four cardinal steps of RARP, indicating that surgeons do continue to improve in performance even after achieving expertise. We hope ultimately to identify associations between APMs and clinical outcomes to tailor interventions to surgeons and optimize patient outcomes.

A deep-learning model using automated performance metrics and clinical features to predict urinary continence recovery after robot-assisted radical prostatectomy.

OBJECTIVES: To predict urinary continence recovery after robot-assisted radical prostatectomy (RARP) using a deep learning (DL) model, which was then used to evaluate surgeon's historical patient outcomes. SUBJECTS AND METHODS: Robotic surgical automated performance metrics (APMs) during RARP, and patient clinicopathological and continence data were captured prospectively from 100 contemporary RARPs. We used a DL model (DeepSurv) to predict postoperative urinary continence. Model features were ranked based on their importance in prediction. We stratified eight surgeons based on the five top-ranked features. The top four surgeons were categorized in 'Group 1/APMs', while the remaining four were categorized in 'Group 2/APMs'. A separate historical cohort of RARPs (January 2015 to August 2016) performed by these two surgeon groups was then used for comparison. Concordance index (C-index) and mean absolute error (MAE) were used to measure the model's prediction performance. Outcomes of historical cases were compared using the Kruskal-Wallis, chi-squared and Fisher's exact tests. RESULTS: Continence was attained in 79 patients (79%) after a median of 126 days. The DL model achieved a C-index of 0.6 and an MAE of 85.9 in predicting continence. APMs were ranked higher by the model than clinicopathological features. In the historical cohort, patients in Group 1/APMs had superior rates of urinary continence at 3 and 6 months postoperatively (47.5 vs 36.7%, P = 0.034, and 68.3 vs 59.2%, P = 0.047, respectively). CONCLUSION: Using APMs and clinicopathological data, the DeepSurv DL model was able to predict continence after RARP. In this feasibility study, surgeons with more efficient APMs achieved higher continence rates at 3 and 6 months after RARP.

Current status of artificial intelligence applications in urology and their potential to influence clinical practice.

OBJECTIVE: 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. MATERIALS 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. RESULTS: 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. CONCLUSION: 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.

Telementoring and Telesurgery for Minimally Invasive Procedures.

PURPOSE: Tremendous interest and need lie at the intersection of telemedicine and minimally invasive surgery. Robotics provides an ideal environment for surgical telementoring and telesurgery given its endoscopic optics and mechanized instrument movement. We review the present status, current challenges and future promise of telemedicine in endoscopic and minimally invasive surgery with a focus on urological applications. MATERIALS AND METHODS: Two paired investigators screened PubMed®, Scopus® and Web of Science® databases for all full text English language articles published between 1995 and 2016 using the key words "telemedicine," "minimally invasive surgical procedure," "robotic surgical procedure," "education" and "distance." We categorized and included studies of level of interaction between proctors and trainees. Research design, special equipment, telecommunication network bandwidth and research outcomes of each study were ascertained and analyzed. RESULTS: Of 65 identified reports 38 peer-reviewed studies qualified for inclusion. Series were categorized into 4 advancing levels, ie verbal guidance, guidance with telestration, guidance with tele-assist and telesurgery. More advanced levels of surgical telementoring provide more effective and experiential teaching but are associated with increased telecommunication network bandwidth requirements and expenses. Concerns regarding patient safety and legal, financial, economic and ethical issues remain to be reconciled. CONCLUSIONS: Telementoring and telesurgery in minimally invasive surgery are becoming more practical and cost effective in facilitating teaching of advanced surgical skills worldwide and delivery of surgical care to underserved areas, yet many challenges remain. Maturity of these modalities depends on financial incentives, favorable legislation and collaboration with cybersecurity experts to ensure safety and cost-effectiveness.

Development and Validation of Objective Performance Metrics for Robot-Assisted Radical Prostatectomy: A Pilot Study.

PURPOSE: We explore and validate objective surgeon performance metrics using a novel recorder ("dVLogger") to directly capture surgeon manipulations on the da Vinci® Surgical System. We present the initial construct and concurrent validation study of objective metrics during preselected steps of robot-assisted radical prostatectomy. MATERIALS AND METHODS: Kinematic and events data were recorded for expert (100 or more cases) and novice (less than 100 cases) surgeons performing bladder mobilization, seminal vesicle dissection, anterior vesicourethral anastomosis and right pelvic lymphadenectomy. Expert/novice metrics were compared using mixed effect statistical modeling (construct validation). Expert reviewers blindly rated seminal vesicle dissection and anterior vesicourethral anastomosis using GEARS (Global Evaluative Assessment of Robotic Skills). Intraclass correlation measured inter-rater variability. Objective metrics were correlated to corresponding GEARS metrics using Spearman's test (concurrent validation). RESULTS: The performance of 10 experts (mean 810 cases, range 100 to 2,000) and 10 novices (mean 35 cases, range 5 to 80) was evaluated in 100 robot-assisted radical prostatectomy cases. For construct validation the experts completed operative steps faster (p <0.001) with less instrument travel distance (p <0.01), less aggregate instrument idle time (p <0.001), shorter camera path length (p <0.001) and more frequent camera movements (p <0.03). Experts had a greater ratio of dominant-to-nondominant instrument path distance for all steps (p <0.04) except anterior vesicourethral anastomosis. For concurrent validation the median experience of 3 expert reviewers was 300 cases (range 200 to 500). Intraclass correlation among reviewers was 0.6-0.7. For anterior vesicourethral anastomosis and seminal vesicle dissection, kinematic metrics had low associations with GEARS metrics. CONCLUSIONS: Objective metrics revealed experts to be more efficient and directed during preselected steps of robot-assisted radical prostatectomy. Objective metrics had limited associations to GEARS. These findings lay the foundation for developing standardized metrics for surgeon training and assessment.

Use of Automated Performance Metrics to Measure Surgeon Performance during Robotic Vesicourethral Anastomosis and Methodical Development of a Training Tutorial.

PURPOSE: We sought to develop and validate automated performance metrics to measure surgeon performance of vesicourethral anastomosis during robotic assisted radical prostatectomy. Furthermore, we sought to methodically develop a standardized training tutorial for robotic vesicourethral anastomosis. MATERIALS AND METHODS: We captured automated performance metrics for motion tracking and system events data, and synchronized surgical video during robotic assisted radical prostatectomy. Nonautomated performance metrics were manually annotated by video review. Automated and nonautomated performance metrics were compared between experts with 100 or more console cases and novices with fewer than 100 cases. Needle driving gestures were classified and compared. We then applied task deconstruction, cognitive task analysis and Delphi methodology to develop a standardized robotic vesicourethral anastomosis tutorial. RESULTS: We analyzed 70 vesicourethral anastomoses with a total of 1,745 stitches. For automated performance metrics experts outperformed novices in completion time (p <0.01), EndoWrist® articulation (p <0.03), instrument movement efficiency (p <0.02) and camera manipulation (p <0.01). For nonautomated performance metrics experts had more optimal needle to needle driver positioning, fewer needle driving attempts, a more optimal needle entry angle and less tissue trauma (each p <0.01). We identified 14 common robotic needle driving gestures. Random gestures were associated with lower efficiency (p <0.01), more attempts (p <0.04) and more trauma (p <0.01). The finalized tutorial contained 66 statements and figures. Consensus among 8 expert surgeons was achieved after 2 rounds, including among 58 (88%) after round 1 and 8 (12%) after round 2. CONCLUSIONS: Automated performance metrics can distinguish surgeon expertise during vesicourethral anastomosis. The expert vesicourethral anastomosis technique was associated with more efficient movement and less tissue trauma. Standardizing robotic vesicourethral anastomosis and using a methodically developed tutorial may help improve robotic surgical training.