Objective: To develop and validate a high-fidelity, nonbiohazardous simulator model for the ultrasound-guided percutaneous nephrolithotomy procedure. Methods: We employed a systematic framework based on Delphi consensus and modern education theory to design a simulation model. Twelve expert surgeons provided input through a hierarchal task analysis and identified procedural tasks, anatomical landmarks, and potential errors. These were translated into engineering deliverables by a team of biomedical engineers and surgical educators. A prototype was developed using three-dimensional printing and hydrogel molding, followed by expert validation through recorded simulations and subsequent multicenter trails with 48 participants. Results: A hydrogel prototype with realistic anatomical features was created using results from the Delphi process. It received positive feedback in areas such as anatomy, procedural fidelity, and education effectiveness, with overall high satisfaction ratings. Validation studies showed a significant difference in performance between novices and experts. Residents demonstrated significant skill improvement and retention after repeated simulations. Conclusions: The developed simulator provides a realistic, effective training tool for urologic education, addressing the need for safer and more accessible surgical training modalities.

You have accessJournal of UrologyLower Tract Reconstruction (Including Transgender) I (V10)1 May 2024V10-01 PRECISION IN PRACTICE: DESIGN AND DEVELOPMENT OF A LOW-COST, HIGH FIDELITY, VASECTOMY HYDROGEL SIMULATION MODEL Ahmed Ghazi, Patrick Saba, Lauren Shepard, Julio Yanes, Francis Petrella, Mary Rostom, and Ranjith Ramasamy Ahmed GhaziAhmed Ghazi , Patrick SabaPatrick Saba , Lauren ShepardLauren Shepard , Julio YanesJulio Yanes , Francis PetrellaFrancis Petrella , Mary RostomMary Rostom , and Ranjith RamasamyRanjith Ramasamy View All Author Informationhttps://doi.org/10.1097/01.JU.0001009392.30237.90.01AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookLinked InTwitterEmail Abstract INTRODUCTION AND OBJECTIVE: Vasectomies are toted as one of the safest and most effective methods of male contraception. However, patient satisfaction and surgical success are highly dependent on the surgeon's ability which is directly correlated to their training. We sought to design a simulation platform to bolster current vasectomy training that replicates the anatomy, tissue texture, and procedural steps with the potential to offer proficiency criteria in the form of clinically relevant objective metrics of simulation performance. METHODS: A hierarchical task analysis by an expert Urologist (annual vasectomy case load >260) identified 12 anatomical landmarks, 10 procedural tasks, 38 subtasks, and 9 errors for a vasectomy procedure. These were converted into fabrication deliverables by engineers to create a high-fidelity male urogenital simulation model utilizing previously validated 3D printing and hydrogel molding techniques. A skin-to-skin vasectomy was performed on the initial prototypes to ensure that all the deliverables were met. RESULTS: The hydrogel model weighs <5lbs, does not require specialized shipping or storage, and has a material cost of ∼$25. Anatomically, it contains all the necessary components required for the procedure including the scrotum (skin, fascia & dartos), spermatic cords, vas deferens, external spermatic sheaths, surrounding sectoral tissues, and lower abdomen. Procedurally, it allows users to perform all 10 key tasks (set-up, local anesthesia, skin incision, vas incision & fascial clearing, delivering vas, dissecting the vas from its fascial sheath, vas ligation, fascial interposition, left/right vasectomy, and closure) and all 38 subtasks. The model also provides error feedback including by not limited to incorrect tissue manipulation (clamping/incisions), injury to surrounding tissue, inability to identify the vas, incorrect ligation/hemostasis/tying of the vas, and lack of/or inadequate fascial barrier. CONCLUSIONS: We created an affordable, high-fidelity, non-biohazardous, hydrogel vasectomy simulation model. Although it is in its early stages, the model provides a risk-free alternative to live training on patients for urology residents to acquire essential skills and confidence in vasectomy procedures. We aim to conduct further validation studies to display its effectiveness in augmenting urologic training. Source of Funding: N/A © 2024 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 211Issue 5SMay 2024Page: e826 Advertisement Copyright & Permissions© 2024 by American Urological Association Education and Research, Inc.Metrics Author Information Ahmed Ghazi More articles by this author Patrick Saba More articles by this author Lauren Shepard More articles by this author Julio Yanes More articles by this author Francis Petrella More articles by this author Mary Rostom More articles by this author Ranjith Ramasamy More articles by this author Expand All Advertisement PDF downloadLoading ...

You have accessJournal of UrologySexual Dysfunction/Infertility/Andrology (V07)1 May 2024V07-02 ENHANCING UROLOGY RESIDENT TRAINING IN VASECTOMIES THROUGH A 3D PRINTED SIMULATION MODEL Julio A. Yanes, David A. Velasquez, Mary Rostom, Francis Petrella, Patrick Saba, Lauren Shepard, Ahmed Ghazi, and Ranjith Ramasamy Julio A. YanesJulio A. Yanes , David A. VelasquezDavid A. Velasquez , Mary RostomMary Rostom , Francis PetrellaFrancis Petrella , Patrick SabaPatrick Saba , Lauren ShepardLauren Shepard , Ahmed GhaziAhmed Ghazi , and Ranjith RamasamyRanjith Ramasamy View All Author Informationhttps://doi.org/10.1097/01.JU.0001008932.49144.fd.02AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookLinked InTwitterEmail Abstract INTRODUCTION AND OBJECTIVE: The limited contraception options for men highlight an urgent need for enhanced training in vasectomies, particularly given the increasing number of vasectomies done in the United States. Urology residents report hesitancy in performing vasectomies, largely due to training barriers such as limited exposure and insufficient autonomy due to patients being awake during the procedure. Traditional cadaveric models, although useful, present issues such as high costs and risk of disease transmission. Addressing these challenges, our project proposed the development of a 3D printed vasectomy training model combined with a comprehensive training video. METHODS: We used magnetic resonance imaging (MRI) and Materialise software to create a computer-aided design for a 3D vasectomy model. The hydrogel-based model was refined using input from a fellowship trained reproductive urologist to ensure anatomical accuracy and utility in enhancing surgical proficiency. The model served as the basis for a comprehensive training video highlighting the procedure's nuances and serving as a teaching aid. RESULTS: The proposed 3D model and training video was offered as a mandatory teaching curriculum to urology residents. The anticipated outcome was a validated training enhancement tool that can be widely disseminated for resident education, offering an innovative solution to the current training deficiencies in vasectomy procedures. CONCLUSIONS: This 3D printed model combined with a comprehensive teaching video for vasectomy has the potential to standardize vasectomy training and increase the pool of resident urologists proficient in this contraceptive method. Source of Funding: Supported by NIDDK grants R01 DK130991, UE5 DK137308, and Clinician Scientist Development Grant from American Cancer Society to RR © 2024 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 211Issue 5SMay 2024Page: e467 Advertisement Copyright & Permissions© 2024 by American Urological Association Education and Research, Inc.Metrics Author Information Julio A. Yanes More articles by this author David A. Velasquez More articles by this author Mary Rostom More articles by this author Francis Petrella More articles by this author Patrick Saba More articles by this author Lauren Shepard More articles by this author Ahmed Ghazi More articles by this author Ranjith Ramasamy More articles by this author Expand All Advertisement PDF downloadLoading ...

You have accessJournal of UrologyReconstruction: Urethral Reconstruction (including stricture) I (MP06)1 May 2024MP06-07 FROM CONSENSUS TO VALIDATION: DESIGN AND DEVELOPMENT OF A HIGH-FIDELITY HYDROGEL SIMULATION MODEL FOR URETHROPLASTY PROCEDURES Patrick Saba, Lauren Shepard, Katherine T. Anderson, Nick Warner, Nima Baradaran, Cooper Benson, William R. Boysen, Benjamin N. Breyer, Lindsay Hampson, Ty T. Higuchi, Niels V. Johnsen, Joseph J. Pariser, Jay Simhan, Alex J. Vanni, Dmitriy Nikolavsky, Edward J. Wright, Arthur L. Burnett, Andrew Cohen, and Ahmed Ghazi Patrick SabaPatrick Saba , Lauren ShepardLauren Shepard , Katherine T. AndersonKatherine T. Anderson , Nick WarnerNick Warner , Nima BaradaranNima Baradaran , Cooper BensonCooper Benson , William R. BoysenWilliam R. Boysen , Benjamin N. BreyerBenjamin N. Breyer , Lindsay HampsonLindsay Hampson , Ty T. HiguchiTy T. Higuchi , Niels V. JohnsenNiels V. Johnsen , Joseph J. PariserJoseph J. Pariser , Jay SimhanJay Simhan , Alex J. VanniAlex J. Vanni , Dmitriy NikolavskyDmitriy Nikolavsky , Edward J. WrightEdward J. Wright , Arthur L. BurnettArthur L. Burnett , Andrew CohenAndrew Cohen , and Ahmed GhaziAhmed Ghazi View All Author Informationhttps://doi.org/10.1097/01.JU.0001009452.79331.fd.07AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookLinked InTwitterEmail Abstract INTRODUCTION AND OBJECTIVE: Urethroplasty is the gold standard treatment for urethral stricture repair; however, it is a technically difficult operation due to complex anatomy, large learning curves, lack of intraoperative teaching, and variations in approaches. Utilizing the consensus-building Delphi process, we sought to create a high-fidelity, non-biohazardous, simulation model to train excision and primary anastomosis or graft urethroplasties. METHODS: 20 high volume reconstructive urologists were recruited to complete the Delphi process aimed to reach expert consensus on necessary parameters and design specifications for the simulation model. Consensus>80% was reached regarding procedural realism, anatomical realism, and educational effectiveness. Using previously validated 3D printing and hydrogel molding techniques, researchers fabricated a hydrogel model that incorporated all the expert determined aspects. Prototypes were sent to 55% of the experts who performed a skin-to-skin urethroplasty simulation (Figure 1) as well as a questionnaire evaluating the model to determine if the consensus defined steps were met. The questionnaire utilized a 5-point Likert scale where agreement=4/5, neutral=3, and disagreement=1/2. RESULTS: 91%, 82%, and 100% agreed the model procedurally replicates: the steps necessary to complete the procedure, tissue textures/behaviors, and anatomical relationships including urethral spatulation (91%), suture placement (91%), perineal incision/exposure (91%), and urethral dissection/exposure (100%). 82%, 91%, 64%, 91%, 55%, and 91% agreed it anatomically replicates the; perineum, urethra, fascia over urethra, corpora cavernosa and spongiosum, and bulbospongiosus muscle. 100%, 100%, 100%, 91% and 82% agreed it offers: a safe/non-biohazardous training platform, is useful for teaching/practicing, improving the technical skills, can assess user's ability to perform this procedure and provides useful error feedback. CONCLUSIONS: We successfully designed a high-fidelity, non-biohazardous, simulation model for urethroplasty procedures utilizing expert consensus which displayed high procedural realism, anatomical realism, and educational effectiveness. Ultimately, this model can be used to improve current urethroplasty training. Download PPT Source of Funding: N/A © 2024 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 211Issue 5SMay 2024Page: e54 Advertisement Copyright & Permissions© 2024 by American Urological Association Education and Research, Inc.Metrics Author Information Patrick Saba More articles by this author Lauren Shepard More articles by this author Katherine T. Anderson More articles by this author Nick Warner More articles by this author Nima Baradaran More articles by this author Cooper Benson More articles by this author William R. Boysen More articles by this author Benjamin N. Breyer More articles by this author Lindsay Hampson More articles by this author Ty T. Higuchi More articles by this author Niels V. Johnsen More articles by this author Joseph J. Pariser More articles by this author Jay Simhan More articles by this author Alex J. Vanni More articles by this author Dmitriy Nikolavsky More articles by this author Edward J. Wright More articles by this author Arthur L. Burnett More articles by this author Andrew Cohen More articles by this author Ahmed Ghazi More articles by this author Expand All Advertisement PDF downloadLoading ...

With the advancement of surgical technology, the opportunity to integrate novel surgical preparation is imperative to improve patient outcomes and enhance safety. Patient specific perfused kidney phantoms including the tumor, parenchyma, artery, vein, and calyx were fabricated using 3D-printing and hydrogel injection molding from scans of 25 patients scheduled for robotic partial-nephrectomy (RAPN). Models are validated for anatomical accuracy, mechanical, functional properties and surrounded by the other models of relevant anatomy in a body cast for a simulated surgical rehearsal. We investigated the impact of these preoperative rehearsals preceding complex RAPN by analyzing changes in surgeons’ decisions following review of both axial-imaging and following rehearsal simulation. Predictive ability of these rehearsal platforms was compared to live surgery outcomes and trifecta of cases as an outcome was calculated. 25 patients with complex renal tumors, average 9.8 nephrometry score and 4.9 cm mean tumor diameter were consented. Mean blood loss and WIT were 193.2 ml and 19.8 min. Two Clavien 2 complications were reported at 30-day postoperative. Trifecta was achieved in 17 (68

You have accessJournal of UrologyGlobal Health/Humanitarian (PD15)1 May 2024PD15-01 DEVELOPMENT OF A HIGH-FIDELITY AFFORDABLE HYDROGEL TRAINING MODEL FOR THE MANAGEMENT OF ISCHEMIC PRIAPISM IN DEVELOPING COUNTRIES Patrick Saba, Lauren Shepard, Nathan Schuler, Sani Aji, Arthur L. Burnett, and Ahmed Ghazi Patrick SabaPatrick Saba , Lauren ShepardLauren Shepard , Nathan SchulerNathan Schuler , Sani AjiSani Aji , Arthur L. BurnettArthur L. Burnett , and Ahmed GhaziAhmed Ghazi View All Author Informationhttps://doi.org/10.1097/01.JU.0001008648.33830.32.01AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookLinked InTwitterEmail Abstract INTRODUCTION AND OBJECTIVE: Ischemic priapism (IP) is characterized by a lack of circulation leading to necrosis, erectile dysfunction (ED), and scarring of penile tissue. This demands immediate treatment to relieve stagnant blood and restore circulation. IP affects about 33% of men with sickle cell disease (SCD), leading to ED rates that are 2.5x higher in men with SCD, but 5x higher in men with IP as well. In sub-Saharan Africa, about 300,000 babies are born with SCD annually, creating a need for widespread training. We created a high-fidelity, affordable, non-biohazardous simulation model to teach the management of IP in countries with limited medical access/training. METHODS: We fabricated a simulation to teach needle aspiration and corporal glandular shunt techniques using 3D printing and hydrogel molding (Figure 1A). The model contains the urethra, corpus spongiosum, and corpus cavernosa, weighs <1lb, does not require specialized shipping or storage, and has an material cost of ∼$5.5 models were sent to the Amino Kano Teaching Hospital in Kano, Nigeria. 16 urology practitioners completed a hands-on simulation led by an expert instructor from Johns Hopkins Hospital (Figure 1B). Subjective survey data was collected using a 5-point Likert scale where agreement=4/5, neutral=3, and disagreement=1/2. RESULTS: 100% and 94% agreed the hydrogel model replicates the anatomy and procedural steps for IP management with 100% and 94% specifically agreeing it replicates the needle aspiration and corporal glandular shunt approaches respectively. 94%, 100%, 100%, and 100% agreed it is useful for visualizing, improving technical skills, teaching, and assessing user ability for IP management. 100% agreed it is easy-to-use and a safe non-biohazardous alternative to traditional teaching methods. 69%, 100%, and 88% agreed the it offers useful error feedback, a low stress environment, and improves user confidence. CONCLUSIONS: This simulation offers a high-fidelity, affordable, hands-on training for areas with limited medical access/training, but high prevalence of SCD/IP. The non-biohazardous material allows training to occur anywhere by avoiding barriers of use due to religion or governmental restrictions. Ultimately, the model offers a new way to expand urologic training while circumventing barriers of access. Download PPT Source of Funding: N/A © 2024 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 211Issue 5SMay 2024Page: e359 Advertisement Copyright & Permissions© 2024 by American Urological Association Education and Research, Inc.Metrics Author Information Patrick Saba More articles by this author Lauren Shepard More articles by this author Nathan Schuler More articles by this author Sani Aji More articles by this author Arthur L. Burnett More articles by this author Ahmed Ghazi More articles by this author Expand All Advertisement PDF downloadLoading ...

The limited contraception options for men highlight an urgent need for enhanced training in vasectomies, particularly given the increasing number of vasectomies performed in the United States. Urology residents report hesitancy in performing vasectomies largely because of training barriers such as limited exposure and insufficient autonomy because of patients being awake during the procedure. Traditional cadaveric models, although useful, present issues such as high costs and risk of disease transmission. Addressing these challenges, our project proposed the development of a 3D printed vasectomy training model combined with a comprehensive training video (Video 1). {"href":"Single Video Player","role":"media-player-id","content-type":"play-in-place","position":"float","orientation":"portrait","label":"Video 1","caption":"Design and implementation of our hydrogel vasectomy simulation model.","object-id":[{"pub-id-type":"doi","id":""},{"pub-id-type":"other","content-type":"media-stream-id","id":"1_ka2mr53d"},{"pub-id-type":"other","content-type":"media-source","id":"Kaltura"}]} METHODS We used MRI and Materialise software to create a computer-aided design for a 3D vasectomy model. The hydrogel-based model was refined using input from a fellowship-trained reproductive urologist to ensure anatomical accuracy and utility in enhancing surgical proficiency. The model served as the basis for a comprehensive training video highlighting the procedure's nuances and serving as a teaching aid. RESULTS The proposed 3D model and training video were offered as a mandatory teaching curriculum to urology residents. The anticipated outcome was a validated training enhancement tool that can be widely disseminated for resident education, offering an innovative solution to the current training deficiencies in vasectomy procedures. CONCLUSIONS This 3D printed model combined with a comprehensive teaching video for vasectomy has the potential to standardize vasectomy training and increase the pool of resident urologists proficient in this contraceptive method. SOURCE OF FUNDING Supported by NIDDK grants R01 DK130991, UE5 DK137308, and Clinician Scientist Development Grant from American Cancer Society to RR. RECUSAL Dr Clifton, associate editor of JU Open Plus, was recused from the editorial and peer review processes due to affiliation with Johns Hopkins Hospital.

You have accessJournal of UrologySurgical Technology & Simulation: Instrumentation & Technology II (MP43)1 May 2024MP43-04 SINGLE-PORT THUNDERDOME: DESIGN OF A CUSTOMIZABLE SINGLE PORT ROBOTIC SKILL DEVELOPMENT PLATFORM USING A CONSENSUS-DRIVEN APPROACH Nathan A. Schuler, Lauren Shepard, Patrick Saba, Sammy Elsamra, Simone Crivellaro, Jihad Kaouk, and Ahmed Ghazi Nathan A. SchulerNathan A. Schuler , Lauren ShepardLauren Shepard , Patrick SabaPatrick Saba , Sammy ElsamraSammy Elsamra , Simone CrivellaroSimone Crivellaro , Jihad KaoukJihad Kaouk , and Ahmed GhaziAhmed Ghazi View All Author Informationhttps://doi.org/10.1097/01.JU.0001008720.96896.83.04AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookLinked InTwitterEmail Abstract INTRODUCTION AND OBJECTIVE: Single port (SP) robotics demonstrate comparable outcomes to multi-port (MP) platforms, however safe adoption is hindered by the need to acquire new platform-specific skills. Despite growing interest in SP, there is no current standardized training curriculum. Our objective was to develop an adaptable platform with customized hydrogel surgical tasks, specific to SP robotics, based on Delphi Expert Consensus. METHODS: 22 SP experts participated in a three-phase modified Delphi consensus building approach. 250 questions were included in 3 categories: overall utility of the curriculum, components of a simulation-based SP curriculum, and assessment of surgical performance. Components reaching a content validity index≥0.80 were used to design a prototype training dome with standardized training models derived from Delphi results. Pilot testing was done by expert instructors at a SP hands-on course using a 5-point Likert scale (n=8). RESULTS: 34% of the 250 questions reached consensus, with 63.5% of consensus questions reaching 100% agreement. Experts agreed: inherent differences exist between SP and MP leading to challenging skill transfer, current training does not address SP-specific needs, and patient safety necessitates a simulation curriculum derived from expert consensus using validated educational approaches. 11 basic and 7 advanced SP-specific skills were identified for the SP curriculum. Using 3D printing and Hydrogel casting, 6 tasks and 2 of 4 procedures were fabricated to address SP-specific skills and provide comprehensive procedural training (Figure 1). 100%, 87.5%, and 75% of experts agreed the tasks provide a realistic platform for SP training, address training of basic and advanced skills identified in the consensus, and repeated practice on these tasks would improve performance for SP surgery, respectively. All experts agreed that the developed extraperitoneal radical prostatectomy and retroperitoneal partial nephrectomy models realistically replicated all steps of the procedure to provide a suitable training platform prior to live surgery. CONCLUSIONS: We present a training platform for necessary skill acquisition using the SP platform, with specific simulation models addressing critical skill domains identified via expert consensus. Download PPT Source of Funding: None © 2024 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 211Issue 5SMay 2024Page: e693 Advertisement Copyright & Permissions© 2024 by American Urological Association Education and Research, Inc.Metrics Author Information Nathan A. Schuler More articles by this author Lauren Shepard More articles by this author Patrick Saba More articles by this author Sammy Elsamra More articles by this author Simone Crivellaro More articles by this author Jihad Kaouk More articles by this author Ahmed Ghazi More articles by this author Expand All Advertisement PDF downloadLoading ...

You have accessJournal of UrologyFemale Voiding Dysfunction/ Pelvic Floor Disorders/ Incontinence/ Neuro-Urology (V02)1 May 2024V02-01 DESIGN AND DEVELOPMENT OF A HIGH-FIDELITY HYDROGEL SIMULATION MODEL FOR ARTIFICIAL URINARY SPHINCTER PLACEMENT UTILIZING EXPERT CONSENSUS Patrick Saba, Lauren Shepard, Katherine T. Anderson, Nick Warner, Nima Baradaran, Cooper Benson, William R. Boysen, Benjamin N. Breyer, Lindsay Hampson, Ty T. Higuchi, Niels V. Johnsen, Joseph J. Pariser, Jay Simhan, Alex J. Vanni, Omar RaheeM, Dmitriy Nikolavsky, Edward J. Wright, Arthur L. Burnett, Andrew Cohen, and Ahmed Ghazi Patrick SabaPatrick Saba , Lauren ShepardLauren Shepard , Katherine T. AndersonKatherine T. Anderson , Nick WarnerNick Warner , Nima BaradaranNima Baradaran , Cooper BensonCooper Benson , William R. BoysenWilliam R. Boysen , Benjamin N. BreyerBenjamin N. Breyer , Lindsay HampsonLindsay Hampson , Ty T. HiguchiTy T. Higuchi , Niels V. JohnsenNiels V. Johnsen , Joseph J. PariserJoseph J. Pariser , Jay SimhanJay Simhan , Alex J. VanniAlex J. Vanni , Omar RaheeMOmar RaheeM , Dmitriy NikolavskyDmitriy Nikolavsky , Edward J. WrightEdward J. Wright , Arthur L. BurnettArthur L. Burnett , Andrew CohenAndrew Cohen , and Ahmed GhaziAhmed Ghazi View All Author Informationhttps://doi.org/10.1097/01.JU.0001008636.33664.3e.01AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookLinked InTwitterEmail Abstract INTRODUCTION AND OBJECTIVE: Artificial urinary sphincter (AUS) implantation is a safe and effective treatment for severe stress urinary incontinence. However, high rates of reoperation due to device erosion, infection, and mechanical failure, combined with limited amount of training outside of live surgery, creates the need to train the next generation of surgeons without patient risk. Utilizing the consensus-building Delphi process, we sought to create a high-fidelity, non-biohazardous, simulation model for AUS implantation. METHODS: 20 high volume AUS implanters were recruited to complete a Delphi process to reach expert consensus on the necessary parameters and design specifications for the simulation model. Consensus>80% was reached in 46% of 187 questions pertaining to procedural realism, anatomical realism, and educational effectiveness. Using previously validated 3D printing and hydrogel molding techniques, researchers fabricated a hydrogel model that incorporated all the expert determined aspects. Prototypes were sent to 9 of the experts who performed a skin-to-skin AUS implantation and a questionnaire evaluating the model and determining if the consensus defined steps were met. The questionnaire utilized a 5-point Likert scale where agreement=4/5, neutral=3, and disagreement=1/2. RESULTS: 100%, 89%, and 100% agreed the model procedurally replicates; all steps of the procedure, tissue texture/behavior, and anatomical relationships including perineal and urethral incision/exposure (100%), circumferential urethral dissection (89%), AUS device prep (89%), reservoir counter incision (67%), cuff measurement/placement (100%), tubing passage (89%), development of subdartos pouch (78%), pump placement (89%), fashioning of tubing (78%), skin closure (89%), and device cycling (79%). 89%, 89%, 78%, 89%, 78%, 89%, and 56% agreed it anatomically replicates the; perineum, urethra, fascia over urethra, corpora cavernosa/spongiosum, bulbospongiosus and pubic bone. 78%, 100%, 100%, 100%, and 89% agreed it offers; useful error feedback, a safe/non-biohazardous training platform, and is useful for teaching/practicing, improving technical skills and assessing trainees. CONCLUSIONS: We successfully created a high-fidelity, non-biohazardous, simulation model for AUS implantation utilizing expert consensus. The model replicated the entire implantation and was rated highly for procedural realism, anatomical realism, and educational effectiveness. Ultimately, this model can be used to improve current AUS training. Source of Funding: n/a © 2024 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 211Issue 5SMay 2024Page: e100 Advertisement Copyright & Permissions© 2024 by American Urological Association Education and Research, Inc.Metrics Author Information Patrick Saba More articles by this author Lauren Shepard More articles by this author Katherine T. Anderson More articles by this author Nick Warner More articles by this author Nima Baradaran More articles by this author Cooper Benson More articles by this author William R. Boysen More articles by this author Benjamin N. Breyer More articles by this author Lindsay Hampson More articles by this author Ty T. Higuchi More articles by this author Niels V. Johnsen More articles by this author Joseph J. Pariser More articles by this author Jay Simhan More articles by this author Alex J. Vanni More articles by this author Omar RaheeM More articles by this author Dmitriy Nikolavsky More articles by this author Edward J. Wright More articles by this author Arthur L. Burnett More articles by this author Andrew Cohen More articles by this author Ahmed Ghazi More articles by this author Expand All Advertisement PDF downloadLoading ...

You have accessJournal of UrologyCME1 Apr 2023PD30-01 USING MACHINE LEARNING TO CLASSIFY PROCEDURE-SPECIFIC SURGICAL EXPERIENCE BASED ON SURGICAL GESTURE RECOGNITION IN A RADICAL PROSTATECTOMY SIMULATION Nathan Schuler, Lauren Shepard, Tyler Holler, Patrick Saba, and Ahmed Ghazi Nathan SchulerNathan Schuler More articles by this author , Lauren ShepardLauren Shepard More articles by this author , Tyler HollerTyler Holler More articles by this author , Patrick SabaPatrick Saba More articles by this author , and Ahmed GhaziAhmed Ghazi More articles by this author View All Author Informationhttps://doi.org/10.1097/JU.0000000000003316.01AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookLinked InTwitterEmail Abstract INTRODUCTION AND OBJECTIVE: Effectiveness of nerve sparing robot assisted radical prostatectomy (NS-RARP) is mainly based on recovery of postoperative erectile function. Previously, robotic kinematic data differentiated surgical expertise in suturing tasks and predicted continence rates. Using a validated realistic NS-RARP simulation with embedded sensors measuring torque on neurovascular bundles (NVB), our objective was to apply machine learning algorithms to classify surgical experience based on pure surgical gesture data inputs. METHODS: 50 certified urologists with average robotic case volumes (RV) of 1206 (range 100 - >5000) completed surveys and a NS-RARP simulation, during which video and force sensor data were collected. Videos were annotated for ten approved individual surgical gestures (e.g., cut, traction etc.) and aggregated based on instrument used and combinations of multiple gestures. Total force, average force, and number of force peak events were calculated. With a total of 33 inputs, output included total/NS-RARP case volume and NVB force data. Data was fit to a Gaussian Mixture model, then separated based on gesture utilization patterns. Tukey’s HSD test was used to determine significance in inter-group comparisons of RV and force metrics. RESULTS: Participants were clustered into 3 groupings based on mean total RV: 14 Super-Users (SU) (2221), 14 High Volume (HV) (1017), and 5 Low Volume (LV) (110) urologists (Figure 1). Significant differences were found between SU and HV for both Robotic case (p=0.03) and RARP volumes (p=0.008). Gesture pattern comparisons showed significant differences between SU vs HV and HV v LV in 64% and 30% of gesture inputs respectively. Force sensor comparisons showed significant differences in Total Force and Force Peak events between SU vs LV (p=0.014; p=0.021) and HV vs LV (p=0.021; p=0.046) groups. No significant differences in forces between SU and HV were found. CONCLUSIONS: This machine learning algorithm successfully categorized surgical experience into caseload and force applied, based solely on gesture inputs within a realistic simulated NS-RARP task. Differences in gestures differentiated HV from SU urologists, alluding to potential for targeted areas of improvement in gesture patterns, even for HV surgeons. Source of Funding: None © 2023 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 209Issue Supplement 4April 2023Page: e830 Advertisement Copyright & Permissions© 2023 by American Urological Association Education and Research, Inc.MetricsAuthor Information Nathan Schuler More articles by this author Lauren Shepard More articles by this author Tyler Holler More articles by this author Patrick Saba More articles by this author Ahmed Ghazi More articles by this author Expand All Advertisement PDF downloadLoading ...

INTRODUCTION:Machine learning methods have emerged as objective tools to evaluate operative performance in urological procedures. Our objectives were to establish machine learning-based methods for predicting surgeon caseload for nerve-sparing robot-assisted radical prostatectomy using our validated hydrogel-based simulation platform and identify potential metrics of surgical expertise.METHODS:Video, robotic kinematics, and force sensor data were collected from 35 board-certified urologists at the 2022 AUA conference. Video was annotated for surgical gestures. Objective performance indicators were derived from robotic system kinematic data. Force metrics were calculated from hydrogel model integrated sensors. Data were fitted to 3 supervised machine learning models-logistic regression, support vector machine, and k-nearest neighbors-which were used to predict procedure-specific learning curve proficiency. Recursive feature elimination was used to optimize the best performing model.RESULTS:Logistic regression predicted caseload with the highest AUC score for 5/7 possible data combinations (force, 64%; objective performance indicators + gestures, 94%; objective performance indicators + force, 90%; gestures + force, 93%; objective performance indicators + gestures + force, 94%). Support vector machine predicted the highest AUC score for objective performance indicators (82%) and gestures (94%). Logistic regression with recursive feature elimination was the most effective model reaching 96% AUC in predicting case-specific experience. Most contributory features were identified across all model types.CONCLUSIONS:We have created a machine learning-based algorithm utilizing a novel combination of objective performance indicators, gesture analysis, and integrated force metrics to predict surgical experience, capable of discriminating between surgeons with low or high robot-assisted radical prostatectomy caseload with 96% AUC in a standardized, simulation-based environment.

You have accessJournal of UrologyCME1 Apr 2023V02-07 DEVELOPMENT AND VALIDATION OF A BENCHTOP SIMULATOR FOR ULTRASOUND GUIDED PERCUTANEOUS NEPHROLITHOTOMY TRAINING USING 3D PRINTING AND HYDROGEL MOLDING Lauren Shepard, Nathan Schuler, Aaron Saxton, Patrick Saba, Andrew Cook, Tyler Holler, Karen Stern, David Tzou, Helena Chang, Justin Ahn, Thomas Tailly, Thomas Chi, and Ahmed Ghazi Lauren ShepardLauren Shepard More articles by this author , Nathan SchulerNathan Schuler More articles by this author , Aaron SaxtonAaron Saxton More articles by this author , Patrick SabaPatrick Saba More articles by this author , Andrew CookAndrew Cook More articles by this author , Tyler HollerTyler Holler More articles by this author , Karen SternKaren Stern More articles by this author , David TzouDavid Tzou More articles by this author , Helena ChangHelena Chang More articles by this author , Justin AhnJustin Ahn More articles by this author , Thomas TaillyThomas Tailly More articles by this author , Thomas ChiThomas Chi More articles by this author , and Ahmed GhaziAhmed Ghazi More articles by this author View All Author Informationhttps://doi.org/10.1097/JU.0000000000003232.07AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookLinked InTwitterEmail Abstract INTRODUCTION AND OBJECTIVE: Ultrasound-guided percutaneous nephrolithotomy (US-PCNL) is an effective and safe approach for management of large renal stones, yet adoption has been limited. The vast majority of US-PCNL are performed in the prone position vs supine position (80% vs 20%); however, there is a need for a safe, realistic procedural training platform for both approaches. We have previously demonstrated the ability of similar hydrogel simulations to improve operative outcomes during fluoroscopic PCNL training. Our objective was the development of a benchtop, non-biohazardous US-PCNL simulator using 3D printing and hydrogel molding and its validation using educational theory. METHODS: Consensus among 12 experts was reached regarding the essential aspects of an ideal US-PCNL model and an associated evaluation checklist using a Delphi consensus methodology. Segmentation software was used to generate a 3D model from an approved patient computed tomography (CT) scan, including kidney, pelvicalyceal system, stone, spine and ribs, abdominal wall, and iliac crest. Post-processing generated 3D printed casts into which hydrogel formulations replicating various anatomical and tissue mechanical properties of the structures were created according to the consensus statement. A prototype was fabricated for expert approval, after which 20 experts and 28 novices performed US-PCNL with performance assessed using the developed checklist. RESULTS: The simulator fulfilled all criteria established in the consensus statement, including external and ultrasound appearance mimicking in vivo appearance, a watertight pelvicalyceal system containing a functional stone that is distensible with retrograde instillation, and realistic tactile feedback during puncture. Experts agreed the simulator provides a safe training alternative (100%), bridges gaps between classroom and clinic (95.7%), and allows trainee performance evaluation (100%) in a risk free environment that can be modified for variable anatomy (88.9%). Highly significant differences were found between expert and novices using the checklist developed (93.4% vs 42.3%, p<0.001). In addition, novice performance improved with repeated practice (p<.001). A similar process was utilized to develop a supine version for US-PCNL simulation. CONCLUSIONS: We have successfully developed and validated a high-fidelity benchtop simulator for both Prone and Supine US-PCNL that can be easily tailored to varying anatomy. Further studies evaluating the transfer of skill are still required. Source of Funding: None © 2023 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 209Issue Supplement 4April 2023Page: e170 Advertisement Copyright & Permissions© 2023 by American Urological Association Education and Research, Inc.MetricsAuthor Information Lauren Shepard More articles by this author Nathan Schuler More articles by this author Aaron Saxton More articles by this author Patrick Saba More articles by this author Andrew Cook More articles by this author Tyler Holler More articles by this author Karen Stern More articles by this author David Tzou More articles by this author Helena Chang More articles by this author Justin Ahn More articles by this author Thomas Tailly More articles by this author Thomas Chi More articles by this author Ahmed Ghazi More articles by this author Expand All Advertisement PDF downloadLoading ...

Introduction and Objective: With introduction of the da Vinci single-port (SP) system, we evaluated which multiport (MP) robotic skills are naturally transferable to the SP platform. Methods: Three groups of urologists: Group 1 (5 inexperienced in MP and SP), Group 2 (5 experienced in MP without SP experience), and Group 3 (2 experienced in both MP and SP) were recruited to complete a validated urethrovesical anastomosis simulation using MP followed by SP robots. Performance was graded using both GEARS and RACE scales. Subjective cognitive load measurements (Surg-TLX and difficulty ratings [/20] of instrument collisions camera and EndoWrist movement) were collected. Results: GEARS and RACE scores for Groups 1 and 3 were maintained on switching from MP to SP (Group 3 scored significantly higher on both systems). Surg-TLX and difficulty scores were also maintained for both groups on switching from MP and SP except for a significant increase in SP camera movement (+7.2, p = 0.03) in Group 1 compared to Group 3 that maintained low scores on both. Group 2 demonstrated significant lower GEARS (-2.9, p = 0.047) and RACE (-5.1, p = 0.011) scores on SP vs MP. On subanalysis, GEARS subscores for force sensitivity and robotic control (-0.7, p = 0.04; -0.9, p = 0.02) and RACE subscores for needle entry, needle driving, and tissue approximation (-0.9, p = 0.01; -1.0, p = 0.02; -1.0, p < 0.01) significantly decreased. GEARS (depth perception, bimanual dexterity, and efficiency) and RACE subscores (needle positioning and suture placement) were maintained. All participants scored significantly lower in knot tying on the SP robot (-1.0, p = 0.03; -1.2, p = 0.02, respectively). Group 2 reported higher Surg-TLX (+13 pts, p = 0.015) and difficulty ratings on SP vs MP (+11.8, p < 0.01; +13.6, p < 0.01; +14 pts, p < 0.01). Conclusions: The partial skill transference across robots raises the question regarding SP-specific training for urologists proficient in MP. Novices maintained difficulty scores and cognitive load across platforms, suggesting that concurrent SP and MP training may be preferred.

You have accessJournal of UrologyCME1 Apr 2023PD01-05 MULTICENTER VALIDATION OF A CONSENSUS-BASED HYDROGEL SIMULATOR FOR ULTRASOUND GUIDED PERCUTANEOUS NEPHROLITHOTOMY USING MODERN EDUCATION THEORY Lauren Shepard, Nathan Schuler, Aaron Saxton, Patrick Saba, Andrew Cook, Tyler Holler, David Tzou, Karen Stern, Helena Chang, Justin Ahn, Thomas Tailly, Thomas Chi, and Ahmed Ghazi Lauren ShepardLauren Shepard More articles by this author , Nathan SchulerNathan Schuler More articles by this author , Aaron SaxtonAaron Saxton More articles by this author , Patrick SabaPatrick Saba More articles by this author , Andrew CookAndrew Cook More articles by this author , Tyler HollerTyler Holler More articles by this author , David TzouDavid Tzou More articles by this author , Karen SternKaren Stern More articles by this author , Helena ChangHelena Chang More articles by this author , Justin AhnJustin Ahn More articles by this author , Thomas TaillyThomas Tailly More articles by this author , Thomas ChiThomas Chi More articles by this author , and Ahmed GhaziAhmed Ghazi More articles by this author View All Author Informationhttps://doi.org/10.1097/JU.0000000000003218.05AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookLinked InTwitterEmail Abstract INTRODUCTION AND OBJECTIVE: Ultrasound-guided approaches for percutaneous nephrolithotomy (US-PCNL) offer several advantages, including significant reduction in radiation exposure; however, they have not been readily adapted. Some of the contributing factors include the lack of a standard training platform and curriculum that has been uniformly approved by experts. As such, there is a current need for a realistic simulator. Herein, we used a consensus-based educational approach for development and multicenter validation of a high-fidelity non-biohazardous PCNL simulator. METHODS: Consensus was reached on a high-fidelity PCNL simulator with 12 international experts using a Delphi methodology over three rounds. The 284 questions were categorized into overall utility, anatomical components, tissue fidelity, and assessment of surgical performance. A hydrogel prototype replicating mechanical properties was developed for experts to evaluate prior to its validation by comparing performances of 20 experts and 28 novices from 5 centers as well as evaluating the models ability to improve novice performance with repeated practice. RESULTS: Consensus (>80% agreement) was reached in 31.3% of questions, within which 65% achieved 100% consensus. The prototype prone PCNL simulator included anatomical landmarks (11th and 12th rib, iliac crest), realistic external and ultrasound appearance with appropriate tactile properties, and a water tight distensible pelvicalyceal system with a stone for laser lithotripsy and retrograde ureteroscopy (Figure 1A). A weighted evaluation checklist was also developed via consensus. Experts agreed that >89.2% of prototype and checklist components conformed to the consensus statement. Novices and experts were graded for US-guided lower pole access, with statistically significant differences for checklist score (42.3±19.0% vs 93.4±4.6%, p<0.001). Furthermore, novices significantly improved both lower pole and upper pole access score (p<0.01, p<0.001) respectively with repeated (x5) training sessions (Figure 1B). CONCLUSIONS: This non-biohazardous benchtop simulator for US-PCNL developed using expert consensus and validated via an educational approach at multiple centers can provide safe, realistic training in a risk-free environment. Source of Funding: None © 2023 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 209Issue Supplement 4April 2023Page: e64 Advertisement Copyright & Permissions© 2023 by American Urological Association Education and Research, Inc.MetricsAuthor Information Lauren Shepard More articles by this author Nathan Schuler More articles by this author Aaron Saxton More articles by this author Patrick Saba More articles by this author Andrew Cook More articles by this author Tyler Holler More articles by this author David Tzou More articles by this author Karen Stern More articles by this author Helena Chang More articles by this author Justin Ahn More articles by this author Thomas Tailly More articles by this author Thomas Chi More articles by this author Ahmed Ghazi More articles by this author Expand All Advertisement PDF downloadLoading ...

Robot-assisted partial nephrectomy (RAPN) is becoming the standard treatment for small renal masses. However, the availability of realistic validated, non-biohazardous procedural platform for training are lacking for both generic and patient-specific training. The multi-institutional validation of a high-fidelity, perfused, inanimate, simulation platform for RAPN utilizing incorporated clinically relevant objective metrics of simulation (CROMS) applying modern validity standards. This concept was further developed into patient specific models for surgical rehearsals by converting patients axial imaging using image segmentation, mechanical testing of hydrogel components to realistically replicate the properties of live tissue and anatomical verification of models to patients' original scans. Utilizing a combination of 3D printing and hydrogel casting a RAPN model was developed from a patient's C.T. scan with a 4.2 cm, upper-pole renal tumor (RENAL nephrometry score 7x). 3D-printed casts designed from the patient's imaging were utilized to fabricate and register hydrogel (Polyvinyl alcohol) components of the kidney including vascular and pelvicalyceal systems. Following mechanical and anatomical verification of the kidney phantom, it was surrounded by other relevant hydrogel organs and placed in a laparoscopic trainer. 27 novice and 16 expert urologists categorized according to caseload, from 5 academic institutions completed the simulation. Mechanical and functional testing protocols were completed to confirm that the properties of PVA matched the live tissue.5 Anatomical accuracy was confirmed by CT scanning the phantom and creating another CAD, which was compared to the original patient CAD. Full-procedural PS rehearsals were completed 24-48 hours prior to their respective live surgeries. Clinically relevant metrics (warm ischemia time, estimated blood loss, and positive surgical margins) from each rehearsal and live case were compared using a Wilcoxon-rank sum test. Expert ratings demonstrated model's superiority to other procedural simulations in replicating procedural steps, bleeding, tissue texture, and appearance. Significant difference between groups was demonstrated in CROMS [console time (p < 0.001), warm ischemia time (p < 0.001), estimated blood loss (p < 0.001)] and GEARS (p < 0.001). Six major intraoperative complications occurred only in novice simulations. GEARS scores highly correlated with the CROMS. The 7%-3freeze/thaw PVA best recreated the mechanical and functional properties of porcine kid- neys, while anatomical verification showed ≤1 mm deviation of the kidney and tumor from the patient anatomy and ≤ 3 mm for the hilar structures. PS rehearsal platforms were fabricated using these methods for 8 patients (average tumor size 5.92 cm and nephrometry score 9.8). A positive correlation was found for warm ischemia time and estimated blood loss between rehearsals and live surgeries. This perfused, procedural model offers an unprecedented realistic simulation platform which incorporates objective, clinically relevant, and procedure-specific performance metrics. Furthermore, this reproducible method shows high anatomical accuracy, realistic tissue properties, and translational effects between rehearsals and live surgery.

BACKGROUND:Penile prosthesis implantation offers a durable, safe, and effective treatment option for male erectile dysfunction; however, many urologists feel apprehensive and uncomfortable placing penile prostheses due to limited training, low surgical experience, and intra- and postoperative complication management.AIM:To compare a previously validated hydrogel inflatable penile prosthesis (IPP) training model with cadaver simulations across 4 main categories: anatomic replication and realism, procedural replication and realism, educational effectiveness, and efficacy and safety.METHODS:An overall 88 participants (15 attendings, 18 fellows, and 55 residents) performed guided IPP placements on a cadaver and a hydrogel model. Based on a 5-point Likert scale, postsurveys were used to assess the participants' opinions regarding anatomic replication and realism, procedural replication and realism, educational effectiveness, and safety between the hydrogel model and cadavers.OUTCOMES:A direct head-to-head scenario was created, allowing participants to fully utilize the hydrogel model and cadaver, which ensured the most accurate comparison possible.RESULTS:A total of 84% agreed that the hydrogel model replicates the relevant human cadaveric anatomy for the procedure, whereas 69% agreed that the hydrogel tissue resembles the appearance of cadaveric tissue. Regarding the pubic bone, outer skin, corporal bodies, dartos layer, and scrotum, 79%, 74%, 82%, 46%, and 30% respectively agreed that the hydrogel tissue resembled the texture/behavior of cadavers. Furthermore, 66% of participants agreed that the hydrogel model replicates all the procedural steps. Specifically, participants agreed that the model replicates the skin incision/dartos dissection (74%), placement of stay suture and corporotomy (92%), corporal dilation (81%), measurement of prosthetic size (98%), reservoir placement (43%), IPP placement (91%), scrotal pump placement (48%), and skin closure (51%). Finally, 86%, 93%, and 78% agreed that the hydrogel model is useful for improving technical skills, as a teaching/practicing tool, and as an evaluation tool, respectively. To conclude, 81% of participants stated that they would include the hydrogel model platform in their current training.CLINICAL IMPLICATIONS:By replicating the IPP procedure, the hydrogel model offers an additional high-fidelity training opportunity for urologists, allowing them to improve their skills and confidence in placing penile prostheses, with the goal of improving patient surgical outcomes.STRENGTHS AND LIMITATIONS:The hydrogel training model allows users to perform the entire IPP placement procedure with high anatomic realism and educational effectiveness, maintaining many of the high-fidelity benefits seen in cadavers while improving safety and accessibility.CONCLUSION:Ultimately, this high-fidelity nonbiohazardous training model can be used to supplement and bolster current IPP training curriculums.

Introduction and ObjectiveToday's educational landscape continues to evolve due to a technology infrastructure that enables increased accessibility and efficiency for students and educators. In an era when reduced opportunity for traditional teacher- student (“face-to-face”) learning is juxtaposed with the exponential growth of internet-based education (“e- Learning”), modern surgical educators are increasingly discovering ways to further utilize remote training platforms for advanced surgical training [1], [2], [3], [4]. While virtual reality is an artificial environment that is created with software and presented to the user in such a way that the user suspends belief and accepts it as a real environment, Augmented Reality (AR) technology superimposes a computer-generated image on a user's view of the real world, thus providing a composite view. Mixed reality (MR) technologies is a form of AR that allows for the fusion of two video streams for real time overlay of a remote instructors’ hands onto the trainee's view. Our objective is to examine the utility and feasibility of remote proctoring for Inflatable Penile Prothesis (IPP) surgical skills training using a previously validated full-procedural non-biohazardous hydrogel simulation model fabricated using 3D printing and hydrogel casting [5] when combined with Mixed Reality technologies.Methods9 urology residents at the University of Rochester (PGY 1–4) were paired and remotely proctored by an expert at Boston University. During inflatable penile prothesis training sessions, participants and proctor were given a model, with a full surgical setup. Pre-learning included a narrated full-procedural demonstration by the proctor followed by a full procedure IPP simulation guided by proctor feedback. The trainees were wearing a Vuzix M4000 smart glasses (Rochester, NY) that house an ultra-bright see-through display utilizing waveguide optics to project a remote instructors’ hands onto the trainee's view through a MR technology application. Pre- and post-training surveys assessed confidence (0–100) and procedural knowledge (15 questions). Opinions on virtual learning and its application to this training session were collected.Results66.7% residents had not performed a prior live IPP placement, while the remaining had completed a median (IQR) of 6 (4.5–8) cases. All measures of confidence and knowledge significantly increased after remote session (Table 1). Scores of the knowledge assessment increased by 13% [±7–18, p = 0.04] following the remote session which was reflected in a 48% [±22–46, p=<0.001], 22% [±9–27, p=<0.05] and 18% [±12–31, p = 0.005] increase in participants confidence in the ability to perform a simulated IPP procedure, knowledge of IPP procedural steps and knowledge of applied anatomy respectively. 77.8% (7/9) of residents had never experienced hands-on remote training due to the limited number of opportunities. All residents (100%) found the remote training session valuable and beneficial for training IPP skills as well as learning steps of the procedure. The residents highly rated the ability to practice complex skills with zero-patient harm (88.9%, 8/9), the non-biohazardous nature of the model (66.7%, 6/9), and having their own hydrogel training model (88.9%, 8/9). 66.7%, preferred a hybrid (virtual combined with in-person learning) for future sessions. Limitations include the low sample participants.ConclusionsRemote proctoring using a MR technologies and non-biohazardous IPP simulation model is feasible with improvement in both confidence and procedural knowledge thus providing its utility. This approach has the potential to provide opportunities for hands-on distance training with remote experts in a safe environment.

You have accessJournal of UrologyCME1 May 2022MP10-10 HEAD-TO-HEAD COMPARISON OF HYDROGEL MODELS TO CADAVERS FOR INFLATABLE PENILE PROSTHESIS (IPP) TRAINING Patrick Saba, Rachel Melnyk, Michael Witthaus, Christopher Wanderling, Tyler Holler, and Ahmed Ghazi Patrick SabaPatrick Saba More articles by this author , Rachel MelnykRachel Melnyk More articles by this author , Michael WitthausMichael Witthaus More articles by this author , Christopher WanderlingChristopher Wanderling More articles by this author , Tyler HollerTyler Holler More articles by this author , and Ahmed GhaziAhmed Ghazi More articles by this author View All Author Informationhttps://doi.org/10.1097/JU.0000000000002532.10AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookLinked InTwitterEmail Abstract INTRODUCTION AND OBJECTIVE: Cadavers have long been the standard for inflatable penile prosthetic (IPP) placement training. However limited availability, biohazard risk and specialized facilities hinders their widespread use. A previously validated high-fidelity, non-biohazardous, hydrogel procedural model for IPP placement was developed to address this need. We aim to evaluate if hydrogel models can adequately perform as an equivalent training tool for IPP placement to supplement standard cadaveric simulations. METHODS: 73 participants (12 experts, 13 fellows, 48 residents) performed guided IPP simulations on a cadaver followed by a hydrogel model (Figure 1). Surveys were completed to compare tissue properties, anatomical and procedural resemblance, and educational effectiveness between the two modalities on a 5-point Likert scale. RESULTS: 86% of participants agreed the hydrogel training model replicated relevant human anatomy. 86% agreed that the hydrogel tissues resembled the appearance of cadaveric tissues with participants also agreeing the dartos (92%), scrotum (90%), pubic bone (88%), skin (86%), and corpora (71%) resembled the texture/behavior of a cadaver. 96%, 94%, 93%, 89%, 89%, 86% agreed that the model replicated procedural steps in cadavers, regarding prosthesis measurement and placement, stay suture placement, tissue closure, corporal dilation, and skin/dartos incision respectively. 100% of experts and fellows agreed the model is useful for: improving technical skills, teaching the procedure, and assessing procedural ability vs 94%, 96%, and 88% of residents respectively. 75% of experts believed that the hydrogel model was equal if not better than cadavers at these respective categories. 83% of participants agreed the model was safer than cadavers and 75% agreed the model was equal if not better than cadavers as an educational tool. 82% (75%, 92%, 81% of experts, fellows, residents, respectively) stated they wish to include the models alongside current cadaver training with 1/4 of experts stating they preferred the models over cadavers. CONCLUSIONS: This procedural simulation allows the practice of each step of IPP placement with equivalent fidelity to standard cadaveric simulations. Its non-biohazardous nature provides a safe and effective educational equivalent for IPP surgical training. Source of Funding: Coloplast Research Grant © 2022 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 207Issue Supplement 5May 2022Page: e150 Advertisement Copyright & Permissions© 2022 by American Urological Association Education and Research, Inc.MetricsAuthor Information Patrick Saba More articles by this author Rachel Melnyk More articles by this author Michael Witthaus More articles by this author Christopher Wanderling More articles by this author Tyler Holler More articles by this author Ahmed Ghazi More articles by this author Expand All Advertisement PDF downloadLoading ...