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BackgroundArtificial intelligence (AI) has the potential to enhance patient safety in surgery, and all its aspects, including education and training, will derive considerable benefit from AI. In the present study, deep-learning models were used to predict the rates of proficiency acquisition in robot-assisted surgery (RAS), thereby providing surgical programs directors information on the levels of the innate ability of trainees to facilitate the implementation of flexible personalized training.Methods176 medical students, without prior experience with surgical simulators, were trained to reach proficiency in five tasks on a virtual simulator for RAS. Ensemble deep neural networks (DNN) models were developed and compared with other ensemble AI algorithms, i.e., random forests and gradient boosted regression trees (GBRT).ResultsDNN models achieved a higher accuracy than random forests and GBRT in predicting time to proficiency, 0.84 vs. 0.70 and 0.77, respectively (Peg board 2), 0.83 vs. 0.79 and 0.78 (Ring walk 2), 0.81 vs 0.81 and 0.80 (Match board 1), 0.79 vs. 0.75 and 0.71 (Ring and rail 2), and 0.87 vs. 0.86 and 0.84 (Thread the rings 2). Ensemble DNN models outperformed random forests and GBRT in predicting number of attempts to proficiency, with an accuracy of 0.87 vs. 0.86 and 0.83, respectively (Peg board 2), 0.89 vs. 0.88 and 0.89 (Ring walk 2), 0.91 vs. 0.89 and 0.89 (Match board 1), 0.89 vs. 0.87 and 0.83 (Ring and rail 2), and 0.96 vs. 0.94 and 0.94 (Thread the rings 2).ConclusionsEnsemble DNN models can identify at an early stage the acquisition rates of surgical technical proficiency of trainees and identify those struggling to reach the required expected proficiency level.

As the adoption of robotic-assisted procedures expands across various surgical specialties, colorectal surgery stands out as a prominent beneficiary. This rise in usage can be traced back to the increased accessibility of robotic platforms and a growing institutional shift towards cutting-edge surgical methods. When compared with traditional laparoscopic methods, robotic techniques offer distinct advantages. Their true potential shines in surgeries involving complex anatomical regions, where the robot's enhanced dexterity and range of motion prove invaluable. The three-dimensional, magnified view provided by robotic systems further boosts surgical precision and clarity. These advantages render robotic assistance especially suitable for colorectal surgeries, notably in intricate areas such as the rectum and endoluminal spaces. As the medical world emphasizes minimally invasive surgical methods, there's a pressing need to evolve and optimize robotic techniques in colorectal surgery. This article traces the evolution of robotic interventions in colorectal surgeries, highlighting both its historical milestones and anticipated future trends. We'll also explore emerging robotic tools and systems set to reshape the colorectal surgical arena. •Robotic surgery improves precision and dexterity, mainly useful for CR procedures•Short-term outcomes are improving over time: ↓operative time, LOS, conversion rate•Oncologic outcomes are similar in all approaches, the advantage not being confirmed•Future: haptic feedback, visual enhancements, artificial intelligence

BackgroundDespite there being a considerable amount of published studies on robotic colorectal surgery (RCS) over the last few years, there is a lack of evidence regarding RCS training pathways. This study examines the short-term clinical outcomes of an international RCS training programme (the European Academy of Robotic Colorectal Surgery—EARCS).MethodsConsecutive cases from 26 European colorectal units who conducted RCS between 2014 and 2018 were included in this study. The baseline characteristics and short-term outcomes of cases performed by EARCS delegates during training were analysed and compared with cases performed by EARCS graduates and proctors.ResultsData from 1130 RCS procedures were collected and classified into three cohort groups (323 training, 626 graduates and 181 proctors). The training cases conversion rate was 2.2% and R1 resection rate was 1.5%. The three groups were similar in terms of baseline characteristics with the exception of malignant cases and rectal resections performed. With the exception of operative time, blood loss and hospital stay (training vs. graduate vs. proctor: operative time 302, 265, 255 min, p < 0.001; blood loss 50, 50, 30 ml, p < 0.001; hospital stay 7, 6, 6 days, p = 0.003), all remaining short-term outcomes (conversion, 30-day reoperation, 30-day readmission, 30-day mortality, clinical anastomotic leak, complications, R1 resection and lymph node yield) were comparable between the three groups.ConclusionsColorectal surgeons learning how to perform RCS under the EARCS-structured training pathway can safely achieve short-term clinical outcomes comparable to their trainers and overcome the learning process in a way that minimises patient harm.

There is a lack of training curricula and educational concepts for robotic-assisted surgery (RAS). It remains unclear how surgical residents can be trained in this new technology and how robotics can be integrated into surgical residency training. The conception of a training curriculum for RAS addressing surgical residents resulted in a three-step training curriculum including multimodal learning contents: basics and simulation training of RAS ( step 1 ), laboratory training on the institutional robotic system ( step 2 ) and structured on-patient training in the operating room ( step 3 ). For all three steps, learning content and video tutorials are provided via cloud-based access to allow self-contained training of the trainees. A prospective multicentric validation study was conducted including seven surgical residents. Transferability of acquired skills to a RAS procedure were analyzed using the GEARS score. All participants successfully completed RoSTraC within 1 year. Transferability of acquired RAS skills could be demonstrated using a RAS gastroenterostomy on a synthetic biological organ model. GEARS scores concerning this procedure improved significantly after completion of RoSTraC (17.1 (±5.8) vs. 23.1 (±4.9), p  < 0.001). In step 3 of RoSTraC, all participants performed a median of 12 (range 5–21) RAS procedures on the console in the operation room. RoSTraC provides a highly standardized and comprehensive training curriculum for RAS for surgical residents. We could demonstrate that participating surgical residents acquired fundamental and advanced RAS skills. Finally, we could confirm that all surgical residents were successfully and safely embedded into the local RAS team.

IntroductionThe variety of robotic surgery systems, training modalities, and assessment tools within robotic surgery training is extensive. This systematic review aimed to comprehensively overview different training modalities and assessment methods for teaching and assessing surgical skills in robotic surgery, with a specific focus on comparing objective and subjective assessment methods.MethodsA systematic review was conducted following the PRISMA guidelines. The electronic databases Pubmed, EMBASE, and Cochrane were searched from inception until February 1, 2022. Included studies consisted of robotic-assisted surgery training (e.g., box training, virtual reality training, cadaver training and animal tissue training) with an assessment method (objective or subjective), such as assessment forms, virtual reality scores, peer-to-peer feedback or time recording.ResultsThe search identified 1591 studies. After abstract screening and full-texts examination, 209 studies were identified that focused on robotic surgery training and included an assessment tool. The majority of the studies utilized the da Vinci Surgical System, with dry lab training being the most common approach, followed by the da Vinci Surgical Skills Simulator. The most frequently used assessment methods included simulator scoring system (e.g., dVSS score), and assessment forms (e.g., GEARS and OSATS).ConclusionThis systematic review provides an overview of training modalities and assessment methods in robotic-assisted surgery. Dry lab training on the da Vinci Surgical System and training on the da Vinci Skills Simulator are the predominant approaches. However, focused training on tissue handling, manipulation, and force interaction is lacking, despite the absence of haptic feedback. Future research should focus on developing universal objective assessment and feedback methods to address these limitations as the field continues to evolve.

PurposeLaparoscopic ileal pouch-anal anastomosis (IPAA) surgery offers improved short-term outcomes over open surgery but can be technically challenging. Robotic surgery has been increasingly used for IPAA surgery, but there is limited evidence supporting its use. This study aims to compare the short-term outcomes of laparoscopic and robotic IPAA procedures.MethodsAll consecutive patients receiving laparoscopic and robotic IPAA surgery at 3 centres, from 3 countries, between 2008 and 2019 were identified from prospectively collated databases. Robotic surgery patients were propensity score matched with laparoscopic patients for gender, previous abdominal surgery, ASA grade (I, II vs III, IV) and procedure performed (proctocolectomy vs completion proctectomy). Their short-term outcomes were examined.ResultsA total of 89 patients were identified (73 laparoscopic, 16 robotic). The 16 patients that received robotic surgery were matched with 15 laparoscopic patients. Baseline characteristics were similar between the two groups. There were no statistically significant differences in any of the investigated short-term outcomes. Length of stay trend was higher for laparoscopic surgery (9 vs 7 days, p = 0.072)ConclusionRobotic IPAA surgery is safe and feasible and offers similar short-term outcomes to laparoscopic surgery. Length of stay may be lower for robotic IPAA surgery, but further larger scale studies are required in order to demonstrate this.

The gold standard treatment for non-metastatic upper tract urothelial cancer (UTUC) is represented by radical nephroureterectomy (RNU). The choice of surgical technique in performing UTUC surgery continues to depend on several factors, including the location and extent of the tumor, the patient’s overall health, and very importantly, the surgeon’s skill, experience, and preference. Although open and laparoscopic approaches are well-established treatments, evidence regarding robot-assisted radical nephroureterectomy (RANU) is growing. Aim of our study was to perform a critical review on the evidence of the last 5 years regarding surgical techniques and outcomes of minimally invasive RNU, mostly focusing on RANU. Reported oncological and function outcomes suggest that minimally invasive RNU is safe and effective, showing similar survival rates compared to the open approach.

The evolution of medical technologies—such as surgical devices and imaging techniques—has transformed all aspects of surgery. A key area of development is robot-assisted minimally invasive surgery (MIS). This review paper provides an overview of the evolution of robotic MIS, from its infancy to our days, and envisioned future challenges. It provides an outlook of breakthrough surgical robotic platforms, their clinical applications, and their evolution over the years. It discusses how the integration of robotic, imaging, and sensing technologies has contributed to create novel surgical platforms that can provide the surgeons with enhanced dexterity, precision, and surgical navigation while reducing the invasiveness and efficacy of the intervention. Finally, this review provides an outlook on the future of robotic MIS discussing opportunities and challenges that the scientific community will have to address in the coming decade. We hope that this review serves to provide a quick and accessible way to introduce the readers to this exciting and fast-evolving area of research, and to inspire future research in this field.

Objective To compare the safety and accuracy of cortical bone trajectory screw placement between the robot-assisted and fluoroscopy-assisted approaches. Methods This retrospective study was conducted between November 2018 and June 2020, including 81 patients who underwent cortical bone trajectory (CBT) surgery for degenerative lumbar spine disease. CBT was performed by the same team of experienced surgeons. The patients were randomly divided into two groups—the fluoroscopy-assisted group (FA, 44 patients) and the robot-assisted group (RA, 37 patients). Robots for orthopedic surgery were used in the robot-assisted group , whereas conventional fluoroscopy-guided screw placement was used in the fluoroscopy-assisted group. The accuracy of screw placement and rate of superior facet joint violation were assessed using postoperative computed tomography (CT). The time of single screw placement, intraoperative blood loss, and radiation exposure to the surgical team were also recorded. The χ 2 test and Student’s t-test were used to analyze the significance of the variables ( P  < 0.05). Results A total of 376 screws were inserted in 81 patients, including 172 screws in the robot-assisted group and 204 pedicle screws in the fluoroscopy-assisted group. Screw placement accuracy was higher in the RA group (160, 93%) than in the FA group (169, 83%) ( P  = 0.003). The RA group had a lower violation of the superior facet joint than the FA group. The number of screws reaching grade 0 in the RA group (58, 78%) was more than that in the FA group (56, 64%) ( P  = 0.041). Screw placement time was longer in the FA group (7.25 ± 0.84 min) than in the RA group (5.58 ± 1.22 min, P  < 0.001). The FA group had more intraoperative bleeding (273.41 ± 118.20 ml) than the RA group (248.65 ± 97.53 ml, P  = 0.313). The radiation time of the FA group (0.43 ± 0.07 min) was longer than the RA group (0.37 ± 0.10 min, P  = 0.001). Furthermore, the overall learning curve tended to decrease. Conclusions Robot-assisted screw placement improves screw placement accuracy, shortens screw placement time, effectively improves surgical safety and efficiency, and reduces radiation exposure to the surgical team. In addition, the learning curve of robot-assisted screw placement is smooth and easy to operate.

PurposeThe aim of the present study is to investigate the impact of the near-infrared (NIRF) technology with indocyanine green (ICG) in robotic urologic surgery by performing a systematic literature review and to provide evidence-based expert recommendations on best practices in this field.MethodsAll English language publications on NIRF/ICG-guided robotic urologic procedures were evaluated. We followed the PRISMA (Preferred Reporting Items for Systematic Review and Meta-Analyses) statement to evaluate PubMed®, Scopus® and Web of Science™ databases (up to April 2019). Experts in the field provided detailed pictures and intraoperative video-clips of different NIRF/ICG-guided robotic surgeries with recommendations for each procedure. A unique QRcode was generated and linked to each underlying video-clip. This new exclusive feature makes the present the first “dynamic paper” that merges text and figure description with their own video providing readers an innovative, immersive, high-quality and user-friendly experience.ResultsOur electronic search identified a total of 576 papers. Of these, 36 studies included in the present systematic review reporting the use of NIRF/ICG in robotic partial nephrectomy (n = 13), robotic radical prostatectomy and lymphadenectomy (n = 7), robotic ureteral re-implantation and reconstruction (n = 5), robotic adrenalectomy (n = 4), robotic radical cystectomy (n = 3), penectomy and robotic inguinal lymphadenectomy (n = 2), robotic simple prostatectomy (n = 1), robotic kidney transplantation (n = 1) and robotic sacrocolpopexy (n = 1).ConclusionNIRF/ICG technology has now emerged as a safe, feasible and useful tool that may facilitate urologic robotic surgery. It has been shown to improve the identification of key anatomical landmarks and pathological structures for oncological and non-oncological procedures. Level of evidence is predominantly low. Larger series with longer follow-up are needed, especially in assessing the quality of the nodal dissection and the feasibility of the identification of sentinel nodes and the impact of these novel technologies on long-term oncological and functional outcomes.