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IntroductionRobot-assisted surgery is becoming increasingly adopted by multiple surgical specialties. There is evidence of inherent risks of utilising new technologies that are unfamiliar early in the learning curve. The development of standardised and validated training programmes is crucial to deliver safe introduction. In this review, we aim to evaluate the current evidence and opportunities to integrate novel technologies into modern digitalised robotic training curricula.MethodsA systematic literature review of the current evidence for novel technologies in surgical training was conducted online and relevant publications and information were identified. Evaluation was made on how these technologies could further enable digitalisation of training.ResultsOverall, the quality of available studies was found to be low with current available evidence consisting largely of expert opinion, consensus statements and small qualitative studies. The review identified that there are several novel technologies already being utilised in robotic surgery training. There is also a trend towards standardised validated robotic training curricula. Currently, the majority of the validated curricula do not incorporate novel technologies and training is delivered with more traditional methods that includes centralisation of training services with wet laboratories that have access to cadavers and dedicated training robots.ConclusionsImprovements to training standards and understanding performance data have good potential to significantly lower complications in patients. Digitalisation automates data collection and brings data together for analysis. Machine learning has potential to develop automated performance feedback for trainees. Digitalised training aims to build on the current gold standards and to further improve the ‘continuum of training’ by integrating PBP training, 3D-printed models, telementoring, telemetry and machine learning.

The global numbers of robotic gastrointestinal surgeries are increasing. However, the evidence base for robotic gastrointestinal surgery does not yet support its widespread adoption or justify its cost. The reasons for its continued popularity are complex, but a notable driver is the push for innovation — robotic surgery is seen as a compelling solution for delivering on the promise of minimally invasive precision surgery — and a changing commercial landscape delivers the promise of increased affordability. Novel systems will leverage the robot as a data-driven platform, integrating advances in imaging, artificial intelligence and machine learning for decision support. However, if this vision is to be realized, lessons must be heeded from current clinical trials and translational strategies, which have failed to demonstrate patient benefit. In this Perspective, we critically appraise current research to define the principles on which the next generation of gastrointestinal robotics trials should be based. We also discuss the emerging commercial landscape and define existing and new technologies.The evidence base for robotic gastrointestinal surgery does not yet support its widespread adoption. Here, Kinross et al. discuss this evidence base and the principles on which future gastrointestinal surgical trials should be based, as well as emerging technologies.

Background Robotic-assisted surgery is used with increasing frequency in general surgery for a variety of applications. In spite of this increase in usage, the learning curve is not yet defined. This study reviews the literature on the learning curve in robotic general surgery to inform adopters of the technology. Methods PubMed and EMBASE searches yielded 3690 abstracts published between July 1986 and March 2016. The abstracts were evaluated based on the following inclusion criteria: written in English, reporting original work, focus on general surgery operations, and with explicit statistical methods. Results Twenty-six full-length articles were included in final analysis. The articles described the learning curves in colorectal (9 articles, 35%), foregut/bariatric (8, 31%), biliary (5, 19%), and solid organ (4, 15%) surgery. Eighteen of 26 (69%) articles report single-surgeon experiences. Time was used as a measure of the learning curve in all studies (100%); outcomes were examined in 10 (38%). In 12 studies (46%), the authors identified three phases of the learning curve. Numbers of cases needed to achieve plateau performance were wide-ranging but overlapping for different kinds of operations: 19–128 cases for colorectal, 8–95 for foregut/bariatric, 20–48 for biliary, and 10–80 for solid organ surgery. Conclusion Although robotic surgery is increasingly utilized in general surgery, the literature provides few guidelines on the learning curve for adoption. In this heterogeneous sample of reviewed articles, the number of cases needed to achieve plateau performance varies by case type and the learning curve may have multiple phases as surgeons add more complex cases to their case mix with growing experience. Time is the most common determinant for the learning curve. The literature lacks a uniform assessment of outcomes and complications, which would arguably reflect expertise in a more meaningful way than time to perform the operation alone.

Twenty years after it was introduced, robotic surgery has become more commonplace in urology – we examine its current uses and controversies

BackgroundPancreaticoduodenectomy (PD) is one of the most technically difficult abdominal operations. Recent advances have allowed surgeons to attempt PD using minimally invasive surgery techniques. This retrospective study aimed to analyze the learning curve of a single surgeon who had carried out his first 100 robot-assisted laparoscopic pancreaticoduodenectomy (RPD) in a high-volume pancreatic center.MethodsThe data on consecutive patients who underwent RPD for malignant or benign pathologies were prospectively collected and retrospectively analyzed. The data included the demographic data, operative time, estimated blood loss, postoperative length of hospital stay, morbidity rate, mortality rate, and final pathological results. The cumulative sum (CUSUM) analysis was used to identify the inflexion points which corresponded to the learning curve.ResultsBetween 2012 and 2016, 100 patients underwent RPD by a single surgeon. From the CUSUM operation time (CUSUM OT) learning curve, two distinct phases of the learning process were identified (early 40 patients and late 60 patients). The operation time (mean, 418 min vs. 317 min), hospital stay (mean, 22 days vs. 15 days), and estimated blood loss (mean, 227 ml vs. 134 ml) were significantly lower after the first 40 patients (P < 0.05). The pancreatic fistula, postoperative hemorrhage, delayed gastric emptying, and reoperation rates also decreased in the late 60 patients group (P < 0.05). Non-significant reductions were observed in the incidences of major (Clavien–Dindo Grade II or higher) morbidity, postoperative death, bile leakage, gastric fistula, wound infection, and open conversion.ConclusionsRPD was technically feasible and safe in selected patients. The learning curve was completed after 40 RPD. Further studies are required to confirm the long-term oncological outcomes of RPD.

BackgroundThe adoption of robotic techniques in liver surgery introduces significant challenges for their safe integration within hepatobiliary surgery units. This study is designed to investigate the complexities associated with establishing a robotic surgery program.MethodsData on robotic hepatobiliary surgeries were prospectively collected from October 2021 to October 2023. Historical cohorts from the institutional experiences for comparison were hand-assisted (HALS) and purely laparoscopic procedures (PLS). Inverse probability of treatment weighting and propensity score matching were employed to compare outcomes between PLS and robotic resections. The learning curve for robotic surgeries was evaluated by the cumulative sum method.ResultsIn this study, 454 patients were enrolled (113 robotic surgeries, 157 HALS, and 184 PLS). The posterosuperior segments resections were significantly higher in the robotic group (47.8%) compared to PLS (31.5%) and HALS (35.7%). There were no conversions in the robotic group, in PLS 2.7% and HALS 3.8%. The degree of difficulty according to the median of the IWATE score and IMM score was significantly higher in the robot group (p < 0.001 and p = 0.008, respectively). No significant differences in short-term outcomes were observed between robotic procedures and PLS in a matched subset of patients. Operative efficiency and blood loss improved significantly after the 75th robotic surgery patient, with high-difficulty cases (IWATE ≥ 10) incorporated from the beginning.ConclusionThis study suggests that robotic liver surgery in units with prior experience in minimally invasive liver surgery offers benefits, such as a lower conversion rate and a higher rate of successful difficult resections.

Background Worldwide, the annual number of robotic surgical procedures continues to increase. Robotic surgical skills are unique from those used in either open or laparoscopic surgery. The acquisition of a basic robotic surgical skill set may be best accomplished in the simulation laboratory. We sought to review the current literature pertaining to the use of virtual reality (VR) simulation in the acquisition of robotic surgical skills on the da Vinci Surgical System. Materials and methods A PubMed search was conducted between December 2014 and January 2015 utilizing the following keywords: virtual reality, robotic surgery, da Vinci, da Vinci skills simulator, SimSurgery Educational Platform, Mimic dV-Trainer, and Robotic Surgery Simulator. Articles were included if they were published between 2007 and 2015, utilized VR simulation for the da Vinci Surgical System, and utilized a commercially available VR platform. Results The initial search criteria returned 227 published articles. After all inclusion and exclusion criteria were applied, a total of 47 peer-reviewed manuscripts were included in the final review. Conclusions There are many benefits to utilizing VR simulation for robotic skills acquisition. Four commercially available simulators have been demonstrated to be capable of assessing robotic skill. Three of the four simulators demonstrate the ability of a VR training curriculum to improve basic robotic skills, with proficiency-based training being the most effective training style. The skills obtained on a VR training curriculum are comparable with those obtained on dry laboratory simulation. The future of VR simulation includes utilization in assessment for re-credentialing purposes, advanced procedural-based training, and as a warm-up tool prior to surgery.

As robotic surgeries increase nationwide, residency programs are implementing commensurate curriculum. Medical student exposure and comfort with these surgeries, however, is lagging. This program sought to improve student interest and confidence through additional robotic exposure. A two-part educational program was implemented at an academic institution. Part-one included a surgeon-led lecture and part-two a hands-on robotics primer where students were exposed to 3-D anatomy and instrumentation via robotic console. Data was collected via RedCap and analyzed for significance (p ​< ​0.05). Thirty-two students participated in part one, ten of which were selected for part two. The majority (82 ​%) reported being interested or very interested in pursuing additional robotic experiences and 40 ​% reported improved confidence in actively assisting in a robotics case (p ​< ​0.005). Conducting robotic exposure events improves medical students' confidence and interest in seeking future robotic surgery experiences. As robotic surgery expands, medical students have shown to benefit from earlier exposure. •As robotic surgery continues to expand, medical curriculum and exposure lags behind.•Without exposure, students report disengagement and low learning in robotic cases.•Curriculum showed significant improvement in student engagement and perception.•Low burden implementation makes this a cost-effective significant learning program.

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.

Purpose Minimally-invasive total gastrectomy (MITG) is associated with lower morbidity in comparison to open total gastrectomy but requires a learning curve (LC). We aimed to perform a pooled analysis of the number of cases required to surmount the LC (N LC ) in MITG. Methods A systematic review of PubMed, Embase, Scopus and the Cochrane Library from inception until August 2022 was performed for studies reporting the LC in laparoscopic total gastrectomy (LTG) and/or robotic total gastrectomy (RTG). Poisson mean (95% confidence interval [CI]) was used to determine the N LC . Negative binomial regression was performed as a comparative analysis. Results There were 12 articles with 18 data sets: 12 data sets (n = 1202 patients) on LTG and 6 data sets (n = 318 patients) on RTG. The majority of studies were conducted in East Asia (94.4%). The majority of the data sets (n = 12/18, 66.7%) used non-arbitrary analyses. The N LC was significantly smaller in RTG in comparison to LTG [RTG 20.5 (95% CI 17.0–24.5); LTG 43.9 (95% CI 40.2–47.8); incidence rate ratio 0.47, p < 0.001]. The N LC was comparable between totally-laparoscopic total gastrectomy (TLTG) and laparoscopic-assisted total gastrectomy (LATG) [LATG 39.0 (95% CI 30.8–48.7); TLTG 36.0 (95% CI 30.4–42.4)]. Conclusions The LC for RTG was significantly shorter for LTG. However existing studies are heterogeneous.