{"paper_id":"eb82537d-6acb-4fcc-90db-cdad58ce401c","body_text":"Vol.:(0123456789)\nJournal of Robotic Surgery (2024) 18:212 \nhttps://doi.org/10.1007/s11701-024-01954-2\nREVIEW\nRobotic assisted versus laparoscopic surgery for deep endometriosis: \na meta‑analysis of current evidence\nMatteo Pavone1,2,3 · Alessandro Baroni1 · Federica Campolo1 · Marta Goglia4 · Diego Raimondo5 · \nAntonella Carcagnì6 · Cherif Akladios7 · Jacques Marescaux3 · Francesco Fanfani1,8 · Giovanni Scambia1,8 · \nManuel Maria Ianieri1,9\nReceived: 19 February 2024 / Accepted: 14 April 2024 / Published online: 16 May 2024 \n© The Author(s) 2024\nAbstract\nEndometriosis is a benign inflammatory onco-mimetic disease affecting 10–15% of women in the world. When it is refractory \nto medical treatments, surgery may be required. Usually, laparoscopy is the preferred approach, but robotic surgery has gained \npopularity in the last 15 years. This study aims to evaluate the safety and efficacy of robotic-assisted laparoscopic surgery \n(RAS) versus conventional laparoscopic surgery (LPS) in the treatment of endometriosis. This study adheres to PRISMA \nguidelines and is registered with PROSPERO. Studies reporting perioperative data comparing RAS and LPS surgery in \npatients with endometriosis querying PubMed, Google Scholar and ClinicalTrials.gov were included in the analysis. The \nQuality Assessment of Diagnostic Accuracy Studies 2 tool (QUADAS-2) was used for the quality assessment of the selected \narticles. Fourteen studies were identified, including 2709 patients with endometriosis stage I-IV for the meta-analysis. There \nwere no significant differences between RAS and LPS in terms of intraoperative and postoperative complications, conver -\nsion rate and estimated blood loss. However, patients in the RAS group have a longer operative time (p < 0.0001) and longer \nhospital stay (p = 0.020) than those in the laparoscopic group. Robotic surgery is not inferior to laparoscopy in patients with \nendometriosis in terms of surgical outcomes; however, RAS requires longer operative times and longer hospital stay. The \nbenefits of robotic surgery should be sought in the easiest potential integration of robotic platforms with new technologies. \nProspective studies comparing laparoscopy to the new robotic systems are desirable for greater robustness of scientific \nevidence.\nkeywords Robotic assisted surgery · Endometriosis · Minimally Invasive surgery · Image-guided surgery · RAS · Robotic \nplatforms\n * Matteo Pavone \n matteo.pavone@guest.policlinicogemelli.it\n * Alessandro Baroni \n baronial.ab@gmail.com\n1 UOC Ginecologia Oncologica, Dipartimento di Scienze \nper la salute della Donna e del Bambino e di Sanità Pubblica, \nFondazione Policlinico Universitario A. Gemelli, IRCCS, \nLargo Agostino Gemelli 8, 00168 Rome, Italy\n2 Institute of Image-Guided Surgery, IHU Strasbourg, \nStrasbourg, France\n3 Research Institute against Digestive Cancer, IRCAD, \nStrasbourg, France\n4 Department of Medical and Surgical Sciences \nand Translational Medicine, Faculty of Medicine \nand Psychology, Sapienza University of Rome, Rome, Italy\n5 Division of Gynecology and Human Reproduction \nPhysiopathology, Department of Medical and Surgical \nSciences (DIMEC), IRCCS, Sant’Orsola-Malpighi Hospital, \nUniversity of Bologna, Bologna, Italy\n6 Facility of Epidemiology and Biostatistics - Gemelli \nGenerator, Fondazione Policlinico Universitario “A. Gemelli” \nIRCCS, Largo A. Gemelli, 8, 00168 Rome, Italy\n7 Department of Gynecologic Surgery, University Hospitals \nof Strasbourg, Strasbourg, France\n8 Università Cattolica del Sacro Cuore, Rome, Italy\n9 Gynecology and Breast Care Center, Mater Olbia Hospital, \nOlbia, Italy\n\n Journal of Robotic Surgery (2024) 18:212\n212 Page 2 of 10\nIntroduction\nEndometriosis, is an “onco-mimetic” inflammatory dis-\nease influenced by estrogen, that impacts the 10–15% of \nwomen in their reproductive age [1 ]. It primarily presents \nin the pelvic region, manifesting as superficial perito-\nneal implants, ovarian endometriomas, or “deep” lesions \nextending beyond the peritoneal surface (> 5 mm), com-\nmonly found in areas like the uterosacral ligaments, \nrectouterine pouch, vagina, bowel, bladder, and ureters. \nSymptoms vary based on the location and may include \ndysmenorrhea, chronic pelvic pain, dyspareunia, infertil-\nity, and urinary and intestinal function impairment [2 ]. \nSurgical excision of lesions is considered recommended \nif hormonal treatments prove insufficient to manage the \nsymptoms [3 , 4], in case of bowel or ureteral stricture \nor in selective case of infertility [4 ]. Minimally invasive \nsurgical (MIS) approaches have become predominant in \nthe surgical management of the disease, with laparoscopy \nas a standard of care [4 ]. Despite its advantages, conven-\ntional laparoscopy has limitations such as 2-dimensional \nvisualization, ergonomic limits, and a restricted range of \ninstruments. Over the past decade, the viability, efficacy, \nand safety of robotic-assisted surgery (RAS) in addressing \ndeep endometriosis has been reported, demonstrating its \nnon-inferiority to laparoscopy [5 ]. Robotic systems offer \nenhanced depth perception, wrist articulation, and dexter -\nity, particularly beneficial for complex cases or challeng-\ning anatomical locations like diaphragmatic endometriosis \nor sites involving the sacral plexus or ischial nerves [6 , 7]. \nThe use of robotic articulated instruments, equipped with \nclutching mechanisms that exceed the range of motion of \nthe human wrist (>  360°), facilitates access to these areas. \nHowever, the lack of tactile feedback and the associated \nhigh costs of installation and maintenance present obsta-\ncles to the widespread adoption of RAS [8 ]. Despite estab-\nlished benefits in various surgical domains, the superiority \nof RAS over traditional laparoscopy in treating endome-\ntriosis remains unknown [9 ]. The aim of this meta-anal-\nysis is to compare the effectiveness and safety of these \napproaches in the surgical management of endometriosis.\nMethods\nThe review was conducted according to Preferred Report-\ning Items for Systematic Reviews and Meta-Analyses \n(PRISMA) guidelines [10]. Before data extraction, the \nreview was registered with the International Prospective \nRegister of Systematic Reviews (PROSPERO, Registration \nN° CRD CRD42023495700).\nEligibility criteria\nAccording to the PICO [10] schema were selected articles \nfocused on comparison between robotic assisted and laparo-\nscopic surgery in deep endometriosis regarding at least one of \nthe following parameters: (i) intraoperative complications (ii) \npostoperative complications (iii) operative time (iv) conversion \nrate (v) estimated blood loss (vi) hospital stay. Articles not \nreporting comparisons between the two surgical approaches \nwere excluded. Only full-text studies were considered eligible \nfor inclusion. Abstracts, reviews, meta-analyses, letters, case \nreports and editorials were excluded (Table 1).\nSearch strategy\nThe studies included for analysis were obtained querying \nthe PubMed database, Google Scholar and ClinicalTrial.gov \nbetween September and November 2023, filtered only by Eng-\nlish language and publication year (1980–2023). The search \nstrategy is reported in the supplementary material (Online \nSupplementary A).\nStudy selection\nRayyan software (Qatar Computing Research Institute, HBKU, \nDoha, Qatar) [11] was used independently by two authors (MP \nand AB) to screen titles and abstracts for eligibility. Manual \nsearches were performed on pertinent resources and online \nlinks, and references of selected articles were examined. \nDuplicate entries were eliminated during the title/abstract \nreview. For all relevant studies, the complete text was reviewed \nby both authors independently. Discordant assessments were \nresolved by consultation of a third author (MG).\nData collection\nData collection included: author, publication year, country, \nsample size, age, BMI, rASRM [12], stage previous surgery, \nintra- and postoperative reported data. We will provide our \ndata for independent analysis by a selected team or for addi-\ntional data analysis or for the reproducibility of this study in \nother centers if such is requested.\nAssessment of risk of bias\nThe risk of bias was assessed independently by two \nreviewers (MP and AB) using the Quality Assessment of \nDiagnostic Accuracy Studies 2 (QUADAS-2) tool [13]. \nThe risk of bias was assessed for the following domains: \npatient selection, index test, reference standard, and flow \n\nJournal of Robotic Surgery (2024) 18:212 \n Page 3 of 10 212\nand timing. Discordant assessments were resolved by con-\nsultation of a third author (MG).\nAnalysis and data synthesis\nStatistical analyses were performed using R statistical \nsoftware (version 4.2.1) meta e metaplus statistical pack -\nage of the software R was used. Risk Ratios (RRs) along-\nside their 95% confidence intervals (CIs) for intra-, post-\noperative complications and conversion rates data were \nextracted from the studies or calculated. To continue varia -\nbles (operative time (min) OT, estimated blood loss (EBL) \nand hospitalization stay) SMD were calculated. A random-\neffects model was used to take the source of heterogene-\nity related to the clinical setting into account. To assess \nheterogeneity between studies, the Cochrane’s Q  test and \nI2 index were used. p  values of < 0.05 were considered as \nvalid for heterogeneity tests. Pooled estimations and the \nrelated 95% CIs were evaluated using forest plots. A fun-\nnel plot was depicted for the detection of publication bias.\nResults\nStudy selection and characteristics\nThe initial search identified 340 articles. After removing \nduplicates and title/abstract screening, 79 manuscripts \nwere assessed for eligibility. Of these, were excluded \nas they addressed a different outcome (51) or a different \ndesign (10) or were inaccessible (2) or in a language dif-\nferent than English (1). A list of excluded articles is pro-\nvided in Online Supplementary B. Consequently, fourteen \nstudies were included for data synthesis (Online Supple-\nmentary C) and one prospective trial was identified. The \nPRISMA flow diagram shows the complete review process \nfrom the original search to the final selection (Fig.  1). The \nFourteen studies selected for the meta-analysis covered a \ntotal of 2709 patients. Of these twelve (85.7%) are retro-\nspective and 2 prospective (14.3%).\nTable 1  Study Characteristics\nAuthor Year Study type Group Sample size (n) Age (mean, SD) BMI rASRM(12) stage\nNezhat et al. [14] 2010 Retrospective LPS\nRAS\n38\n40\n33 (18–46)\n35 (22–49)\n23 (18–31)\n24 (19–37)\nI–IV\nDulemba et al. [15] 2013 Retrospective LPS\nRAS\n100\n180\n29.2 ± 9.2\n32.6 ± 9.7\n26.8 ± 11.9\n27.9 ± 7.7\nI–IV\nNezhat et al. [20] 2014 Retrospective LPS\nRAS\n86\n32\n40 ± 4.5\n42.5 ± 2.2\n24.53 ± 1.2\n27.36 ± 2.5\nIII–IV\nNezhat et al. [19] 2015 Retrospective LPS\nRAS\n273\n147\n31 ± 5.7\n30 ± 2.5\n23 ± 2.5\n23 ± 3.2\nIII–IV\nMagrina et al. [21] 2015 Retrospective LPS\nRAS\n162\n331\n38.3 ± 10.7\n40 ± 10.1\n25.5 ± 5.7\n26.1 ± 5.9\nIII–IV\nSoto et al. [5] 2017 Prospective LPS\nRAS\n38\n35\n34.5 ± 8.5\n34.3 ± 7.2\n24.8 ± 5.9\n26.1 ± 5.2\nI–IV\nLe Gac et al. [22] 2020 Prospective LPS\nRAS\n25\n23\n37 ± 8\n36 ± 7\n25 ± 4\n25 ± 3\nIII–IV\nHiltunen et al. [16] 2021 Retrospective LPS\nRAS\n76\n18\nNA\nNA\n26 (19–39)\n24 (18–38)\nI–IV\nRaimondo et al. [23] 2021 Retrospective LPS\nRAS\n22\n22\n36 ± 5\n38 ± 7\n22.5 (21–24)\n24.5 (21–27)\nIII–IV\nFerrier et al. [17] 2022 Retrospective LPS\nRAS\n61\n61\n35 ± 7\n36 ± 7\n26 ± 8\n25 ± 5\nI–IV\nLegendri et al. [18] 2022 Retrospective LPS\nRAS\n28\n26\n34 (27.5–37.5)\n36.5(29.7–43.5)\n23 (21–29)\n23 (20.5–27.5)\nIV\nCrestani et al. [26] 2023 Retrospective LPS\nRAS\n73\n89\nNA\nNA\n26 (19–39)\n24 (18–38)\nIII–IV\nVolodarsky Perel et al. [24] 2023 Retrospective LPS\nRAS\n451\n97\n37.9 (31.7–44.1\n37.3 (30.5–44.1)\n22.6 (20.3–25.6)\n23.2 (21.3–26.9)\nIII–IV\nVerrelli et al. [25] 2023 Retrospective LPS\nRAS\n104\n71\n38.4 (31.5–45.3)\n37.3 (31.4–43.2)\n23.6 (19.5–27.7)\n23.8 (18.8–28.8)\nIII–IV\n\n Journal of Robotic Surgery (2024) 18:212\n212 Page 4 of 10\nRisk of bias of included studies\nThe quality assessment of the included studies is presented \nin Online Supplementary D. Most studies were at low risk \nof bias regarding patient selection, index test, and reference \nstandard domains (8, 61,5%).\nFive articles had an unclear risk of bias in the patient’s \nselection as they reported data on patients without differ -\nentiating the rASRM stage [5 , 14–17] while one focused \nonly on stage IV [18]. One was at an unclear risk of bias \nand applicability in patient selection due to the exclusion of \nwomen undergoing bladder ureteral or bowel resection [19].\nMeta‑analysis\nIntra‑ and postoperative complications\nEight [5 , 15–17, 20–23] studies assessed the intra-opera-\ntive complications of RAS and LPS surgical procedures: \nthe Risk Ratio (RR) of 1.638, 95% CI [0.552; 4.855] and \np = 0.373, indicated no significant difference between RAS \nand LPS. The I2 was 23.3%, and test of heterogeneity sug-\ngested low statistical heterogeneity (Fig.  2).\nEleven [5 , 15–18, 20–25] studies assessed the post-\noperative complication of RAS and LPS surgical proce -\ndures: the Risk Ratio (RR) of 0.952, 95% CI [0.776; 1.169] \nand p = 0.642, indicated no significant difference between \nRAS and LPS. The I2 was 0.0%, and test of heterogeneity \nsuggested low statistical heterogeneity (Fig.  3).\nConversion rate\nFour [5 , 17, 21, 23] studies assessed the conversion rates \nof RAS and LPS surgical procedures: the Risk Ratio (RR) \nof 1.262, 95% CI [0.328; 4.846] and p  = 0.734, indicated \nno significant difference between RAS and LPS. The I 2 \nwas 0.0%, and the test of heterogeneity suggested low sta-\ntistical heterogeneity (Fig.  4).\nFig. 1  PRISMA Flow diagram \nfor study selection\n\n\nJournal of Robotic Surgery (2024) 18:212 \n Page 5 of 10 212\nFig. 2  Forest plot for intraoperative complications comparing RAS with LPS\nFig. 3  Forest plot for postoperative complications comparing RAS with LPS\nFig. 4  Forest plot for conversion rates comparing RAS with LPS\n\n Journal of Robotic Surgery (2024) 18:212\n212 Page 6 of 10\nOperative time\nEleven [5, 14, 15, 17, 20–23, 25–27] studies assessed the \noperative time of the two surgical procedures. The stand-\nardisation mean difference (SMD) of 0.54 (min), 95% CI \n[0.247; 0.842] and p < 0.0001, shows that the patients in \nthe RAS group have a longer operative time than those of \nthe laparoscopic group. The I2 was 83% and the Cochrane’s \nQ test significant results (p  < 0.0001) suggested high sta-\ntistical heterogeneity between studies (Fig.  5).\nEstimated blood loss\nNine [5 , 14, 15, 17, 20–23, 25] studies assessed the esti-\nmated blood loss of RAS and LPS surgical procedures: the \nstandardisation mean difference (SMD) of 0.028, 95% CI \n[− 0.080; 0.136] and p  = 0.616, indicated no significant \ndifference between RAS and LPS. The I 2 was 1.8%, and \nthe test of heterogeneity suggested low statistical hetero-\ngeneity (Fig.  6).\nLength of hospital stay\nSeven [17, 20–23, 25, 26] studies assessed hospitalization \nstay of RAS vs LPS surgical procedures: the standardisation \nmean difference (SMD) of 0.135, 95% CI [0.022; 0.262] and \np = 0.020, indicated a significant difference between RAS \nand LPS. The I 2 was 26.7%, and the test of heterogeneity \nsuggested low statistical heterogeneity (Fig.  7).\nDiscussion\nThe results of this meta-analysis show the absence of signifi-\ncant differences between the robotic-assisted surgery and the \nstandard laparoscopic approach for endometriosis surgery \nin terms of intraoperative and postoperative complications, \nFig. 5  Forest plot for operative time comparing RAS with LPS\nFig. 6  Forest plot for blood loss comparing RAS with LPS\n\nJournal of Robotic Surgery (2024) 18:212 \n Page 7 of 10 212\nconversion rate and estimated blood loss. However, patients \nin the RAS group have a longer operative time (p < 0.0001) \nand longer hospital stay (p  = 0.020) than those in the lapa-\nroscopic group.\nThese results confirm what was previously reported in \nthe metanalysis of Restaino et al. comprising 5 articles on \nthe same topic, with no statistical differences for operative \noutcomes and a longer OT reported for RAS with a weighted \nmean difference of 0.54 (p  < 0.00001) [9]. Therefore, dis-\ncrepancies are reported in the literature regarding OT in \nRAS procedures for endometriosis. A longer operating time \n(MD = 28.09 min, CI 11.59–44.59) and an increased aver -\nage time of use of the operating room (MD = 51.39 min, CI \n15.07–87.72;) is also shown by Csirzó et al. in their recent \narticle [ 28]. However, Magrina et al. [ 21] after adjusting \ntheir findings for age, blood loss, and number of procedures \nper patient, showed that RAS approach resulted in 16.2% \nshorter OT than LPS.\nA recent prospective multicentre randomized trial \n(LAROSE trial) enrolling 73 patients with suspicion of \npelvic endometriosis, showed a similar OT between RAS \nand LPS (mean ± SD, 107 ± 48 min vs. 102 ± 63 min) when \nadjusted to the stage of disease [5 ]. According to the latter, \nthe study of Raimondo et al. [23] showed no significant dif-\nference between the two groups regarding OT.\nAmong the factors contributing to the extension of the \ntime required to perform robotic surgery is the docking of \nthe platform. However, these times are directly proportional \nto the team’s experience and decrease with the learning \ncurve [29]. Regarding the longer hospital stay this could \nbe attributable to a bias in the worst health conditions of \npatients who are candidates for robotic surgery than for LPS \n(i.e. obesity) [30].\nIn addition, after two decades of the Da  Vinci® surgi-\ncal robotic system (Intuitive Surgical, California, USA) as \nthe sole protagonist in the field of RAS, the introduction \nof new robotic platforms on the marketplace with differ -\nent features (i.e. open consoles, independent bed-side units) \nmay highlight new evidence. The feasibility of surgical \ninterventions for endometriosis using the new  HUGO™  \nRAS (Medtronic, Minneapolis, USA) [31, 32] has already \nbeen demonstrated while for other new platforms as the Ver-\nsius (CMR robotics, UK) system studies are ongoing [33]. \nRobotic single-site surgery for managing endometriosis was \ncarried out by Huang et al. In 12% of cases, an extra port was \nintroduced to facilitate greater precision of instruments and \nto address a broader surgical field, particularly in instances \ninvolving more complex locations [34]. Despite the growing \nglobal adoption of robotic surgery and the increased exper -\ntise among surgeons, there is currently insufficient evidence \nto establish the superiority of robotic surgery over standard \nlaparoscopy in endometriosis surgery. The limited reim-\nbursement for robotic procedures and the extended operative \ntime remains significant concerns, particularly when juxta-\nposed with the absence of discernible differences in periop-\nerative outcomes. It is important to assess the benefits of the \ndevelopment of robotic surgery beyond the comparison of \nspecific outcomes. As the range of available platforms con-\ntinues to expand, it becomes imperative to precisely deline-\nate the potential advantages and constraints associated with \ndifferent systems. The crucial task is not solely to choose the \nmost suitable platform for an individual surgeon, but also to \npinpoint the optimal system tailored to the specific require-\nments of single patients or procedures [35].\nThe current challenge lies in the training of surgeons and \nthe development of the operating room of the future. In the \nera of digital surgery, robotic platforms serve as computer \ninterfaces capable of integrating various real-time data \nanalysis modalities. This enables advanced systems to pro-\nvide augmented surgical vision through augmented reality \n(AR), improved surgical decisions using artificial intelli-\ngence (AI), and enhanced surgical manoeuvres through the \nadvancement of robotic instruments [36]. The incorpora-\ntion of preoperative planning, utilizing 3D acquisition of \nradiological images, coupled with the utilization of deep \nlearning (DL) algorithms to analyze surgical phases, forms \nFig. 7  Forest plot for the length of hospitalization comparing RAS with LPS\n\n Journal of Robotic Surgery (2024) 18:212\n212 Page 8 of 10\nan ideal toolkit for enhancing robotic surgery [ 37]. This \nholistic approach aims to reduce intraoperative complica-\ntions and optimize surgical outcomes by minimizing surgi-\ncal discrepancies. The operating room is transitioning into \na control center akin to an airport control tower, capable of \nprocessing 2D/3D inputs derived from preoperative images, \nenvironmental and laparoscopic cameras, and patient physi-\nological signals. It then relays outputs to robotic platforms, \noffering real-time information on the surgeon’s screen dur-\ning intraoperative processes, such as remaining operating \ntime or the patient’s clinical situation. Image-guided surgery, \nparticularly intraoperative ultrasound, is gaining prominence \nin robotic surgery [38, 39]. The integration of drop-in ultra-\nsound probes, easily manipulated by robotic graspers, allows \naccess to challenging anatomical spaces [40]. Intraoperative \nultrasound, with images projected onto the surgeon’s screen \nvia platforms like Intuitive Surgical’s TilePro, proves benefi-\ncial for achieving surgical radicality in endometriosis [41]. \nMoreover, robotic systems prove beneficial for educational \npurposes, providing simulators that can democratize training \nopportunities, even for non-expert surgeons [42].\nIn this context, the recent published IDEAL Robotics \nColloquium proposes recommendations for evaluation dur-\ning development, comparative study and clinical monitor -\ning of surgical robots—providing practical guidelines for \ndevelopers, clinicians, patients and healthcare systems [43].\nThis paper represents the most recent analysis of the cur-\nrent literature on the comparison of RAS and laparoscopy \nin patients with endometriosis. The inclusion of 5 papers \npublished in the last 24 months, as well as the methodo-\nlogical accuracy and the assessment of the risk of bias are \nundoubtedly strengths of the work. However, the retrospec-\ntive nature of most of the included articles and the adoption \nin all papers of the Da Vinci platform as the only robotic \nsystem analysed represent a limitation of this research. Only \none prospective trial was found ongoing (NCT05179109) \nwith the aim to examine whether robot-assisted laparoscopy \nis superior compared to conventional laparoscopy as regards \nto patient outcome at 6, 12 and 24 months postoperatively, \nmeasured by questionnaires concerning the pain symptoms \nand disease-related quality-of-life. Future studies, includ-\ning experience with new robotic platforms and comparisons \nbetween these, will be needed to better understand the ben-\nefits of RAS over conventional laparoscopy.\nConclusion\nIn conclusion, robotic surgery is not inferior to laparos-\ncopy in patients with endometriosis in terms of surgical \noutcomes; however, RAS require longer operative times \nand longer hospital stays. The benefits of robotic surgery \nshould be sought in the easiest potential integration of \nrobotic platforms with new technologies. Furthermore, \nprospective studies comparing laparoscopy to the new \nrobotic systems are desirable for greater robustness of \nscientific evidence.\nSupplementary Information The online version contains supplemen-\ntary material available at https:// doi. org/ 10. 1007/ s11701- 024- 01954-2.\nAuthor contributions Study design: MP, MMI; Literature search: MP, \nAB, MG; Data extraction: MP, AB; Data synthesis: MP, AB, SR, LCT, \nDR; Manuscript drafting: MP, MG; Statistical analysis: AC; Critical \nrevision of the manuscript: CA, JM, FF, GS. All Authors approved the \nfinal version of the manuscript for submission.\nFunding Open access funding provided by Università Cattolica del \nSacro Cuore within the CRUI-CARE Agreement. None.\nData availability All data generated or analyzed in this review are \nincluded in the manuscript and its figures/tables. Further enquiries can \nbe directed to the corresponding author.\nDeclarations \nConflict of interest Authors have no relevant conflict of interest to de-\nclare.\nOpen Access  This article is licensed under a Creative Commons Attri-\nbution 4.0 International License, which permits use, sharing, adapta-\ntion, distribution and reproduction in any medium or format, as long \nas you give appropriate credit to the original author(s) and the source, \nprovide a link to the Creative Commons licence, and indicate if changes \nwere made. The images or other third party material in this article are \nincluded in the article's Creative Commons licence, unless indicated \notherwise in a credit line to the material. If material is not included in \nthe article's Creative Commons licence and your intended use is not \npermitted by statutory regulation or exceeds the permitted use, you will \nneed to obtain permission directly from the copyright holder. To view a \ncopy of this licence, visit http:// creat iveco mmons. org/ licen ses/ by/4. 0/.\nReferences\n 1. Giudice LC (2010) Clinical practice. Endometr N Engl J Med \n362(25):2389–2398\n 2. Ianieri MM, Raimondo D, Rosati A, Cocchi L, Trozzi R, Maletta \nM et al (2022) Impact of nerve-sparing posterolateral parame-\ntrial excision for deep infiltrating endometriosis on postopera-\ntive bowel, urinary, and sexual function. Int J Gynaecol Obstet \n159(1):152–159\n 3. Ianieri MM, Buca DIP, Panaccio P, Cieri M, Francomano F, Lib-\nerati M (2017) Retroperitoneal endometriosis in postmenopausal \nwoman causing deep vein thrombosis: case report and review of \nthe literature. Clin Exp Obstet Gynecol 44(1):148–150\n 4. Dunselman GAJ, Vermeulen N, Becker C, Calhaz-Jorge C, \nD’Hooghe T, De Bie B et al (2014) ESHRE guideline: manage-\nment of women with endometriosis. Hum Reprod 29(3):400–412\n 5. Soto E, Luu TH, Liu X, Magrina JF, Wasson MN, Einarsson JI \net al (2017) Laparoscopy vs. robotic surgery for endometriosis \n(LAROSE): a multicenter, randomized, controlled trial. Fertil \nSteril 107(4):996-1002.e3\n\nJournal of Robotic Surgery (2024) 18:212 \n Page 9 of 10 212\n 6. Roman H, Dennis T, Grigoriadis G, Merlot B (2022) Robotic \nmanagement of diaphragmatic endometriosis in 10 steps. J Minim \nInvasive Gynecol 29(6):707–708\n 7. Ianieri MM, Nardone ADC, Pavone M, Benvenga G, Pafundi MP, \nCampolo F et al (2023) Are ureterolysis for deep endometrio-\nsis really all the same? an anatomical classification proposal for \nureterolysis: a single-center experience. Int J Gynaecol Obstet \n162:1010–1019\n 8. Pavone M, Marescaux J, Seeliger B (2023) Current sta-\ntus of robotic abdominopelvic surgery. Show-Chwan Med J \n22(3):467473. https:// doi. org/ 10. 30185/ scmj. 202307/ pp. 0003\n 9. Restaino S, Mereu L, Finelli A, Spina MR, Marini G, Catena U \net al (2020) Robotic surgery vs laparoscopic surgery in patients \nwith diagnosis of endometriosis: a systematic review and meta-\nanalysis. J Robot Surg 14(5):687–694\n 10. Moher D, Liberati A, Tetzlaff J, Altman DG, PRISMA Group \n(2009) Preferred reporting items for systematic reviews and meta-\nanalyses: the PRISMA statement. PLoS Med 6(7):e1000097\n 11. Ouzzani M, Hammady H, Fedorowicz Z, Elmagarmid A (2016) \nRayyan—a web and mobile app for systematic reviews. Syst Rev \n5(1):210\n 12. American Society for Reproductive Medicine (1997) Revised \nAmerican society for reproductive medicine classification of \nendometriosis: 1996. Fertil Steril 67(5):817–821\n 13. Whiting PF, Rutjes AWS, Westwood ME, Mallett S, Deeks JJ, \nReitsma JB et al (2011) QUADAS-2: a revised tool for the qual-\nity assessment of diagnostic accuracy studies. Ann Intern Med \n155(8):529–536\n 14. Nezhat C, Lewis M, Kotikela S, Veeraswamy A, Saadat L, Hajhos-\nseini B et al (2010) Robotic versus standard laparoscopy for the \ntreatment of endometriosis. Fertil Steril 94(7):2758–2760\n 15. Dulemba JF, Pelzel C, Hubert HB (2013) Retrospective analysis of \nrobot-assisted versus standard laparoscopy in the treatment of pel-\nvic pain indicative of endometriosis. J Robot Surg 7(2):163–169\n 16. Hiltunen J, Eloranta ML, Lindgren A, Keski-Nisula L, Anttila \nM, Sallinen H (2021) Robotic-assisted laparoscopy is a feasible \nmethod for resection of deep infiltrating endometriosis, especially \nin the rectosigmoid area. J Int Med Res 49(8):3000605211032788\n 17. Ferrier C, Le Gac M, Kolanska K, Boudy AS, Dabi Y, Touboul \nC et al (2022) Comparison of robot-assisted and conventional \nlaparoscopy for colorectal surgery for endometriosis: a prospec-\ntive cohort study. Int J Med Robot 18(3):e2382\n 18. Legendri S, Carbonnel M, Feki A, Moawad G, Aubry G, Vallée A, \net al. Improvement of Post-Operative Quality of Life in Patients 2 \nYears after Minimally Invasive Surgery for Pain and Deep Infil-\ntrating Endometriosis. J Clin Med [Internet]. 2022 [cited 10AD \nJan 1];11(20). https:// pubmed. ncbi. nlm. nih. gov/ 36294 462/. \nAccessed 12 Dec 2023\n 19. Nezhat CR, Stevens A, Balassiano E, Soliemannjad R (2015) \nRobotic-assisted laparoscopy vs conventional laparoscopy for \nthe treatment of advanced stage endometriosis. J Minim Invasive \nGynecol 22(1):40–44\n 20. Nezhat FR, Sirota I (2014) Perioperative outcomes of robotic \nassisted laparoscopic surgery versus conventional laparoscopy sur-\ngery for advanced-stage endometriosis. JSLS 18(4):e2014.00094\n 21. Magrina JF, Espada M, Kho RM, Cetta R, Chang YHH, Magtibay \nPM (2015) Surgical excision of advanced endometriosis: perioper-\native outcomes and impacting factors. J Minim Invasive Gynecol \n22(6):944–950\n 22. Le Gac M, Ferrier C, Touboul C, Owen C, Arfi A, Boudy AS et al \n(2020) Comparison of robotic versus conventional laparoscopy for \nthe treatment of colorectal endometriosis: pilot study of an expert \ncenter. J Gynecol Obstet Hum Reprod 49:101885\n 23. Raimondo D, Alboni C, Orsini B, Aru AC, Farulla A, Maletta \nM et al (2021) Comparison of perioperative outcomes between \nstandard laparoscopic and robot-assisted approach in patients \nwith rectosigmoid endometriosis. Acta Obstet Gynecol Scand \n100(9):1740–1746\n 24. Volodarsky-Perel A, Merlot B, Denost Q, Dennis T, Chanavaz-\nLacheray I, Roman H (2023) Robotic-assisted versus conventional \nlaparoscopic approach in patients with large rectal endometriotic \nnodule: the evaluation of safety and complications. Colorectal Dis \n25(11):2233–2242\n 25. Verrelli L, Merlot B, Chanavaz-Lacheray I, Braund S, D’ Ancona \nG, Kade S, et al. Robotic surgery for severe endometriosis: a pre-\nliminary comparative study of cost estimation. J Minim Invasive \nGynecol [Internet]. 2023 [cited 11AD Jan 1]. https:// pubmed. ncbi. \nnlm. nih. gov/ 37935 331/. Accessed 12 Dec 2023\n 26. Crestani A, Bibaoune A, Le Gac M, Dabi Y, Kolanska K, Ferrier \nC, et al. Changes in hospital consumption of opioid and non-\nopioid analgesics after colorectal endometriosis surgery. J Robot \nSurg [Internet]. 2023 [cited 8AD Jan 1]. https:// pubmed. ncbi. nlm. \nnih. gov/ 37606 871/. Accessed 12 Dec 2023\n 27. Hodneland E, Dybvik JA, Wagner-Larsen KS, Šoltészová V, \nMunthe-Kaas AZ, Fasmer KE et al (2021) Automated segmenta -\ntion of endometrial cancer on MR images using deep learning. Sci \nRep 8(11):179\n 28. Csirzó Á, Kovács DP, Szabó A, Fehérvári P, Jankó Á, Hegyi P \net al (2023) Robot-assisted laparoscopy does not have demon-\nstrable advantages over conventional laparoscopy in endometrio-\nsis surgery: a systematic review and meta-analysis. Surg Endosc \n38:529–539\n 29. Panico G, Mastrovito S, Campagna G, Monterossi G, Costantini \nB, Gioè A et al (2023) Robotic docking time with the Hugo™ \nRAS system in gynecologic surgery: a procedure independ-\nent learning curve using the cumulative summation analysis \n(CUSUM). J Robot Surg 17(5):2547–2554\n 30. Alboni C, Mattos LC, Marca AL, Raimondo D, Casadio P, Serac-\nchioli R et al (2023) Robotic surgery and deep infiltrating endo-\nmetriosis treatment: the state of art. CEOG 50(1):13\n 31. Pavone M, Seeliger B, Alesi MV, Goglia M, Marescaux J, \nScambia G et al (2023) Initial experience of robotically assisted \nendometriosis surgery with a novel robotic system: first case series \nin a tertiary care center. Updates Surg. https:// doi. org/ 10. 1007/ \ns13304- 023- 01724-z\n 32. Pavone M, Goglia M, Campolo F, Scambia G, Ianieri MM (2023) \nEn-block butterfly excision of posterior compartment deep endo-\nmetriosis: the first experience with the new surgical robot Hugo™ \nRAS. Facts Views Vis Obgyn 15(4):359–362\n 33. Sighinolfi MC, De Maria M, Meneghetti J, Felline M, Ceretti AP, \nMosillo L et al (2024) The use of versius CMR for pelvic sur -\ngery: a multicentric analysis of surgical setup and early outcomes. \nWorld J Urol 42(1):31\n 34. Huang Y, Duan K, Koythong T, Patil NM, Fan D, Liu J et al \n(2022) Application of robotic single-site surgery with optional \nadditional port for endometriosis: a single institution’s experience. \nJ Robotic Surg 16(1):127–135\n 35. Fanfani F, Restaino S, Ercoli A, Chiantera V, Fagotti A, Gallotta \nV et al (2016) Robotic versus laparoscopic surgery in gynecology: \nwhich should we use? Minerva Ginecol 68(4):423–430\n 36. Lecointre L, Verde J, Goffin L, Venkatasamy A, Seeliger B, Lodi \nM et al (2022) Robotically assisted augmented reality system for \nidentification of targeted lymph nodes in laparoscopic gynecologi-\ncal surgery: a first step toward the identification of sentinel node. \nSurg Endosc 36(12):9224–9233\n 37. Seeliger B, Diana M, Ruurda JP, Konstantinidis KM, Marescaux \nJ, Swanström LL (2019) Enabling single-site laparoscopy: the \nSPORT platform. Surg Endosc 33(11):3696–3703\n 38. Mascagni P, Padoy N (2021) OR black box and surgical control \ntower: recording and streaming data and analytics to improve sur-\ngical care. J Visc Surg 158(3S):S18-25\n\n Journal of Robotic Surgery (2024) 18:212\n212 Page 10 of 10\n 39. Pavone M, Spiridon IA, Lecointre L, Seeliger B, Scambia G, Ven-\nkatasamy A et al (2023) Full-field optical coherence tomography \nimaging for intraoperative microscopic extemporaneous lymph \nnode assessment. Int J Gynecol Cancer. https:// doi. org/ 10. 1136/ \nijgc- 2023- 005050\n 40. Guerra F, Amore Bonapasta S, Annecchiarico M, Bongiolatti S, \nCoratti A (2015) Robot-integrated intraoperative ultrasound: ini-\ntial experience with hepatic malignancies. Minim Invasive Ther \nAllied Technol 24(6):345–349\n 41. Otani K, Kiyomatsu T, Ishimaru K, Kataoka A, Hayashi Y, Gohda \nY (2023) Usefulness of real-time navigation using intraoperative \nultrasonography for rectal cancer resection. Asian J Endosc Surg \n16(4):819–821\n 42. Simmonds C, Brentnall M, Lenihan J (2021) Evaluation of a novel \nuniversal robotic surgery virtual reality simulation proficiency \nindex that will allow comparisons of users across any virtual real-\nity simulation curriculum. Surg Endosc 35(10):5867–5875\n 43. Marcus HJ, Ramirez PT, Khan DZ, Layard Horsfall H, Hanrahan \nJG, Williams SC et al (2024) The IDEAL framework for surgi-\ncal robotics: development, comparative evaluation and long-term \nmonitoring. Nat Med 30(1):61–75\nPublisher's Note Springer Nature remains neutral with regard to \njurisdictional claims in published maps and institutional affiliations.","source_license":"CC0","license_restricted":false}