{"paper_id":"0904cd0b-00d7-424d-b6f1-1a33e3da1d64","body_text":"Attention to the Editor - Similarity Report- Evaluating the Learning Curve in Robot-Assisted Laparoscopic Total Hysterectomy: Single-Port versus Multi-port Da Vinci Platforms | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Attention to the Editor - Similarity Report- Evaluating the Learning Curve in Robot-Assisted Laparoscopic Total Hysterectomy: Single-Port versus Multi-port Da Vinci Platforms Riccardo Vizza, Simone Garzon, Giacomo Corrado, Valentina Bruno, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7687838/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 01 Nov, 2025 Read the published version in Journal of Robotic Surgery → Version 1 posted 9 You are reading this latest preprint version Abstract The aim of this study is to assess and compare the learning curves of the Da Vinci S Multi-port (MP) and Da Vinci Single-Port (SP) platforms for total hysterectomy. This is a retrospective comparative study on patients undergoing robot-assisted total hysterectomy (benign or early endometrial cancer indication) with the MP and SP systems. All operations were performed by the same surgeon, and cases performed with the SP system were performed after the MP system. Operating time was used as the main indicator of the learning curve, analyzed by the CUSUM method. Data were processed with R software, and differences between groups were evaluated with t-test and Chi-squared test (significance p < 0.05). A total of 147 patients were analyzed (73 MP, 74 SP). Mean operating times were similar between the two groups (117 min MP vs 114 min SP). Hemoglobin drop was significantly lower in the SP group (−1.18 g/dL vs −2.07 g/dL, p < 0.05). CUSUM analysis showed that the learning curve of the MP system reaches the mastery phase after 50 cases, while 13 cases are sufficient for the SP system when initiated after having mastered the MP system. The learning rate was -0.3 min/case for MP and -0.009 min/case for SP. In conclusion much of the experience gained with the Da Vinci Multiport System is transferable to the SP platform, allowing for a shorter learning curve and rapid achievement of surgical mastery with similar learning phases. Learning Curve Robot-Assisted Hysterectomy Da Vinci Single-Port Da Vinci Multi-port Figures Figure 1 Figure 2 Introduction The Da Vinci SP© is a specialized variant of the Da Vinci surgical system tailored for single-port surgery. It utilizes a single robotic arm that passes through one small skin incision. This is made possible by its design, which includes a single flexible camera and three multi-articulated robotic instruments. All these instruments employ two points of articulation within the body, allowing for better angulation toward the surgical field. The Da Vinci Single Port by Intuitive Surgical, Inc. (Sunnyvale, CA, USA) received approval for gynecologic surgery in South Korea in 2019 and in Japan in 2022. In the United States, while SP was approved in 2019 for select otolaryngology and urology procedures, the approval for gynecologic surgery is still pending. In the EU, the Da Vinci SP has been approved for gynecological procedures in 2024. Since 2019, the SP system has been demonstrated feasible for several major robot-assisted operations. SP surgery technique was introduced into clinical practice to perform cholecystectomy, colorectal surgery, urological and gynecological surgery, with encouraging preliminary results[ 1 – 4 ]. Given that the SP system was introduced after widespread adoption of the MP platform, it is crucial to understand whether the competencies gained with MP robot-assisted surgery are transferable to SP procedures. This has important implications for surgical training, patient safety, and the efficient integration of new technology into clinical practice. In this context, analyzing the learning curve (LC) becomes particularly relevant, as it represents the relationship between a surgeon’s experience and patient outcomes, and it is crucial to estimate the number of patients at risk of suboptimal outcomes during the learning process[ 5 ]. This study aims to define the learning curve for the SP platform and the MP platform, with particular attention to how prior MP experience influences SP performance. Methods We conducted a retrospective study to compare the learning curve for the procedure of total hysterectomy performed using the Da Vinci SP© (Intuitive Surgical, Inc., Sunnyvale, CA, USA) system versus the Da Vinci S Multi-port© (Intuitive Surgical, Inc., Sunnyvale, CA, USA) system. The study was approved by the Ethics Committee of the Regina Elena National Cancer Institute of Rome, Italy (RS: 322/IRE/25). Informed consent to surgical intervention, including consent for the retrospective use of clinical data, was obtained from all the patients in accordance with local and international legislation (Declaration of Helsinki)[ 6 ]. We retrospectively reviewed the prospectively maintained operative room database and identified all consecutive total hysterectomy procedures performed with robotic platform by the same single surgeon (EV). We selected the first cases performed by the surgeon with MP and SP system. The involved surgeon had already extensive background in laparoscopic surgery and advanced gynecological oncological procedures before the beginning of the study. The study period was between July 2010 and September 2011 for the MP system and between June 2024 and April 2025 for the SP system. We chose the time interval 2010–2011 for the MP cases, because MP was introduced in July 2010 at our Institution: in this way, we obtained that the involved surgeon did not have previous experience with a robotic MP system. We identified and included all consecutive robotic-assisted type A and type B1 hysterectomies[ 7 ] performed for benign gynecological condition or clinical stage I endometrial cancer. Variables of interest, such as patient demographics, surgical indications, operative times, and estimated blood loss, were retrieved from a prospectively maintained institutional database or collected from medical records by trained physicians. For the MP group, the da Vinci S system with three robotic arms was used. After establishing pneumoperitoneum, three 8-mm da Vinci robotic trocars were inserted, together with a 10-mm assistant trocar. For the SP group, a single 2.5-cm incision was made at the lower rim of the umbilicus and carried down to the fascia, which was then opened along the body’s longitudinal axis. The leading edge of the folded port was introduced into the incision with a downward motion while countertraction was applied with retractors. A small Intuitive access port was placed, and pneumoperitoneum was established by insufflating to 12 mm Hg. Statistical analysis Patient demographics and perioperative variables were summarized using standard descriptive statistics, as appropriate, overall and stratified based on the surgical platform: the single-port group (SP) and the Da Vinci S multi-port group (MP). Comparisons between the two groups were performed for continuous variables using the Student’s t-test, while differences in nominal categorical variables were assessed with the Chi-squared test. The cumulative sum (CUSUM) methodology was used for the learning curve analysis, and the primary endpoint was the operative time as best surrogate of learning and proficiency in using the robotic-surgical platform. All surgical cases were sorted by surgical dates. Data for each patient in the series were plotted on a chart from left to right on the x-axis, while the y-axis represents the CUSUM value of operative time. The CUSUM of the operative time was defined as follows: S 0 ​=0; S i = S i−1 + (OT i − \\(\\:\\overline{OT}\\) ). Where S i ​ is the CUSUM value for case i , S i−1 is the CUSUM value for the previous case. OT i ​ is the observed operative time for case i , and \\(\\:\\overline{OT}\\) is the mean operative time. The initial CUSUM value is 0 so S 0 ​=0 . Additionally, for both groups we plotted the raw operative times in chronological order, with a trend line representing the learning rate, modeled through linear regression. Statistical significance was considered with a p-value < 0.05. All statistical analyses were performed with R Statistical Software (v4.1.2; R Core Team 2024). Results Demographic and Clinical Characteristics A total of 73 consecutive patients who underwent Da Vinci S multi-port (MP) hysterectomy between July 2010 and September 2011, and 74 consecutive patients who underwent Da Vinci SP single-port (SP) hysterectomy between June 2024 and April 2025 were identified and included in the analysis. Demographic and clinical characteristics are summarized in Table 1 . The two groups did not differ in terms of surgical indications. In the MP group, 56 patients had endometrial cancer and 17 had benign gynecological conditions (including endometrial hyperplasia and uterine fibromatosis). In the SP group, 63 patients had endometrial cancer and 11 had benign conditions. The two groups differed significantly in terms of age (mean 58 years in MP vs. 63 years in SP, p < 0.05), while they were comparable in terms of body mass index (mean BMI 31 kg/m² in MP vs. 29 kg/m² in SP). Regarding operative time no statistically significant difference was observed (p > 0.05), with a mean duration of 117 minutes in the MP group and 114 minutes in the SP group. In contrast, the mean postoperative hemoglobin drop was significantly lower in the SP group (1.18 g/dL) compared to the MP group (2.07 g/dL), with this difference reaching statistical significance (p < 0.05). Table 1 Demographic and clinical characteristics Characteristics Da Vinci MP group (n = 73) Da Vinci SP group (n = 74) p Mean Age (years) 58 (SD: 10.9) 63 (SD: 12.2) 0.011 Mean BMI (kg/m²) 31 (SD: 8.1) 29 (SD: 7.3) 0.055 Diagnosis EC n = 56, Benign cases n = 17 EC n = 63, Benign cases n = 11 0.193 Mean operative Time (minutes) 117 (SD: 42.7) 114 (SD: 34.2) 0.658 Mean Hb drop (g/dL) 2.07 (SD: 1.1) 1.18 (SD: 0.91) 0.0000327 Learning Curve Analysis All procedures were performed by the same surgeon; the initial multi-port robot-assisted procedures were performed by the surgeon at a time when he had no prior experience in robot-assisted surgery. In contrast, the first SP cases were carried out after the surgeon had already gained substantial experience with multi-port robot-assisted surgery, although he had no prior clinical experience specifically with the SP platform. A marked reduction in operative time was observed with increasing experience in the MP group, showing a learning rate of approximately − 0.3 minutes per case, indicating a steeper learning curve (Fig. 1 A). Conversely, the SP group demonstrates a much more gradual decline in operative time, with a learning rate of − 0.009 minutes per case, reflecting a nearly flat learning curve (Fig. 1 B). The CUSUM analysis of operative times in the MP group (Fig. 2 A) identifies three distinct phases: A learning phase, characterized by a steep upward slope of the curve, corresponding to longer-than-average operative times. A proficiency phase, where the curve continues to rise but with a reduced slope. A mastery phase, marked by a downward slope, indicating operative times below the average. In the MP group, the transition to the mastery phase occurred after 50 cases. In contrast, the CUSUM curve of the SP group (Fig. 2 B) reveals a different pattern. The learning phase is shorter, with a rapid transition directly into the mastery phase without a clearly distinguishable proficiency phase. This mastery phase, marked by a consistent downward trend in the CUSUM curve, begins around case 13 and continues until case 25. Beyond this point, the curve flattens, indicating stabilization of operative performance and times oscillating closely around the mean. R 2 for MP and SP CUSUM curves is respectively 0.897 and 0.862. Discussion The comparative analysis of the learning curves showed that, although there was no statistically significant difference in mean operative times between the da Vinci S Multi-port and the da Vinci Single-Port platforms, the progression of surgical proficiency differed markedly. Specifically, the two platforms exhibited distinct learning curve patterns, indicating differences in the rate and trajectory of skill acquisition. The CUSUM learning curves showed that the SP system reached the mastery phase after 13 procedures, whereas the multi-port MP system required 50 (Fig. 2 B vs Fig. 2 A). This compressed trajectory is consistent with the principle of positive skill transfer: once a surgeon has internalized the psychomotor and cognitive workflows of console-based robot-assisted surgery, additional platforms that share similar hand-controller geometry, instrument kinematics and visual feedback impose a far smaller cognitive load. Consequently, only the platform-specific nuances (e.g. adaptation of operative angle and working distance, preventive hemostasis due to smaller instruments) must be learned, while core skills, camera control, three-dimensional depth perception, management of the controls and wristed articulation are already automatized. Comparable accelerations have been reported when surgeons move from MP to SP systems in radical prostatectomy [ 8 , 9 ] . The MP curve displays the canonical three phases described in surgical education theory. In the initial learning phase, the curve has a steep upward slope, indicating that the operator is taking longer than the mean operative time. As declarative knowledge is converted into automatized motor patterns, the curve enters the proficiency phase; where the curve begins to flatten but still has an upward slope. Finally, the mastery phase is reached when the curve reverses its slope and begins to decline, indicating that the operating times are below the mean; in this phase the performance is optimized, the times are reduced, and there is stability. This triphasic pattern mirrors the model of skill acquisition first articulated by Fitts & Posner[ 10 ]; and it reflects the typical one described in the literature for other surgical learning curves[ 11 – 14 ]. Although often indicated with different terminology, some studies present in the literature recognize the same three phases identified by us in the learning curve (learning, proficiency and mastery). Other studies identify only two phases in the learning process; nevertheless, the overall pattern of the curves remains the same[ 11 , 15 ]. Although the SP CUSUM curve does not display the canonical triphasic pattern seen in the MP group, its abbreviated learning trajectory suggests a more efficient adaptation process. The rapid transition from learning to mastery without a proficiency phase indicates that the surgeon was able to bypass much of the intermediate skill refinement typically required in the MP approach. This efficiency likely stems from prior robot-assisted experience and the ergonomic and technological advantages of the SP system. Moreover, this curve features a steady-state phase that is not present in the MP curve. This final segment can be interpreted as a phase of sustained performance or plateau, where the surgeon operates with consistent efficiency and minimal variability, The strengths of this study include the fact that, to date, the literature is devoid of analyses and comparisons between the learning curve of the Da Vinci SP with that of the Da Vinci multi-port system, not only in the field of gynecology but in surgical practice more broadly. Another strength of the study is the strong similarity between the patient populations being compared and the fact that all procedures were performed by the same surgeon, ensuring greater consistency and reliability of the results. The main limitation of the study is that all procedures were performed by a surgeon with many years of experience in laparoscopic surgery. Moreover, before starting to use the Da Vinci SP system, the surgeon had already performed over 1,500 procedures with the Da Vinci multi-port system and had prior experience with both single-site robot-assisted surgery and single-incision laparoscopic surgery[ 16 ]. This extensive prior experience may have influenced the observed learning curve for the Da Vinci SP. This limits the generalizability of the results to less experienced surgeons or training contexts. Conclusion The surgical experience gained with the Da Vinci multi-port system appears to be largely transferable to the Da Vinci SP platform. Future studies involving multiple surgeons with varying levels of experience are needed to validate these findings and provide a more comprehensive understanding of the learning dynamics with the SP system. Declarations Competing Interests Enrico Vizza serves as a proctor for Da Vinci surgical system training courses organized by Intuitive Surgical Inc. However, Intuitive Surgical Inc. had no role in the study design, data collection, analysis, interpretation, manuscript preparation, or decision to publish this work. The other authors declare that they have no conflict of interest. Funding Sources: the authors declare that no funds, grants, or other support were received during the preparation of this manuscript. Competing Interests: Enrico Vizza serves as a proctor for Da Vinci surgical system training courses organized by Intuitive Surgical Inc. However, Intuitive Surgical Inc. had no role in the study design, data collection, analysis, interpretation, manuscript preparation, or decision to publish this work. The other authors declare that they have no conflict of interest. Ethical approval : this study was performed in line with the principles of the Declaration of Helsinki. Approval was granted by the Ethics Committee of Lazio Area 5 (RS: 322/IRE/25). Consent to participate: informed consent was obtained from all individual participants included in the study. Consent for publication: not applicable (the manuscript does not contain individual person’s data in any form). Data availability: the datasets generated and/or analyzed during the current study are available from the corresponding author on reasonable request. Corresponding author: Riccardo Vizza MD, Unit of Obstetrics and Gynecology, Department of Surgery, Dentistry, Pediatrics, and Gynecology, AOUI Verona, University of Verona. Piazzale A. Stefani 1, 37126 - Verona, Italy. [email protected] Author Contribution study conception and design: GC, RV, EV. Literature review: SG, RV, AG. Acquisition of data: EV, GC, EB. Analysis and interpretation of data: SG, RV. Drafting of the manuscript: RV, SG, VB. Critical revision and final approval of the manuscript: EV, SU. All agree to be held accountable for all aspects of the work. Acknowledgments The authors wish to thank the nursing staff of the Gynecologic Oncology Unit of the Regina Elena National Cancer Institute for their assistance during the surgery. Data Availability the datasets generated and/or analyzed during the current study are available from the corresponding author on reasonable request. References Bianco FM, Dreifuss NH, Chang B, Schlottmann F, Cubisino A, Mangano A, Pavelko Y, Masrur MA, Giulianotti PC (2022) Robotic single‐port surgery: Preliminary experience in general surgery. Int J Med Robot 18:e2453. https://doi.org/10.1002/rcs.2453 Nguyen TT, Basilius J, Ali SN, Dobbs RW, Lee DI (2023) Single-Port Robotic Applications in Urology. J Endourol 37:688–699. https://doi.org/10.1089/end.2022.0600 Vizza R, Corrado G, Mancini E, Baiocco E, Russo M, Vincenzoni C, Bruno V, Falconer H, Vizza E (2025) Feasibility, safety, and efficacy of robotic single-port hysterectomy (R-SPH) using the da Vinci SP system in low-risk endometrial cancer: a pilot study. Minim Invasive Ther Allied Technol 1–8. https://doi.org/10.1080/13645706.2025.2538764 Vizza E, Giannini A, Bruno V, Baiocco E, Mancini E, Vizza R, Uccella S, Raspagliesi F, Bogani G (2025) Robotic-assisted single-port and multi-port surgical staging in early-stage endometrial cancer: a propensity matched comparison. Eur J Surg Oncol 110269. https://doi.org/10.1016/j.ejso.2025.110269 Khan N, Abboudi H, Khan MS, Dasgupta P, Ahmed K (2014) Measuring the surgical ‘learning curve’: methods, variables and competency. BJU Int 113:504–508. https://doi.org/10.1111/bju.12197 World Medical Association Declaration of Helsinki: Ethical Principles for Medical Research Involving Human Subjects. JAMA 310:2191. https://doi.org/10.1001/jama.2013.281053 Querleu D, Morrow CP (2008) Classification of radical hysterectomy. Lancet Oncol 9:297–303. https://doi.org/10.1016/S1470-2045(08)70074-3 Covas Moschovas M, Bhat S, Onol F, Rogers T, Patel V (2021) Early outcomes of single‐port robot‐assisted radical prostatectomy: lessons learned from the learning‐curve experience. BJU Int 127:114–121. https://doi.org/10.1111/bju.15158 Ramos-Carpinteyro R, Ferguson EL, Chavali JS, Geskin A, Soputro N, Kaouk J (2023) Single-port Transvesical Robot-assisted Radical Prostatectomy: The Surgical Learning Curve of the First 100 Cases. Urology 178:76–82. https://doi.org/10.1016/j.urology.2023.05.027 Fitts PM, Posner MI (1967) Human performance. Brooks/Cole Publishing Company Lombardi PM, Mazzola M, Veronesi V, Granieri S, Cioffi SPB, Baia M, Del Prete L, Bernasconi DP, Danelli P, Ferrari G (2023) Learning curve of laparoscopic cholecystectomy: a risk-adjusted cumulative summation (RA-CUSUM) analysis of six general surgery residents. Surg Endosc 37:8133–8143. https://doi.org/10.1007/s00464-023-10345-x Maayan O, Pajak A, Shahi P, Asada T, Subramanian T, Araghi K, Singh N, Korsun MK, Singh S, Tuma OC, Sheha ED, Dowdell JE, Qureshi SA, Iyer S (2023) Percutaneous Transforaminal Endoscopic Discectomy Learning Curve: A CuSum Analysis. Spine 48:1508–1516. https://doi.org/10.1097/BRS.0000000000004730 Gil PJ, Ruiz-Manzanera JJ, Ruiz de Angulo D, Munitiz V, Ferreras D, López V, Conesa A, Ortiz Á, Martínez de Haro LF, Ramírez P (2022) Learning Curve for Laparoscopic Sleeve Gastrectomy: a Cumulative Summation (CUSUM) Analysis. Obes Surg 32:2598–2604. https://doi.org/10.1007/s11695-022-06145-2 Li X, Guo S, Yao K, Ge Z, Li Y, Hu J, Xia H (2025) Learning curve of transanal minimally invasive surgery for rectal neoplasm. Front Oncol 15:1545589. https://doi.org/10.3389/fonc.2025.1545589 Wu J, Wang Y, Huang Y, Long X, Tang J, Gu D (2025) Learning curve analysis of extraperitoneal single-site robotic-assisted radical prostatectomy: a CUSUM-based approach. J Robot Surg 19:49. https://doi.org/10.1007/s11701-024-02202-3 Vizza R, Corrado G, Bruno V, Baiocco E, Zorzato PC, Uccella S, Vizza E (2025) Robotic Single‐Port Versus Robotic Single‐Site Hysterectomy in Early Endometrial Cancer: A Case Control Study. Int J Med Robot 21:e70107. https://doi.org/10.1002/rcs.70107 Additional Declarations Competing interest reported. Enrico Vizza serves as a proctor for Da Vinci surgical system training courses organized by Intuitive Surgical Inc. However, Intuitive Surgical Inc. had no role in the study design, data collection, analysis, interpretation, manuscript preparation, or decision to publish this work. The other authors declare that they have no conflict of interest. Supplementary Files detailedreportenlearningcurvefinalversionsutxt.pdf Cite Share Download PDF Status: Published Journal Publication published 01 Nov, 2025 Read the published version in Journal of Robotic Surgery → Version 1 posted Editorial decision: Revision requested 06 Oct, 2025 Reviews received at journal 06 Oct, 2025 Reviews received at journal 06 Oct, 2025 Reviewers agreed at journal 05 Oct, 2025 Reviewers agreed at journal 05 Oct, 2025 Reviewers invited by journal 01 Oct, 2025 Editor assigned by journal 26 Sep, 2025 Submission checks completed at journal 26 Sep, 2025 First submitted to journal 22 Sep, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {\"props\":{\"pageProps\":{\"initialData\":{\"identity\":\"rs-7687838\",\"acceptedTermsAndConditions\":true,\"allowDirectSubmit\":false,\"archivedVersions\":[],\"articleType\":\"Research Article\",\"associatedPublications\":[],\"authors\":[{\"id\":525615408,\"identity\":\"2937e0e4-0e5f-49f1-a453-3684ee1b9dc2\",\"order_by\":0,\"name\":\"Riccardo Vizza\",\"email\":\"data:image/png;base64,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\",\"orcid\":\"\",\"institution\":\"University of Verona\",\"correspondingAuthor\":true,\"prefix\":\"\",\"firstName\":\"Riccardo\",\"middleName\":\"\",\"lastName\":\"Vizza\",\"suffix\":\"\"},{\"id\":525615409,\"identity\":\"9a7b42f0-a889-40ee-a5ab-636d876925c3\",\"order_by\":1,\"name\":\"Simone Garzon\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"University of Verona\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Simone\",\"middleName\":\"\",\"lastName\":\"Garzon\",\"suffix\":\"\"},{\"id\":525615410,\"identity\":\"e9e73d53-3e90-4699-9701-a37e3825f887\",\"order_by\":2,\"name\":\"Giacomo Corrado\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"UOC Ginecologia Oncologica, Fondazione Policlinico Universitario A. 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06:47:45\",\"extension\":\"jpg\",\"order_by\":1,\"title\":\"Figure 1\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":48813,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003e(A) Operating time as a function of the number of interventions in MP group (B) Operating time as a function of the number of interventions in SP group\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"1.jpg\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7687838/v1/5200453517527fce5aa2fdde.jpg\"},{\"id\":93558191,\"identity\":\"395ba0cb-2dd4-40f1-ac27-5ff4d411ce71\",\"added_by\":\"auto\",\"created_at\":\"2025-10-15 06:55:45\",\"extension\":\"jpg\",\"order_by\":2,\"title\":\"Figure 2\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":80379,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003e(A) CUSUM Analysis of Operative Time for MP group (B) CUSUM Analysis of Operative Time for SP group\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"2.jpg\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7687838/v1/7e6464a15cfa64aeb2235acb.jpg\"},{\"id\":95039783,\"identity\":\"2466bcdd-bc92-4615-886c-29b0560e02cb\",\"added_by\":\"auto\",\"created_at\":\"2025-11-03 16:02:21\",\"extension\":\"pdf\",\"order_by\":0,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"manuscript-pdf\",\"size\":641536,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"manuscript.pdf\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7687838/v1/cc7bcaef-96b4-4796-a781-d016328dc249.pdf\"},{\"id\":93558455,\"identity\":\"53067f38-69f6-4cea-951d-035932870ca4\",\"added_by\":\"auto\",\"created_at\":\"2025-10-15 07:03:46\",\"extension\":\"pdf\",\"order_by\":1,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"supplement\",\"size\":357342,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"detailedreportenlearningcurvefinalversionsutxt.pdf\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7687838/v1/b07ce49391ed7fa4b4b93d28.pdf\"}],\"financialInterests\":\"Competing interest reported. Enrico Vizza serves as a proctor for Da Vinci surgical system training courses organized by Intuitive Surgical Inc. However, Intuitive Surgical Inc. had no role in the study design, data collection, analysis, interpretation, manuscript preparation, or decision to publish this work. The other authors declare that they have no conflict of interest.\",\"formattedTitle\":\"Attention to the Editor - Similarity Report- Evaluating the Learning Curve in Robot-Assisted Laparoscopic Total Hysterectomy: Single-Port versus Multi-port Da Vinci Platforms\",\"fulltext\":[{\"header\":\"Introduction\",\"content\":\"\\u003cp\\u003eThe Da Vinci SP\\u0026copy; is a specialized variant of the Da Vinci surgical system tailored for single-port surgery. It utilizes a single robotic arm that passes through one small skin incision. This is made possible by its design, which includes a single flexible camera and three multi-articulated robotic instruments. All these instruments employ two points of articulation within the body, allowing for better angulation toward the surgical field.\\u003c/p\\u003e\\u003cp\\u003eThe Da Vinci Single Port by Intuitive Surgical, Inc. (Sunnyvale, CA, USA) received approval for gynecologic surgery in South Korea in 2019 and in Japan in 2022. In the United States, while SP was approved in 2019 for select otolaryngology and urology procedures, the approval for gynecologic surgery is still pending. In the EU, the Da Vinci SP has been approved for gynecological procedures in 2024.\\u003c/p\\u003e\\u003cp\\u003eSince 2019, the SP system has been demonstrated feasible for several major robot-assisted operations. SP surgery technique was introduced into clinical practice to perform cholecystectomy, colorectal surgery, urological and gynecological surgery, with encouraging preliminary results[\\u003cspan additionalcitationids=\\\"CR2 CR3\\\" citationid=\\\"CR1\\\" class=\\\"CitationRef\\\"\\u003e1\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR4\\\" class=\\\"CitationRef\\\"\\u003e4\\u003c/span\\u003e].\\u003c/p\\u003e\\u003cp\\u003eGiven that the SP system was introduced after widespread adoption of the MP platform, it is crucial to understand whether the competencies gained with MP robot-assisted surgery are transferable to SP procedures. This has important implications for surgical training, patient safety, and the efficient integration of new technology into clinical practice. In this context, analyzing the learning curve (LC) becomes particularly relevant, as it represents the relationship between a surgeon\\u0026rsquo;s experience and patient outcomes, and it is crucial to estimate the number of patients at risk of suboptimal outcomes during the learning process[\\u003cspan citationid=\\\"CR5\\\" class=\\\"CitationRef\\\"\\u003e5\\u003c/span\\u003e]. This study aims to define the learning curve for the SP platform and the MP platform, with particular attention to how prior MP experience influences SP performance.\\u003c/p\\u003e\"},{\"header\":\"Methods\",\"content\":\"\\u003cp\\u003eWe conducted a retrospective study to compare the learning curve for the procedure of total hysterectomy performed using the Da Vinci SP\\u0026copy; (Intuitive Surgical, Inc., Sunnyvale, CA, USA) system versus the Da Vinci S Multi-port\\u0026copy; (Intuitive Surgical, Inc., Sunnyvale, CA, USA) system. The study was approved by the Ethics Committee of the Regina Elena National Cancer Institute of Rome, Italy (RS: 322/IRE/25). Informed consent to surgical intervention, including consent for the retrospective use of clinical data, was obtained from all the patients in accordance with local and international legislation (Declaration of Helsinki)[\\u003cspan citationid=\\\"CR6\\\" class=\\\"CitationRef\\\"\\u003e6\\u003c/span\\u003e].\\u003c/p\\u003e\\u003cp\\u003eWe retrospectively reviewed the prospectively maintained operative room database and identified all consecutive total hysterectomy procedures performed with robotic platform by the same single surgeon (EV). We selected the first cases performed by the surgeon with MP and SP system. The involved surgeon had already extensive background in laparoscopic surgery and advanced gynecological oncological procedures before the beginning of the study. The study period was between July 2010 and September 2011 for the MP system and between June 2024 and April 2025 for the SP system. We chose the time interval 2010\\u0026ndash;2011 for the MP cases, because MP was introduced in July 2010 at our Institution: in this way, we obtained that the involved surgeon did not have previous experience with a robotic MP system.\\u003c/p\\u003e\\u003cp\\u003eWe identified and included all consecutive robotic-assisted type A and type B1 hysterectomies[\\u003cspan citationid=\\\"CR7\\\" class=\\\"CitationRef\\\"\\u003e7\\u003c/span\\u003e] performed for benign gynecological condition or clinical stage I endometrial cancer. Variables of interest, such as patient demographics, surgical indications, operative times, and estimated blood loss, were retrieved from a prospectively maintained institutional database or collected from medical records by trained physicians.\\u003c/p\\u003e\\u003cp\\u003eFor the MP group, the da Vinci S system with three robotic arms was used. After establishing pneumoperitoneum, three 8-mm da Vinci robotic trocars were inserted, together with a 10-mm assistant trocar.\\u003c/p\\u003e\\u003cp\\u003eFor the SP group, a single 2.5-cm incision was made at the lower rim of the umbilicus and carried down to the fascia, which was then opened along the body\\u0026rsquo;s longitudinal axis. The leading edge of the folded port was introduced into the incision with a downward motion while countertraction was applied with retractors. A small Intuitive access port was placed, and pneumoperitoneum was established by insufflating to 12 mm Hg.\\u003c/p\\u003e\\u003cdiv id=\\\"Sec3\\\" class=\\\"Section2\\\"\\u003e\\u003ch2\\u003eStatistical analysis\\u003c/h2\\u003e\\u003cp\\u003ePatient demographics and perioperative variables were summarized using standard descriptive statistics, as appropriate, overall and stratified based on the surgical platform: the single-port group (SP) and the Da Vinci S multi-port group (MP). Comparisons between the two groups were performed for continuous variables using the Student\\u0026rsquo;s t-test, while differences in nominal categorical variables were assessed with the Chi-squared test.\\u003c/p\\u003e\\u003cp\\u003eThe cumulative sum (CUSUM) methodology was used for the learning curve analysis, and the primary endpoint was the operative time as best surrogate of learning and proficiency in using the robotic-surgical platform. All surgical cases were sorted by surgical dates. Data for each patient in the series were plotted on a chart from left to right on the x-axis, while the y-axis represents the CUSUM value of operative time. The CUSUM of the operative time was defined as follows: \\u003cem\\u003eS\\u003c/em\\u003e\\u003csub\\u003e\\u003cem\\u003e0\\u003c/em\\u003e\\u003c/sub\\u003e\\u003cem\\u003e​=0; S\\u003c/em\\u003e\\u003csub\\u003e\\u003cem\\u003ei\\u003c/em\\u003e\\u003c/sub\\u003e \\u003cem\\u003e= S\\u003c/em\\u003e\\u003csub\\u003e\\u003cem\\u003ei\\u0026minus;1\\u003c/em\\u003e\\u003c/sub\\u003e \\u003cem\\u003e+ (OT\\u003c/em\\u003e\\u003csub\\u003e\\u003cem\\u003ei\\u003c/em\\u003e\\u003c/sub\\u003e \\u003cem\\u003e\\u0026minus;\\u003c/em\\u003e \\u003cspan class=\\\"InlineEquation\\\"\\u003e\\u003cspan class=\\\"mathinline\\\"\\u003e\\\\(\\\\:\\\\overline{OT}\\\\)\\u003c/span\\u003e\\u003c/span\\u003e\\u003cem\\u003e).\\u003c/em\\u003e Where \\u003cem\\u003eS\\u003c/em\\u003e\\u003csub\\u003e\\u003cem\\u003ei\\u003c/em\\u003e​\\u003c/sub\\u003e is the CUSUM value for case \\u003cem\\u003ei\\u003c/em\\u003e, S\\u003csub\\u003ei\\u0026minus;1\\u003c/sub\\u003e is the CUSUM value for the previous case. \\u003cem\\u003eOT\\u003c/em\\u003e\\u003csub\\u003e\\u003cem\\u003ei\\u003c/em\\u003e\\u003c/sub\\u003e​ is the observed operative time for case \\u003cem\\u003ei\\u003c/em\\u003e, and \\u003cspan class=\\\"InlineEquation\\\"\\u003e\\u003cspan class=\\\"mathinline\\\"\\u003e\\\\(\\\\:\\\\overline{OT}\\\\)\\u003c/span\\u003e\\u003c/span\\u003e is the mean operative time. The initial CUSUM value is 0 so \\u003cem\\u003eS\\u003c/em\\u003e\\u003csub\\u003e\\u003cem\\u003e0\\u003c/em\\u003e\\u003c/sub\\u003e\\u003cem\\u003e​=0\\u003c/em\\u003e. Additionally, for both groups we plotted the raw operative times in chronological order, with a trend line representing the learning rate, modeled through linear regression.\\u003c/p\\u003e\\u003cp\\u003eStatistical significance was considered with a p-value\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.05. All statistical analyses were performed with R Statistical Software (v4.1.2; R Core Team 2024).\\u003c/p\\u003e\\u003c/div\\u003e\"},{\"header\":\"Results\",\"content\":\"\\u003cp\\u003eDemographic and Clinical Characteristics\\u003c/p\\u003e\\u003cp\\u003eA total of 73 consecutive patients who underwent Da Vinci S multi-port (MP) hysterectomy between July 2010 and September 2011, and 74 consecutive patients who underwent Da Vinci SP single-port (SP) hysterectomy between June 2024 and April 2025 were identified and included in the analysis. Demographic and clinical characteristics are summarized in Table\\u0026nbsp;\\u003cspan refid=\\\"Tab1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e.\\u003c/p\\u003e\\u003cp\\u003eThe two groups did not differ in terms of surgical indications. In the MP group, 56 patients had endometrial cancer and 17 had benign gynecological conditions (including endometrial hyperplasia and uterine fibromatosis). In the SP group, 63 patients had endometrial cancer and 11 had benign conditions. The two groups differed significantly in terms of age (mean 58 years in MP vs. 63 years in SP, p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.05), while they were comparable in terms of body mass index (mean BMI 31 kg/m\\u0026sup2; in MP vs. 29 kg/m\\u0026sup2; in SP). Regarding operative time no statistically significant difference was observed (p\\u0026thinsp;\\u0026gt;\\u0026thinsp;0.05), with a mean duration of 117 minutes in the MP group and 114 minutes in the SP group. In contrast, the mean postoperative hemoglobin drop was significantly lower in the SP group (1.18 g/dL) compared to the MP group (2.07 g/dL), with this difference reaching statistical significance (p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.05).\\u003c/p\\u003e\\u003cp\\u003e\\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003ctable float=\\\"Yes\\\" id=\\\"Tab1\\\" border=\\\"1\\\"\\u003e\\u003ccaption language=\\\"En\\\"\\u003e\\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 1\\u003c/div\\u003e\\u003cdiv class=\\\"CaptionContent\\\"\\u003e\\u003cp\\u003eDemographic and clinical characteristics\\u003c/p\\u003e\\u003c/div\\u003e\\u003c/caption\\u003e\\u003ccolgroup cols=\\\"4\\\"\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c1\\\" colnum=\\\"1\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c2\\\" colnum=\\\"2\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c3\\\" colnum=\\\"3\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c4\\\" colnum=\\\"4\\\"\\u003e\\u003c/div\\u003e\\u003cthead\\u003e\\u003ctr\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eCharacteristics\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003eDa Vinci MP group (n\\u0026thinsp;=\\u0026thinsp;73)\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003eDa Vinci SP group (n\\u0026thinsp;=\\u0026thinsp;74)\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003ep\\u003c/p\\u003e\\u003c/th\\u003e\\u003c/tr\\u003e\\u003c/thead\\u003e\\u003ctbody\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eMean Age (years)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e58 (SD: 10.9)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e63 (SD: 12.2)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e0.011\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eMean BMI (kg/m\\u0026sup2;)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e31 (SD: 8.1)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e29 (SD: 7.3)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e0.055\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eDiagnosis\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003eEC n\\u0026thinsp;=\\u0026thinsp;56, Benign cases n\\u0026thinsp;=\\u0026thinsp;17\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003eEC n\\u0026thinsp;=\\u0026thinsp;63, Benign cases n\\u0026thinsp;=\\u0026thinsp;11\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e0.193\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eMean operative Time (minutes)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e117 (SD: 42.7)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e114 (SD: 34.2)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e0.658\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eMean Hb drop (g/dL)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e2.07 (SD: 1.1)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e1.18 (SD: 0.91)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e0.0000327\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003c/tbody\\u003e\\u003c/colgroup\\u003e\\u003c/table\\u003e\\u003c/div\\u003e\\u003c/p\\u003e\\u003cp\\u003eLearning Curve Analysis\\u003c/p\\u003e\\u003cp\\u003eAll procedures were performed by the same surgeon; the initial multi-port robot-assisted procedures were performed by the surgeon at a time when he had no prior experience in robot-assisted surgery. In contrast, the first SP cases were carried out after the surgeon had already gained substantial experience with multi-port robot-assisted surgery, although he had no prior clinical experience specifically with the SP platform.\\u003c/p\\u003e\\u003cp\\u003eA marked reduction in operative time was observed with increasing experience in the MP group, showing a learning rate of approximately \\u0026minus;\\u0026thinsp;0.3 minutes per case, indicating a steeper learning curve (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003eA). Conversely, the SP group demonstrates a much more gradual decline in operative time, with a learning rate of \\u0026minus;\\u0026thinsp;0.009 minutes per case, reflecting a nearly flat learning curve (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003eB). The CUSUM analysis of operative times in the MP group (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003eA) identifies three distinct phases: A learning phase, characterized by a steep upward slope of the curve, corresponding to longer-than-average operative times. A proficiency phase, where the curve continues to rise but with a reduced slope. A mastery phase, marked by a downward slope, indicating operative times below the average.\\u003c/p\\u003e\\u003cp\\u003eIn the MP group, the transition to the mastery phase occurred after 50 cases. In contrast, the CUSUM curve of the SP group (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003eB) reveals a different pattern. The learning phase is shorter, with a rapid transition directly into the mastery phase without a clearly distinguishable proficiency phase. This mastery phase, marked by a consistent downward trend in the CUSUM curve, begins around case 13 and continues until case 25. Beyond this point, the curve flattens, indicating stabilization of operative performance and times oscillating closely around the mean. R\\u003csup\\u003e2\\u003c/sup\\u003e for MP and SP CUSUM curves is respectively 0.897 and 0.862.\\u003c/p\\u003e\\u003cp\\u003e\\u003c/p\\u003e\\u003cp\\u003e\\u003c/p\\u003e\"},{\"header\":\"Discussion\",\"content\":\"\\u003cp\\u003eThe comparative analysis of the learning curves showed that, although there was no statistically significant difference in mean operative times between the da Vinci S Multi-port and the da Vinci Single-Port platforms, the progression of surgical proficiency differed markedly. Specifically, the two platforms exhibited distinct learning curve patterns, indicating differences in the rate and trajectory of skill acquisition.\\u003c/p\\u003e\\u003cp\\u003eThe CUSUM learning curves showed that the SP system reached the mastery phase after 13 procedures, whereas the multi-port MP system required 50 (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003eB vs Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003eA). This compressed trajectory is consistent with the principle of positive skill transfer: once a surgeon has internalized the psychomotor and cognitive workflows of console-based robot-assisted surgery, additional platforms that share similar hand-controller geometry, instrument kinematics and visual feedback impose a far smaller cognitive load. Consequently, only the platform-specific nuances (e.g. adaptation of operative angle and working distance, preventive hemostasis due to smaller instruments) must be learned, while core skills, camera control, three-dimensional depth perception, management of the controls and wristed articulation are already automatized. Comparable accelerations have been reported when surgeons move from MP to SP systems in radical prostatectomy [\\u003cspan citationid=\\\"CR8\\\" class=\\\"CitationRef\\\"\\u003e8\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR9\\\" class=\\\"CitationRef\\\"\\u003e9\\u003c/span\\u003e] .\\u003c/p\\u003e\\u003cp\\u003eThe MP curve displays the canonical three phases described in surgical education theory. In the initial learning phase, the curve has a steep upward slope, indicating that the operator is taking longer than the mean operative time. As declarative knowledge is converted into automatized motor patterns, the curve enters the proficiency phase; where the curve begins to flatten but still has an upward slope. Finally, the mastery phase is reached when the curve reverses its slope and begins to decline, indicating that the operating times are below the mean; in this phase the performance is optimized, the times are reduced, and there is stability. This triphasic pattern mirrors the model of skill acquisition first articulated by Fitts \\u0026amp; Posner[\\u003cspan citationid=\\\"CR10\\\" class=\\\"CitationRef\\\"\\u003e10\\u003c/span\\u003e]; and it reflects the typical one described in the literature for other surgical learning curves[\\u003cspan additionalcitationids=\\\"CR12 CR13\\\" citationid=\\\"CR11\\\" class=\\\"CitationRef\\\"\\u003e11\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR14\\\" class=\\\"CitationRef\\\"\\u003e14\\u003c/span\\u003e]. Although often indicated with different terminology, some studies present in the literature recognize the same three phases identified by us in the learning curve (learning, proficiency and mastery). Other studies identify only two phases in the learning process; nevertheless, the overall pattern of the curves remains the same[\\u003cspan citationid=\\\"CR11\\\" class=\\\"CitationRef\\\"\\u003e11\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR15\\\" class=\\\"CitationRef\\\"\\u003e15\\u003c/span\\u003e].\\u003c/p\\u003e\\u003cp\\u003eAlthough the SP CUSUM curve does not display the canonical triphasic pattern seen in the MP group, its abbreviated learning trajectory suggests a more efficient adaptation process. The rapid transition from learning to mastery without a proficiency phase indicates that the surgeon was able to bypass much of the intermediate skill refinement typically required in the MP approach. This efficiency likely stems from prior robot-assisted experience and the ergonomic and technological advantages of the SP system. Moreover, this curve features a steady-state phase that is not present in the MP curve. This final segment can be interpreted as a phase of sustained performance or plateau, where the surgeon operates with consistent efficiency and minimal variability,\\u003c/p\\u003e\\u003cp\\u003eThe strengths of this study include the fact that, to date, the literature is devoid of analyses and comparisons between the learning curve of the Da Vinci SP with that of the Da Vinci multi-port system, not only in the field of gynecology but in surgical practice more broadly. Another strength of the study is the strong similarity between the patient populations being compared and the fact that all procedures were performed by the same surgeon, ensuring greater consistency and reliability of the results. The main limitation of the study is that all procedures were performed by a surgeon with many years of experience in laparoscopic surgery. Moreover, before starting to use the Da Vinci SP system, the surgeon had already performed over 1,500 procedures with the Da Vinci multi-port system and had prior experience with both single-site robot-assisted surgery and single-incision laparoscopic surgery[\\u003cspan citationid=\\\"CR16\\\" class=\\\"CitationRef\\\"\\u003e16\\u003c/span\\u003e]. This extensive prior experience may have influenced the observed learning curve for the Da Vinci SP. This limits the generalizability of the results to less experienced surgeons or training contexts.\\u003c/p\\u003e\"},{\"header\":\"Conclusion\",\"content\":\"\\u003cp\\u003eThe surgical experience gained with the Da Vinci multi-port system appears to be largely transferable to the Da Vinci SP platform. Future studies involving multiple surgeons with varying levels of experience are needed to validate these findings and provide a more comprehensive understanding of the learning dynamics with the SP system.\\u003c/p\\u003e\"},{\"header\":\"Declarations\",\"content\":\"\\u003cp\\u003e\\u003cstrong\\u003eCompeting Interests\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eEnrico Vizza serves as a proctor for Da Vinci surgical system training courses organized by Intuitive Surgical Inc. However, Intuitive Surgical Inc. had no role in the study design, data collection, analysis, interpretation, manuscript preparation, or decision to publish this work. The other authors declare that they have no conflict of interest.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eFunding Sources:\\u0026nbsp;\\u003c/strong\\u003ethe authors declare that no funds, grants, or other support were received during the preparation of this manuscript.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eCompeting Interests:\\u003c/strong\\u003e Enrico Vizza serves as a proctor for Da Vinci surgical system training courses organized by Intuitive Surgical Inc. However, Intuitive Surgical Inc. had no role in the study design, data collection, analysis, interpretation, manuscript preparation, or decision to publish this work. The other authors declare that they have no conflict of interest.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cem\\u003e\\u003cstrong\\u003eEthical approval\\u003c/strong\\u003e\\u003c/em\\u003e\\u003cstrong\\u003e:\\u003c/strong\\u003e this study was performed in line with the principles of the Declaration of Helsinki. Approval was granted by the Ethics Committee of Lazio Area 5 (RS: 322/IRE/25).\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eConsent to participate:\\u003c/strong\\u003e informed consent was obtained from all individual participants included in the study.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eConsent for publication:\\u003c/strong\\u003e not applicable (the manuscript does not contain individual person\\u0026rsquo;s data in any form).\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eData availability:\\u0026nbsp;\\u003c/strong\\u003ethe datasets generated and/or analyzed during the current study are available from the corresponding author on reasonable request.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eCorresponding author:\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eRiccardo Vizza MD,\\u003c/p\\u003e\\n\\u003cp\\u003eUnit of Obstetrics and Gynecology, Department of Surgery, Dentistry, Pediatrics, and Gynecology, AOUI Verona, University of Verona.\\u003c/p\\u003e\\n\\u003cp\\u003ePiazzale A. Stefani 1, 37126 - Verona, Italy.\\u003c/p\\u003e\\n\\u003cp\\u003ericcardo.vizza@studenti.univr.it\\u003c/p\\u003e\\n\\u003ch2\\u003eAuthor Contribution\\u003c/h2\\u003e\\n\\u003cp\\u003estudy conception and design: GC, RV, EV. Literature review: SG, RV, AG. Acquisition of data: EV, GC, EB. Analysis and interpretation of data: SG, RV. Drafting of the manuscript: RV, SG, VB. Critical revision and final approval of the manuscript: EV, SU. All agree to be held accountable for all aspects of the work.\\u003c/p\\u003e\\n\\u003ch2\\u003eAcknowledgments\\u003c/h2\\u003e\\n\\u003cp\\u003eThe authors wish to thank the nursing staff of the Gynecologic Oncology Unit of the Regina Elena National Cancer Institute for their assistance during the surgery.\\u003c/p\\u003e\\n\\u003ch2\\u003eData Availability\\u003c/h2\\u003e\\n\\u003cp\\u003ethe datasets generated and/or analyzed during the current study are available from the corresponding author on reasonable request.\\u003c/p\\u003e\"},{\"header\":\"References\",\"content\":\"\\u003col\\u003e\\n\\u003cli\\u003eBianco FM, Dreifuss NH, Chang B, Schlottmann F, Cubisino A, Mangano A, Pavelko Y, Masrur MA, Giulianotti PC (2022) Robotic single‐port surgery: Preliminary experience in general surgery. Int J Med Robot 18:e2453. https://doi.org/10.1002/rcs.2453\\u003c/li\\u003e\\n\\u003cli\\u003eNguyen TT, Basilius J, Ali SN, Dobbs RW, Lee DI (2023) Single-Port Robotic Applications in Urology. J Endourol 37:688\\u0026ndash;699. https://doi.org/10.1089/end.2022.0600\\u003c/li\\u003e\\n\\u003cli\\u003eVizza R, Corrado G, Mancini E, Baiocco E, Russo M, Vincenzoni C, Bruno V, Falconer H, Vizza E (2025) Feasibility, safety, and efficacy of robotic single-port hysterectomy (R-SPH) using the da Vinci SP system in low-risk endometrial cancer: a pilot study. Minim Invasive Ther Allied Technol 1\\u0026ndash;8. https://doi.org/10.1080/13645706.2025.2538764\\u003c/li\\u003e\\n\\u003cli\\u003eVizza E, Giannini A, Bruno V, Baiocco E, Mancini E, Vizza R, Uccella S, Raspagliesi F, Bogani G (2025) Robotic-assisted single-port and multi-port surgical staging in early-stage endometrial cancer: a propensity matched comparison. Eur J Surg Oncol 110269. https://doi.org/10.1016/j.ejso.2025.110269\\u003c/li\\u003e\\n\\u003cli\\u003eKhan N, Abboudi H, Khan MS, Dasgupta P, Ahmed K (2014) Measuring the surgical \\u0026lsquo;learning curve\\u0026rsquo;: methods, variables and competency. BJU Int 113:504\\u0026ndash;508. https://doi.org/10.1111/bju.12197\\u003c/li\\u003e\\n\\u003cli\\u003eWorld Medical Association Declaration of Helsinki: Ethical Principles for Medical Research Involving Human Subjects. JAMA 310:2191. https://doi.org/10.1001/jama.2013.281053\\u003c/li\\u003e\\n\\u003cli\\u003eQuerleu D, Morrow CP (2008) Classification of radical hysterectomy. Lancet Oncol 9:297\\u0026ndash;303. https://doi.org/10.1016/S1470-2045(08)70074-3\\u003c/li\\u003e\\n\\u003cli\\u003eCovas Moschovas M, Bhat S, Onol F, Rogers T, Patel V (2021) Early outcomes of single‐port robot‐assisted radical prostatectomy: lessons learned from the learning‐curve experience. BJU Int 127:114\\u0026ndash;121. https://doi.org/10.1111/bju.15158\\u003c/li\\u003e\\n\\u003cli\\u003eRamos-Carpinteyro R, Ferguson EL, Chavali JS, Geskin A, Soputro N, Kaouk J (2023) Single-port Transvesical Robot-assisted Radical Prostatectomy: The Surgical Learning Curve of the First 100 Cases. Urology 178:76\\u0026ndash;82. https://doi.org/10.1016/j.urology.2023.05.027\\u003c/li\\u003e\\n\\u003cli\\u003eFitts PM, Posner MI (1967) Human performance. Brooks/Cole Publishing Company\\u003c/li\\u003e\\n\\u003cli\\u003eLombardi PM, Mazzola M, Veronesi V, Granieri S, Cioffi SPB, Baia M, Del Prete L, Bernasconi DP, Danelli P, Ferrari G (2023) Learning curve of laparoscopic cholecystectomy: a risk-adjusted cumulative summation (RA-CUSUM) analysis of six general surgery residents. Surg Endosc 37:8133\\u0026ndash;8143. https://doi.org/10.1007/s00464-023-10345-x\\u003c/li\\u003e\\n\\u003cli\\u003eMaayan O, Pajak A, Shahi P, Asada T, Subramanian T, Araghi K, Singh N, Korsun MK, Singh S, Tuma OC, Sheha ED, Dowdell JE, Qureshi SA, Iyer S (2023) Percutaneous Transforaminal Endoscopic Discectomy Learning Curve: A CuSum Analysis. Spine 48:1508\\u0026ndash;1516. https://doi.org/10.1097/BRS.0000000000004730\\u003c/li\\u003e\\n\\u003cli\\u003eGil PJ, Ruiz-Manzanera JJ, Ruiz de Angulo D, Munitiz V, Ferreras D, L\\u0026oacute;pez V, Conesa A, Ortiz \\u0026Aacute;, Mart\\u0026iacute;nez de Haro LF, Ram\\u0026iacute;rez P (2022) Learning Curve for Laparoscopic Sleeve Gastrectomy: a Cumulative Summation (CUSUM) Analysis. Obes Surg 32:2598\\u0026ndash;2604. https://doi.org/10.1007/s11695-022-06145-2\\u003c/li\\u003e\\n\\u003cli\\u003eLi X, Guo S, Yao K, Ge Z, Li Y, Hu J, Xia H (2025) Learning curve of transanal minimally invasive surgery for rectal neoplasm. Front Oncol 15:1545589. https://doi.org/10.3389/fonc.2025.1545589\\u003c/li\\u003e\\n\\u003cli\\u003eWu J, Wang Y, Huang Y, Long X, Tang J, Gu D (2025) Learning curve analysis of extraperitoneal single-site robotic-assisted radical prostatectomy: a CUSUM-based approach. J Robot Surg 19:49. https://doi.org/10.1007/s11701-024-02202-3\\u003c/li\\u003e\\n\\u003cli\\u003eVizza R, Corrado G, Bruno V, Baiocco E, Zorzato PC, Uccella S, Vizza E (2025) Robotic Single‐Port Versus Robotic Single‐Site Hysterectomy in Early Endometrial Cancer: A Case Control Study. Int J Med Robot 21:e70107. https://doi.org/10.1002/rcs.70107\\u003c/li\\u003e\\n\\u003c/ol\\u003e\"}],\"fulltextSource\":\"\",\"fullText\":\"\",\"funders\":[],\"hasAdminPriorityOnWorkflow\":false,\"hasManuscriptDocX\":true,\"hasOptedInToPreprint\":true,\"hasPassedJournalQc\":\"\",\"hasAnyPriority\":false,\"hideJournal\":false,\"highlight\":\"\",\"institution\":\"\",\"isAcceptedByJournal\":true,\"isAuthorSuppliedPdf\":false,\"isDeskRejected\":\"\",\"isHiddenFromSearch\":false,\"isInQc\":false,\"isInWorkflow\":false,\"isPdf\":false,\"isPdfUpToDate\":true,\"isWithdrawnOrRetracted\":false,\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"journal-of-robotic-surgery\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":false,\"externalIdentity\":\"jors\",\"sideBox\":\"Learn more about [Journal of Robotic Surgery](http://link.springer.com/journal/11701)\",\"snPcode\":\"11701\",\"submissionUrl\":\"https://submission.nature.com/new-submission/11701/3\",\"title\":\"Journal of Robotic Surgery\",\"twitterHandle\":\"\",\"acdcEnabled\":true,\"dfaEnabled\":true,\"editorialSystem\":\"em\",\"reportingPortfolio\":\"Springer Hybrid\",\"inReviewEnabled\":true,\"inReviewRevisionsEnabled\":false},\"keywords\":\"Learning Curve, Robot-Assisted Hysterectomy, Da Vinci Single-Port, Da Vinci Multi-port\",\"lastPublishedDoi\":\"10.21203/rs.3.rs-7687838/v1\",\"lastPublishedDoiUrl\":\"https://doi.org/10.21203/rs.3.rs-7687838/v1\",\"license\":{\"name\":\"CC BY 4.0\",\"url\":\"https://creativecommons.org/licenses/by/4.0/\"},\"manuscriptAbstract\":\"\\u003cp\\u003eThe aim of this study is to assess and compare the learning curves of the Da Vinci S Multi-port (MP) and Da Vinci Single-Port (SP) platforms for total hysterectomy. This is a retrospective comparative study on patients undergoing robot-assisted total hysterectomy (benign or early endometrial cancer indication) with the MP and SP systems. All operations were performed by the same surgeon, and cases performed with the SP system were performed after the MP system. Operating time was used as the main indicator of the learning curve, analyzed by the CUSUM method. Data were processed with R software, and differences between groups were evaluated with t-test and Chi-squared test (significance p \\u0026lt; 0.05). A total of 147 patients were analyzed (73 MP, 74 SP). Mean operating times were similar between the two groups (117 min MP vs 114 min SP). Hemoglobin drop was significantly lower in the SP group (−1.18 g/dL vs −2.07 g/dL, p \\u0026lt; 0.05). CUSUM analysis showed that the learning curve of the MP system reaches the mastery phase after 50 cases, while 13 cases are sufficient for the SP system when initiated after having mastered the MP system. The learning rate was -0.3 min/case for MP and -0.009 min/case for SP. In conclusion much of the experience gained with the Da Vinci Multiport System is transferable to the SP platform, allowing for a shorter learning curve and rapid achievement of surgical mastery with similar learning phases.\\u003c/p\\u003e\",\"manuscriptTitle\":\"Attention to the Editor - Similarity Report- Evaluating the Learning Curve in Robot-Assisted Laparoscopic Total Hysterectomy: Single-Port versus Multi-port Da Vinci Platforms\",\"msid\":\"\",\"msnumber\":\"\",\"nonDraftVersions\":[{\"code\":1,\"date\":\"2025-10-15 06:47:41\",\"doi\":\"10.21203/rs.3.rs-7687838/v1\",\"editorialEvents\":[{\"type\":\"communityComments\",\"content\":0},{\"type\":\"decision\",\"content\":\"Revision requested\",\"date\":\"2025-10-06T21:28:01+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"editorInvitedReview\",\"content\":\"\",\"date\":\"2025-10-06T15:21:21+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"editorInvitedReview\",\"content\":\"\",\"date\":\"2025-10-06T10:55:18+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"reviewerAgreed\",\"content\":\"61607838375485771626181065477785883572\",\"date\":\"2025-10-05T06:51:27+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"reviewerAgreed\",\"content\":\"117155721350732171435750779707594339264\",\"date\":\"2025-10-05T04:18:10+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"reviewersInvited\",\"content\":\"\",\"date\":\"2025-10-01T13:19:36+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"editorAssigned\",\"content\":\"\",\"date\":\"2025-09-26T12:02:55+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"checksComplete\",\"content\":\"\",\"date\":\"2025-09-26T06:26:20+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"submitted\",\"content\":\"Journal of Robotic Surgery\",\"date\":\"2025-09-22T21:48:46+00:00\",\"index\":\"\",\"fulltext\":\"\"}],\"status\":\"published\",\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"journal-of-robotic-surgery\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":false,\"externalIdentity\":\"jors\",\"sideBox\":\"Learn more about [Journal of Robotic Surgery](http://link.springer.com/journal/11701)\",\"snPcode\":\"11701\",\"submissionUrl\":\"https://submission.nature.com/new-submission/11701/3\",\"title\":\"Journal of Robotic Surgery\",\"twitterHandle\":\"\",\"acdcEnabled\":true,\"dfaEnabled\":true,\"editorialSystem\":\"em\",\"reportingPortfolio\":\"Springer Hybrid\",\"inReviewEnabled\":true,\"inReviewRevisionsEnabled\":false}}],\"origin\":\"\",\"ownerIdentity\":\"074fb381-092c-4aae-aa0f-4c16f97478f1\",\"owner\":[],\"postedDate\":\"October 15th, 2025\",\"published\":true,\"recentEditorialEvents\":[],\"rejectedJournal\":[],\"revision\":\"\",\"amendment\":\"\",\"status\":\"published-in-journal\",\"subjectAreas\":[],\"tags\":[],\"updatedAt\":\"2025-11-03T15:59:17+00:00\",\"versionOfRecord\":{\"articleIdentity\":\"rs-7687838\",\"link\":\"https://doi.org/10.1007/s11701-025-02928-8\",\"journal\":{\"identity\":\"journal-of-robotic-surgery\",\"isVorOnly\":false,\"title\":\"Journal of Robotic Surgery\"},\"publishedOn\":\"2025-11-01 15:56:57\",\"publishedOnDateReadable\":\"November 1st, 2025\"},\"versionCreatedAt\":\"2025-10-15 06:47:41\",\"video\":\"\",\"vorDoi\":\"10.1007/s11701-025-02928-8\",\"vorDoiUrl\":\"https://doi.org/10.1007/s11701-025-02928-8\",\"workflowStages\":[]},\"version\":\"v1\",\"identity\":\"rs-7687838\",\"journalConfig\":\"researchsquare\"},\"__N_SSP\":true},\"page\":\"/article/[identity]/[[...version]]\",\"query\":{\"redirect\":\"/article/rs-7687838\",\"identity\":\"rs-7687838\",\"version\":[\"v1\"]},\"buildId\":\"XKTyCvWXoU3ODBz1xrDgd\",\"isFallback\":false,\"isExperimentalCompile\":false,\"dynamicIds\":[84888],\"gssp\":true,\"scriptLoader\":[]}","source_license":"CC-BY-4.0","license_restricted":false}