Research hotspots and trends in robot-assisted surgery in pediatric urology: a bibliometric review based on CiteSpace visual analysis

Objective: This study aims to analyze the global research landscape of robot-assisted surgery in pediatric urology, identify developmental trends through visualization methods, and provide references and recommendations for future research directions.

Methods: A retrospective bibliometric analysis was conducted using literature retrieved from PubMed and Web of Science Core Collection, covering the period from January 2005 to March 2025. CiteSpace 6.4.R1 software was used for data processing and visualization. Analyses included publication trends, keyword co-occurrence, keyword clustering, and burst detection to construct knowledge maps and explore research dynamics.

Results: A total of 498 publications were included in the analysis. The number of publications showed a clear upward trend. The author collaboration network was relatively dense, with an identifiable core research team. The University of Chicago emerged as the leading institution in terms of publication volume in the field of robot-assisted surgery in pediatric urology. Gundeti MS was the most productive author, contributing 51 published papers to the field. Keyword clustering analysis revealed 10 clusters, and burst detection identified 20 keywords with significant citation bursts.

Conclusions: Current international research primarily focuses on disease classification and surgical techniques in robot-assisted surgery in pediatric urology. Future studies should continue to explore the clinical potential of robot-assisted surgery, broaden its application scope, and promote the diversified development of pediatric urology.

1 Introduction

Robot-assisted surgery (RAS) represents a significant advancement in the evolution of minimally invasive surgery techniques, enhancing surgical precision and elevating operative procedures to a new technological standard. Initially introduced in clinical practice in the United States (U.S.) (1), surgical robots were adopted in China in 2007 and have since been widely and effectively applied in fields such as adult urology (2). RAS is characterized by a high-definition optical imaging system and a highly flexible operating mechanism (3), offering several advantages including shortened hospital stays, reduced pain, improved cosmetic outcomes of the incision, and a lower incidence of postoperative complications (4). However, due to challenges such as limited instrument size, the wide age range of pediatric patients, and physiological and anatomical differences from adults, the application of RAS in the pediatric field has developed relatively slowly (5). In recent years, research interest in pediatric urologic RAS has been steadily increasing (6–8). Nevertheless, no comprehensive bibliometric analysis has yet been conducted to systematically summarize the current research landscape, key focus areas, and emerging trends in this field from a macro-level perspective.

Knowledge graphs (KGs) typically uses entities and relationships to model real-world concepts and their interconnections. Through visual graph representations, KGs can effectively illustrate the core structure, historical development, emerging frontiers, and overall knowledge architecture of a discipline, thereby promoting interdisciplinary integration. As a new analytical approach, KGs provide practical and valuable insights for academic research (9, 10). CiteSpace is a software based on co-citation analysis theory and pathfinding network algorithms, used for bibliometric and visual analysis of scientific literature in specific research domains (11). This study uses the CiteSpace version 6.4.R1 software to conduct a visual bibliometric analysis of global literature on RAS in pediatric urology over the past 20 years, with the aim of providing valuable references and strategic guidance for future research directions and evidence-based decision-making.

2. Materials and methods

2.1 Study design and ethics

This retrospective bibliometric analysis focused exclusively on published articles and did not involve any human participants or clinical trials. Therefore, ethical approval from an institutional review board was not required.

2.2 Data collection

Literature searches were conducted in PubMed database and Web of Science Core Collection database using advanced search strategies. The search scope was limited to publications classified as “Article” and “Review”, and restricted to the English language. The following subject terms were used: “robotic-assisted surgery” OR “robot-assisted laparoscopic” OR “robot-assisted laparoscopic ureterocalicostomy” OR “robot-assisted laparoscopic pyeloplasty (RALP)” OR “robot-assisted laparoscopic ureteral reimplantation” OR “robotic-assisted laparoscopic partial nephrectomy” OR “robotic ipsilateral UU” OR “robotic partial nephrectomy” AND “Children” OR “Toddlers” OR “Pediatrics” OR “Infants” OR “Adolescents.” The publication date range was set from January 1, 2005 to March 10, 2025.

2.3 Study inclusion and exclusion criteria

Inclusion criteria: (1) Publications focusing on RAS in pediatric urology that are publicly available. Exclusion criteria: (1) Studies not clearly involving pediatric and urologic RAS; (2) Conference abstracts, announcements, news reports, and other non-research content; (3) Incomplete announcements; (4) Duplicate publications; (5) Publications not in English. The literature screening process was independently conducted by two researchers. In cases of disagreement, a third researcher was consulted to reach a consensus.

2.4 Analytical methods

Literature from Web of Science Core Collection was exported in plain text format, while the literature from PubMed database was exported in NBIB format. Both files were saved as download_.txt and subsequently imported into CiteSpace 6.4.R1 for format conversion and deduplication before analysis. The Time Slicing was set from January 2005 to March 2025, with Years Per Slice set to 1. For Pruning, the options Pathfinder, Pruning sliced networks, and Pruning the merged networks were selected. The selected Node Types included Author, Institution, Country, and Keyword, while all other threshold parameters were kept at their default settings. In the resulting visual maps, the node size represents the co-occurrence frequency, node color indicates the corresponding year, and the lines between nodes represent collaborative relationships.

3 Results

3.1 Bibliometric analysis of publication years

A total of 943 articles were initially retrieved, and after applying the inclusion and exclusion criteria, 498 articles were included in the final analysis. The literature selection process is shown in Figure 1. Before 2010, the annual publication volume was relatively low, with fewer than 10 articles per year. From 2011 to 2016, a gradual increase was observed. Notably, there was a significant surge in publication volume during the periods 2018–2019 and 2023–2024. Overall, the number of publications has shown a steadily increasing trend, indicating growing academic interest in this field. The publication trend over the past 20 years in RAS in pediatric urology is illustrated in Figure 2.

Figure 1

Figure 1. PRISMA flow diagram of the literature selection process. The flowchart illustrates the identification, screening, eligibility assessment, and final inclusion of publications related to robot-assisted surgery in pediatric urology, detailing the number of records retrieved, excluded, and included at each stage.

Figure 2

Figure 2. Annual publication trends in robot-assisted surgery in pediatric urology (2005–2025). The line chart shows the yearly number of publications over time, with a fitted trend line indicating the overall growth trajectory of research output in this field.

3.2 Bibliometric analysis of institutions

Author affiliation information was recorded according to the institutional affiliation listed in each publication at the time of article submission/publication. As shown in Table 1, the institutions that have published ten or more articles are comprised mainly of academic and medical centers in the United States, Italy, and France. A node centrality greater than 0.1 indicates that the institution occupies a key position in the collaboration network. Higher centrality value suggest closer and more extensive collaborative relationships with other institutions.

Table 1

Table 1. Research institutions with a publication record of at least 10 papers.

3.3 Bibliometric analysis of authors

The top 10 most productive authors are listed in Table 2. The number of core authors was calculated using Price's Law (12) based on the formula: N = 0.749 × √Npmax, where Npmax represents the number of publications by the most prolific author. Authors with more than N publications are defined as core authors. Among the 498 included articles, there were a total of 624 authors. The author with the highest number of publications was Mohan S. Gundeti from the University of Chicago, with 51 publications. Therefore, N = 0.749 × √51 ≈ 6. Consequently, 53 authors with more than 6 publications were identified as core contributors. The author collaboration network is shown in Figure 3. Several core research teams have formed in this field, with key representatives including Mohan S. Gundeti, Ciro Esposito, Chester J. Koh, Maria Escolino, and Lorenzo Masieri. These teams have established strong collaborative networks both within and across groups, indicating a robust academic cooperation in this area of study.

Table 2

Table 2. Top 10 authors in terms of number of publications.

Figure 3

Figure 3. Co-authorship network of authors in robot-assisted surgery in pediatric urology. The network visualization generated by CiteSpace displays collaborations among authors. Node size represents publication frequency, while links indicate co-authorship relationships. Node colors correspond to the time of publication.

3.4 Bibliometric analysis of keyword co-occurrence

Keyword co-occurrence analysis is a statistical method used to analyze the frequency with which keywords appear together to reveal the intensity of their associations and identify research hotspots (13). The frequency and distribution of keywords reflect the current state of the research field and emerging trends. In the co-occurrence network, larger nodes represent keywords with higher frequencies, indicating greater academic attention (14). These keywords indicate that the current research in RAS in pediatric urology mainly focuses on diseases types and postoperative outcomes. The keyword co-occurrence network consisted of 389 nodes and 873 links, with a network density of 0.0116, as shown in Figure 4.

Figure 4

Figure 4. Keyword co-occurrence network in robot-assisted surgery in pediatric urology. This network map illustrates the relationships among high-frequency keywords. Larger nodes indicate higher occurrence frequency, and links represent co-occurrence strength. Colors reflect the temporal evolution of research topics.

3.5 Bibliometric analysis of keyword clustering

To further explore the thematic structure of the research field, a clustering analysis was conducted on the keyword co-occurrence network, resulting in ten major clusters, as shown in Figure 5 and Table 3. The modularity of the network is quantified by the Modularity Q index, which ranges from 0 to 1. A Q value of greater than 0.3 indicates a significant clustering structure within the network, while values closer to 1 suggest more distinct and well-separated clusters, reflecting better clustering performance. Silhouette S is a contour coefficient used to measure network homogeneity, with values ranging from −1 to 1. A silhouette value greater than 0.7 indicates strong clustering with high homogeneity. As shown in Figure 5, the keyword clustering analysis yielded 10 keyword clusters with a Modularity Q value of 0.7947 and a Silhouette S of 0.9382, indicating a well-defined modular structure with excellent homogeneity and clustering validity. As shown in Figure 5, these clusters can be broadly grouped into the following thematic categories: ① Disease types: Clusters #0 and #7 ② Surgical sites: Clusters #1, #5, #6, and #8 ③ Surgical approaches: Clusters #2, #3, #4, and #9. There is a certain degree of overlap between the clusters, indicating close and relevant connections among different research areas. The Size value listed in Table 3 refers to the number of publications within each cluster, while the Silhouette value indicates the degree of internal cohesion—the higher the value, the greater the homogeneity within that cluster.

Figure 5

Figure 5. Keyword clustering analysis based on CiteSpace. The clustered network identifies major research themes in robot-assisted pediatric urology. Each cluster is labeled with representative keywords, and the colors distinguish different thematic groups, reflecting the knowledge structure of the field.

Table 3

Table 3. Keyword clustering table.

3.6 Bibliometric analysis of keywords with citation bursts

Citation burst detection identifies keywords that exhibit a sharp increase in usage frequency over a short time, signaling emerging trends or research frontiers. The year indicates the initial appearance of the corresponding keyword in the literature. The start and end years denote the time span of the burst, and the length of the red line in the visualization corresponds to the duration of the burst period. As shown in Figure 6, research before 2010 mainly focused on the comparison between open and minimally invasive surgery. After 2010, the emphasis shifted toward evaluating the outcomes of RAS. In recent years, increasing attention has been given to the application of robotic techniques in treating conditions such as UPJO and primary obstructive megaureter (POM).

Figure 6

Figure 6. Top 20 keywords with the strongest citation bursts from 2005 to 2025. The burst detection analysis highlights keywords that experienced significant increases in citation frequency over specific periods, indicating emerging trends and research frontiers in robot-assisted surgery in pediatric urology.

4 Discussion

4.1 General characteristics

This study is the first to apply CiteSpace to bibliometrically analyze research related to robotic-assisted surgery in pediatric urology. A total of 498 publications were retrieved from PubMed and the Web of Science Core Collection between January 1, 2005, and March 10, 2025. The annual number of publications has shown a steady upward trajectory, indicating that this field is rapidly expanding and continuously gaining academic attention. The distribution of the most prolific institutions (≥10 publications) is detailed in Table 1. Of the twelve centers that meet this criterion, eight are located in the United States, while three and one are from Italy and France, respectively. The University of Chicago leads with 47 publications (9.4%), highlighting its dominant contribution. As the birthplace and primary application market of surgical robotic systems, the United States benefits from first-mover advantage, strong industry–academia integration, and high-volume pediatric centers supported by stable funding and specialized research infrastructure (15). These structural advantages have facilitated early adoption, rapid technical maturation, and sustained research productivity. Similarly, several European countries demonstrate strong publication output, which may reflect differences in resource allocation, investment capacity, regulatory environments, and timing of technological diffusion across regions. Strengthening global academic collaboration and knowledge exchange may therefore support more equitable dissemination and effective implementation of pediatric robotic surgery internationally.

Despite these advances, evidence gaps persist. Most current clinical studies remain single-center, observational, and short-term in nature, and there continues to be a lack of large-scale comparative outcome studies, rigorous cost-effectiveness analyses, and long-term functional outcome evaluation. Future studies should emphasize multicenter collaboration, standardized outcome frameworks, and prospective long-term follow-up to generate higher-level clinical evidence that can better inform value-based decision-making.

Beyond mapping research structure, these bibliometric findings also provide important clinical and translational insight. High-output countries and institutions may serve as reference models to support global capacity-building, competency-based training, and standardized robotic curriculum development in pediatric urology. Moreover, observed publication patterns may assist policymakers in rational resource allocation and in promoting more equitable access to robotic platforms worldwide. Advancing research beyond technical feasibility toward comparative effectiveness trials, economic evaluation, and long-term functional outcome assessment will be essential to accelerate evidence-based and sustainable implementation of robotic surgery in pediatric urology.

4.2 Research topics and emerging trends

Keywords reflect the core thematic structure of published studies and serve as an effective indicator of evolving research priorities. The keyword co-occurrence and clustering analyses in this study demonstrate that research on pediatric robotic-assisted surgery over the past two decades has been mainly concentrated in hydronephrosis-related conditions (UPJO and VUR), renal duplication anomalies, urachal cysts, and neurogenic bladder (NB). Correspondingly, procedure-related hotspots have been centered on RALP, robotic partial nephrectomy, nephron-sparing surgery, pediatric kidney transplantation, ureteral reimplantation, and artificial urinary sphincter–related interventions. These patterns reveal that the research focus has gradually shifted from early adoption of robotic platforms in relatively standardized reconstructive procedures toward broader expansion into more complex anatomical corrections and functional reconstructive domains.

Neurogenic bladder (NB) also represents a growing niche within this research landscape. In addition to procedures such as artificial urinary sphincter implantation, bladder augmentation, and ureteral reimplantation, two studies have reported rare yet innovative robotic bladder neck reconstruction techniques in pediatric patients (robotic bladder neck plication and a robotic-assisted “keel” reconstruction approach), suggesting that novel reconstructive innovations may continue to emerge in this space (16, 17). The accumulation of such specialized indications indicates a potential future trajectory toward increased technical diversification and refinement within pediatric robotic urology.

Overall, these keyword-based patterns reflect an ongoing evolution from feasibility-focused adoption toward procedure expansion, surgical complexity escalation, and innovation-driven refinement. As robotic capabilities continue to improve, future research trends are likely to further integrate advanced reconstructive techniques, broaden indication portfolios, and develop standardized frameworks that support the safe and evidence-based implementation of robotic platforms in pediatric urology worldwide.

5 Limitations

Our study has several limitations that should be taken into account when interpreting the findings. First, the data sources were restricted to PubMed and Web of Science. Although these are widely recognized and authoritative databases, the exclusion of other major sources such as Embase, Scopus, and regional or non-English journals may introduce database coverage bias and lead to incomplete capture of the global research output. Second, the bibliometric analysis relied on keyword-based retrieval, which is inherently sensitive to the selection of search terms and may be affected by language bias and terminology evolution over time. This may result in the omission of relevant studies that apply alternative expressions. Third, the interpretation of network visualization and cluster relationships may still involve subjective judgment. Although CiteSpace includes multiple algorithms to enhance structural robustness, discrepancies among them may affect the stability and consistency of the identified knowledge structures. Collectively, these factors may influence the comprehensiveness and interpretability of our findings.

6 Conclusion

These findings also carry important clinical implications, underscoring the need to strengthen global collaboration, improve equity in resource allocation, and accelerate capacity-building in regions with emerging robotic adoption. Prioritizing multicenter comparative outcome research, cost-effectiveness evaluation, and long-term functional follow-up should be considered essential future directions to support evidence-based and sustainable implementation of pediatric robotic surgery worldwide.

Author contributions

JH: Writing – original draft, Writing – review & editing, Data curation. DL: Writing – original draft, Writing – review & editing, Software, Supervision.

Funding

The author(s) declared that financial support was not received for this work and/or its publication.

Acknowledgments

We sincerely thank Dr. Lili Liu from the West China Second University Hospital for her valuable guidance and constructive suggestions on the study design.

Conflict of interest

The author(s) declared that this work was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Generative AI statement

The author(s) declared that generative AI was not used in the creation of this manuscript.

Any alternative text (alt text) provided alongside figures in this article has been generated by Frontiers with the support of artificial intelligence and reasonable efforts have been made to ensure accuracy, including review by the authors wherever possible. If you identify any issues, please contact us.

Publisher's note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

References

1. Mikhail D, Sarcona J, Mekhail M, Richstone L. Urologic robotic surgery. Surg Clin North Am. (2020) 100:361–78. doi: 10.1016/j.suc.2019.12.003

PubMed Abstract | Crossref Full Text | Google Scholar

2. Ma LP, Guo YJ, Zhou CY, Guo YJ, Liu XY, Wang XZ. A study on the current Status of clinical use and management of surgical robots in China. Chin Hosp Manage. (2024) 44:46–49 + 54.

Google Scholar

3. Morrell ALG, Morrell-Junior AC, Morrell AG, Mendes JMF, Tustumi F, DE-Oliveira-E-Silva LG, et al. The history of robotic surgery and its evolution: when illusion becomes reality. Rev Col Bras Cir. (2021) 48:e20202798. doi: 10.1590/0100-6991e-20202798

PubMed Abstract | Crossref Full Text | Google Scholar

4. Nguyen C, Bachtel H, Koh CJ. Pediatric robotic urologic surgery: pyeloplasty and ureteral reimplantation. Semin Pediatr Surg. (2023) 32:151264. doi: 10.1016/j.sempedsurg.2023.151264

PubMed Abstract | Crossref Full Text | Google Scholar

5. Liu HZ, Tang DX. Application of robot-assisted laparoscopic surgery in pediatric urology. Chin J Minim Invasive Surg. (2021) 21:432–6. doi: 10.3969/j.issn.1009-6604.2021.05.011

Crossref Full Text | Google Scholar

6. Abdulfattah S, Aghababian A, Saxena S, Eftekharzadeh S, Mitchell A, Ai E, et al. Outcomes of robot-assisted laparoscopic pyeloplasty among pediatric patients with complex renal anatomy: a retrospective comparative study. J Pediatr Urol. (2024) 20:1174–8. doi: 10.1016/j.jpurol.2024.07.019

PubMed Abstract | Crossref Full Text | Google Scholar

7. Abdulfattah S, Ai E, Quairoli M, Eftekharzadeh S, Fischer K, Aghababian A, et al. Robot-assisted laparoscopic ureteral reimplantation in Pre-toilet trained children with vesicoureteral reflux. Urology. (2024) 195:126–31. doi: 10.1016/j.urology.2024.10.030

PubMed Abstract | Crossref Full Text | Google Scholar

8. Abdulfattah S, Zirel L, Mittal S, Srinivasan A, Shukla AR. The missed crossing vessel during open pyeloplasty: a potential advantage of the robot-assisted approach in children. J Robot Surg. (2024) 18:285. doi: 10.1007/s11701-024-02006-5

PubMed Abstract | Crossref Full Text | Google Scholar

9. Liang XJ. A review of research on mapping knowledge domains in science. Libr J. (2009) 28:58–62. doi: 10.13663/j.cnki.lj.2009.06.025

Crossref Full Text | Google Scholar

10. Ma HZ, Qian YR, Leng HY, Wu HP, Tao WB, Zhang YY. A review of research progress on knowledge graph embedding. Comput Eng. (2025) 51:18–34. doi: 10.19678/j.issn.1000-3428.0068386

Crossref Full Text | Google Scholar

11. Ninkov A, Frank JR, Maggio LA. Bibliometrics: methods for studying academic publishing. Perspect Med Educ. (2022) 11:173–6. doi: 10.1007/s40037-021-00695-4

PubMed Abstract | Crossref Full Text | Google Scholar

12. Veiga-Del-Baño JM, Cámara MÁ, Oliva J, Hernández-Cegarra AT, Andreo-Martínez P, Motas M. Mapping of emerging contaminants in coastal waters research: a bibliometric analysis of research output during 1986−2022. Mar Pollut Bull. (2023) 194:115366. doi: 10.1016/j.marpolbul.2023.115366

Crossref Full Text | Google Scholar

13. Wei X, Wang M, Yu S, Han Z, Li C, Zhong Y, et al. Mapping the knowledge of omics in myocardial infarction: a scientometric analysis in R studio, VOSviewer, citespace, and SciMAT. Medicine (Baltimore). (2025) 104:e41368. doi: 10.1097/MD.0000000000041368

PubMed Abstract | Crossref Full Text | Google Scholar

14. Cheng P, Tang H, Dong Y, Liu K, Jiang P, Liu Y. Knowledge mapping of research on land use change and food security: a visual analysis using CiteSpace and VOSviewer. Int J Environ Res Public Health. (2021) 18:13065. doi: 10.3390/ijerph182413065

PubMed Abstract | Crossref Full Text | Google Scholar

15. Mocan L. Surgical management of gastric cancer: a systematic review. J Clin Med. (2021) 10:2557. doi: 10.3390/jcm10122557

PubMed Abstract | Crossref Full Text | Google Scholar

16. Minoli DG, Albo G, De Marco EA, Gnech M, Montanari E, Manzoni G, et al. Robotic bladder neck plication for incontinence treatment. Cent European J Urol. (2021) 74(2):274–5. doi: 10.5173/ceju.2021.0355.R3

PubMed Abstract | Crossref Full Text | Google Scholar

17. Minoli DG, De Marco EA, Gnech M, Rocco B, Manzoni G, Berrettini A. Laparoscopic robotic-assisted “keel” bladder neck construction. J Pediatr Urol. (2020) 16(6):857–8. doi: 10.1016/j.jpurol.2020.09.027

PubMed Abstract | Crossref Full Text | Google Scholar