Bibliometric analysis and network visualisation for horizon scanning of bioengineering innovations

Bibliometric analysis and network visualisation for horizon scanning of bioengineering innovations | 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 Bibliometric analysis and network visualisation for horizon scanning of bioengineering innovations Claire Eastaugh, Pauline Addis, Megan Fairweather, Rhiannon Potter, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9401325/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Horizon scanning is essential for identifying emerging healthcare technologies before widespread adoption, to guide research priorities and inform policy. Traditional keyword-based retrieval can fail to capture novel bioengineering applications due to the volume, complexity, and diversity of terminology, thereby limiting early detection of impactful innovations. Bioengineering’s breadth presents unique challenges, making bibliometric techniques such as keyword co-occurrence analysis and network visualisation a promising alternative for scalable and reproducible horizon scanning. This study develops and evaluates a methodological approach combining bibliometric analysis and network mapping to detect signals of technological innovation in bioengineering. Using Embase (OVID), free-text searches were conducted, and results processed via EndNote for de-duplication. VOSviewer generated co-occurrence networks, supported by a custom thesaurus to standardise terms and group them into three thematic categories: (1) tissues and medical devices, (2) therapeutics and drug delivery, and (3) genetic engineering. High-frequency keywords indicated established research areas, while low-frequency, recent terms signalled emerging trends. Supplementary searches in PubMed (post-2025) and clinicaltrials.gov (post-2020) triangulated findings for relevance and potential application. This integrated approach enhances transparency, reproducibility, and scalability in horizon scanning, offering structured insights into the bioengineering landscape. It supports prioritisation of research areas, funding allocation, and policy development, particularly in complex interdisciplinary fields, demonstrating the potential of network bibliographic analysis for horizon scanning in broad and undefined topics. Horizon scanning bibliometric analysis network visualisation keyword visualisation innovation detection bioengineering Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Background Horizon scanning is a systematic method for assessing information sources to identify unmet needs, future developments, and emerging innovations before market adoption (Amanatidou et al., 2012; Hines et al., 2019; van Rij, 2010). The process facilitates the integration of new technologies into healthcare by enabling early identification, guiding research priorities, and reducing delays in patient access; and encompasses a range of methods for searching a wide range of sources (Canadian Agency for Drugs and Technologies in Health, 2015; Simpson & (EuroScan) EuroScan International Network, 2014). Methods range from expert-based insights to data-driven or participatory approaches, and may be supported by automation tools (Amanatidou et al., 2012; Douw et al., 2003; Garcia Gonzalez-Moral et al., 2023; Hines et al., 2019). The process of horizon scanning usually follows a flow of actions starting with identification of weak signals, which are early, subtle indicators that may become significant trends (van Rij, 2010). This is followed by filtration, prioritisation, and finally selection and dissemination (Hines et al., 2019). Starting a horizon scanning exercise when the scope or problem is not clearly defined presents considerable challenges. A better defined and narrower topic allows more effective searches to be designed. Traditional information retrieval methods, often structured around conceptual frameworks like PICOS (Population, Intervention, Comparison, Outcome, Study design) (Eriksen & Frandsen, 2018), are less suited to broad horizon scanning exercises that start with little concept definition, prompting exploration of alternative approaches. Network bibliographic visualisation analysis, rooted in scientometrics and bibliometrics, offers a range of benefits that enhance research quality, transparency, and strategic insight. It enables the detection of emerging research themes and topics by employing analytical techniques such as bibliographic coupling and co-occurrence analysis, which transform bibliometric data into visual network maps (Boyack & Klavans, 2010; Érdi et al., 2013). These maps use nodes (e.g., authors, keywords) and edges (e.g., collaborations, shared keywords), with clusters representing thematically grouped nodes. For instance, a cluster might link ‘tissue engineering’ with ‘regenerative medicine’. Tools like VOSviewer (www.vosviewer.com) enable interactive mapping of homogeneous networks which typically include only one type of entity and only one type of link, such as keywords (van Eck & Waltman, 2017). Such visualisation helps to detect weak signals and emerging trends, enhancing methodological rigor when combined with other data sources (Érdi et al., 2013; Small et al., 2013). We used this new methodology to explore emerging trends in bioengineering, an interdisciplinary field merging biology and engineering, currently of interest in healthcare due to its rapid growth, healthcare impact, and growing value on the global medical device market (BCC Research LLC, 2025; Fortune Business Insights, 2025; Grand View Research, 2025; Precedence Research, 2025; Zoting, 2025). Proactive horizon scanning in bioengineering enables the timely identification of technologies, such as advanced implants or drug delivery systems, with high potential to address pressing healthcare challenges. Given bioengineering’s complexity and translational hurdles, integrating network bibliographic visualisation into horizon scanning can transform bibliometric data into intuitive maps that reveal emerging trends and clusters. This study examines the application of network bibliographic analysis as a method for detecting innovation signals in horizon scanning. The method was developed to support a wider project to identify emerging technologies in bioengineering with the potential to be game-changing; that is, to offer new solutions to health problems or re-purpose an existing technology for a new application. Using bioengineering as a case study, it demonstrates methodological support for projects with broad scope and undefined criteria. Detailed analyses of three bioengineering domains supported by this method (tissues and medical devices, therapeutics and drug delivery systems, and genetically engineered organisms) are available on the NIHR Innovation Observatory website. Methods A multi-step approach was used to identify, filter and prioritise bioengineering innovations; figure 1 provides a visual summary of the methods employed. First, a bioengineering-related search strategy which aimed to maximise sensitivity was developed by an information specialist in the Embase (Ovid) database (supplementary file 1). Results were exported as a RIS file into VOSviewer, where keyword co-occurrence analysis was selected to generate a list of all the keywords with rankings and link strengths. Then, these terms were refined into a custom thesaurus to unify variants, disambiguate meanings, and group keywords into three thematic categories: (1) tissues and medical devices, (2) therapeutics and drug delivery systems, and (3) genetically engineered organisms. Next, visualisations for each category were created in VOSviewer using the original RIS file and custom thesaurus. Finally, two sets of keywords were identified: (1) high-frequency and (2) emerging keywords appearing in more recent literature, and used to guide searches for bioengineering innovations in other sources such as ClinicalTrials.gov and PubMed. Search strategy A bibliographic search was developed by one information specialist (CE) in Embase (Ovid) and peer-reviewed by a second (SGGM) for consistency, performance assessment, and errors following the PRESS checklist (McGowan et al., 2016; Page et al., 2021). Free-text English search strings were used, including terms for ‘bioengineering’, ‘genetically engineered’, and ‘synthetic biology’, developed through recent literature searches (supplementary file 1) (Foo et al., 2017; Kemp et al., 2020; Kong et al., 2023; Leclerc O, 2022). A previously published (Crawford M, 2020) key bioengineering paper outlined trends (2010-2020) and we aimed to build from this. The search was run in January 2025 and a RIS file of the search results generated. EndNote 21 was used as the repository for the bibliometric sources found. To maximise retrieval, the unspecified search field (.mp) was used, covering multiple fields (title, abstract, headings, keywords) for comprehensive results. Although subject headings provide standardised vocabulary for related topics, free-text terms were chosen after scoping searches to capture emerging innovations not yet included in controlled vocabularies, improving specificity. VOSviewer VOSviewer is a free open-source tool that visualises bibliometric networks by mapping keyword co-occurrences into clusters, which reveal thematic patterns (figure 2; (van Eck & Waltman, 2010). For example, in the custom thesaurus for tissues and medical devices (figure 3), ‘hydrogel’ was identified as a cluster with co-occurrence to ‘extracellular matrix’, ‘collagen’ and ‘cell differentiation’ (figure 4). Semantic analysis was used to identify words and phrases that are strongly associated with specific topics in the literature. These domain-specific terms have high discriminatory power, helping to distinguish concepts and visualise the structure of the research field (van Eck et al., 2010). Identified terms were plotted on a term map based on their similarity: the closer two terms appear, the more frequently they are linked in the dataset. This allows patterns of association to emerge. Highly-related terms are grouped into clusters, which represent interconnected themes or concepts, providing insights into how different areas of research are structured and related (De Jong & Bus, 2023). Keywords The RIS file from the bibliographic search was uploaded to VOSviewer (v1.6.20) for co-occurrence analysis using full counting. A minimum occurrence threshold of 5 was applied to exclude rare keywords, and the number of keywords to be selected was set to the maximum number. VOSviewer generated 4,266 keywords, which were exported to Excel to create a thesaurus of relevant and standardised terms. Creation of custom thesaurus The keyword list underwent screening and refinement to clean the data and improve data quality by removing duplicates, correcting errors and inconsistences, and consolidating different versions of the same term, e.g., UK/US English spelling variations, to ensure uniform treatment. Irrelevant terms were excluded e.g., ‘activated sludge’ and ‘yeast’, as were standardised synonyms. Related terms were grouped e.g., ‘liver’, ‘liver cell’, and ‘liver organoid’ were grouped under ‘liver’. Creation of VOSviewer maps After refinement, the custom thesaurus and RIS files were uploaded to VOSviewer to generate a keyword map using the same analysis settings and thresholds. Transparent records of the process were maintained, producing a map based on the original Embase (Ovid) search refined by the screened thesaurus (Figure 3). Data analysis To perform analysis at greater depth, an export of the VOSviewer keyword co-occurrence map data as a .csv file was used to perform analysis within each of the three arms. The top three most frequent and three newest keywords were assessed, with the rationale that frequent terms indicate strong research interest (blue dot in figure 5), while newer, low-frequency terms suggest emerging technologies (orange dot in figure 5). Publication dates were taken from VOSviewer as the mean for each keyword. Validity was checked by comparing six keywords, three most and three least frequent, with EndNote data. Using the data in Table 1 as an example, the maximum difference was 7.2 months, and a paired two-tailed t-test showed no significant difference (P=0.357), indicating that the publication date given by VOSviewer was valid and reliable. The three most frequent and three newest keywords were used for additional database searches, and results were reviewed to identify technologies with potential benefits and game-changing impact. Table 1: Example of mean publication years between VOSviewer and EndNote Keyword Mean publication year Difference (months) VOSviewer EndNote Nanofabrication 2021.67 2021.67 0 Gold nanoparticle 2021.97 2021.95 0.24 Graphene 2022.27 2022.22 0.60 Microfluidic device 2023.40 2023.56 1.92 Transistor 2023.00 2022.40 7.20 Zirconium 2022.20 2022.07 0.78 Quality assurance To ensure methodological quality, the search strategy was developed by an experienced information specialist (CE) and peer-reviewed (SGGM). A pilot screen of 100 keywords was independently assessed by three reviewers (PA, MF, and RP) achieving 93.76% inter-rater agreement, well above the 80% minimum standard acceptable interrater agreement (McHugh, 2012). Any disagreements were resolved through discussion and consensus-building. Groupings were refined through peer review, and methods and preliminary results were further reviewed by a senior researcher (AM). Strengths and limitations This methodology’s strength lies in combining bibliometric keyword analysis with VOSviewer network visualisation, making it effective for broad scientific fields with no narrow scope in horizon scanning projects like bioengineering. By analysing both high-frequency and recent low-frequency keywords, it identifies emerging technologies often missed by traditional searches. Supplementary searches in PubMed and ClinicalTrials.gov add validity, while peer-reviewed strategies and high inter-rater agreement ensure quality. Limitations include the manual thesaurus creation, which is time-consuming and subjective; future improvements could use large language models for synonym detection, thematic grouping, and automated screening, and integrate data from patents or funding to enhance early signal detection. Compared to advanced tools like Gephi (Bastian et al., 2009) or R’s Network Analysis packages (Telarico et al., 2025), VOSviewer offers limited statistical functions, basic text mining (Mondal et al., 2025) and lacks visualisations such as tree maps. However, these features were unnecessary for this study, and VOSviewer’s simplicity and intuitive design outweighed its limitations. In addition to supporting multiple sources such as PubMed, Lens, and OpenAlex, providing advanced clustering for thematic mapping, and shareable visualisations (Mondal et al., 2025; Singapore Management University, 2022), VOSviewer offers intuitive navigation and efficient processing of large bibliometric datasets, and the option to use a custom thesaurus. VOSviewer is widely cited, indicating its widespread use and recognition as a core analytical tool for mapping keyword co‑occurrences (Tomaszewski, 2023). Implications and recommendations Integrating VOSviewer with a user-generated custom thesaurus and bibliometric analysis provides a transparent, reproducible, and scalable method for detecting emerging bioengineering technologies. This approach addresses a key methodological gap by enabling the visualisation of related terms and their frequencies, which helps uncover records missed by traditional keyword searches that depend on the explicit inclusion of predetermined terms in the query. Analysing recent, low-frequency keywords highlights potential innovations, while thematic clustering provides a structured view of the field, enabling stakeholders to identify patterns, relationships, and potential areas for targeted investment or research. These insights can guide funding priorities, early-stage research directions, and policy decisions by identifying gaps and future opportunities such as under-researched areas or emerging fields with high potential impact. Recent, low-frequency keywords should be prioritised for further investigation through structured expert consultations, comprehensive grey literature searches, and targeted exploration of clinical trial databases to validate their potential significance. Future enhancements could include integrating patent and funding data to triangulate results, providing a more robust framework for early signal detection and trend validation. VOSviewer generated 4,266 keywords whereas there were 4,439 records from the Embase (Ovid) search which would typically undergo a labour-intensive title/abstract screening, full text screening, and data extraction before analysis could take place. Though the keywords were screened for relevance, refinement, correcting, deduplication and consolidating, and quality assurance assessments took place, this was an efficient process and therefore could be conducted in a timely manner. The effective use of VOSviewer demonstrates that bibliometric tools can enhance horizon scanning by improving the detection of emerging trends, particularly in research fields where the scope is ambiguous or not well-defined by conceptual frameworks. Furthermore, this methodology can be applied as a longer-term approach because the same keywords can be reused for updated database searches. As a result, the initial effort invested in screening the thesaurus is justified by its capacity to support the continuous update of results. Declarations Corresponding author : Claire H. Eastaugh, National Institute for Health and care Research (NIHR) Innovation Observatory, Newcastle University, The Catalyst, Room 3.12, 3 Science Square, Newcastle Helix, Newcastle Upon Tyne, NE4 5TG [email protected] Funding/support: This study/project is funded by the National Institute for Health and Care Research (NIHR) NIHR IO/project reference HSRIC-2016-10009. The views expressed are those of the author(s) and not necessarily those of the NIHR or the Department of Health and Social Care. Competing interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Data availability statement: All data relevant to the study are included in the article or uploaded as supplemental information. Acknowledgements: We thank our stakeholders at the Department of Health and Social Care for their expertise and guidance, and Katie Twentyman for her assistance in creating the visual summary. Author contributions: Conceptualisation: CE and SGGM Methodology: CE and SGGM Validation: CE, SGGM, PA, MF and RP Formal analysis: PA, MF and RP Investigation: CE, SGGM, PA, MF and RP Resources: CE, SGGM and PA Data curation: PA, MF and RP Writing - original draft: CE, SGGM and PA Writing - review & editing: CE, SGGM, PA, MF, RP and AM Visualisation: CE and PA Supervision: SGGM, PA and AM Project administration: PA and AM All authors read and approved the final manuscript. References Amanatidou, E., Butter, M., Carabias, V., Könnölä, T., Leis, M., Saritas, O., Schaper-Rinkel, P., & van Rij, V. (2012). On concepts and methods in horizon scanning: Lessons from initiating policy dialogues on emerging issues. Science and Public Policy , 39 (2), 208-221. https://doi.org/10.1093/scipol/scs017 Bastian, M., Heymann, S., & Jacomy, M. (2009). Gephi: An open source software for exploring and manipulating networks . https://gephi.org BCC Research LLC. (2025). Medical Devices Industry to Surpass $1.3 Trillion by 2029 . https://www.globenewswire.com/news-release/2025/03/18/3044771/0/en/Medical-Devices-Industry-to-Surpass-1-3-Trillion-by-2029.html Boyack, K. W., & Klavans, R. (2010). Co-citation analysis, bibliographic coupling, and direct citation: Which citation approach represents the research front most accurately? Journal of the American Society for Information Science and Technology , 61 (12), 2389-2404. https://doi.org/https://doi.org/10.1002/asi.21419 Canadian Agency for Drugs and Technologies in Health. (2015). Horizon scanning process [Version 1.0] . https://www.cadth.ca/sites/default/files/externalprocesses_horizonscanningprogram.pdf Crawford M. (2020, 14 October 2022). Top 10 Bioengineering Trends for the 2020s . https://www.asme.org/topics-resources/content/top-10-bioengineering-trends De Jong, R., & Bus, D. (2023). VOSviewer: putting research into context . https://doi.org/10.21428/a1847950.acdc99d6 Douw, K., Vondeling, H., Eskildsen, D., & Simpson, S. (2003). Use of the Internet in Scanning the Horizon for New and Emerging Health Technologies: A Survey of Agencies Involved in Horizon Scanning. J Med Internet Res , 5 (1), e6. https://doi.org/10.2196/jmir.5.1.e6 Érdi, P., Makovi, K., Somogyvári, Z., Strandburg, K., Tobochnik, J., Volf, P., & Zalányi, L. (2013). Prediction of emerging technologies based on analysis of the US patent citation network. Scientometrics , 95 (1), 225-242. https://doi.org/10.1007/s11192-012-0796-4 Eriksen, M. B., & Frandsen, T. F. (2018). The impact of patient, intervention, comparison, outcome (PICO) as a search strategy tool on literature search quality: a systematic review. J Med Libr Assoc , 106 (4), 420-431. https://doi.org/10.5195/jmla.2018.345 Foo, J. L., Ling, H., Lee, Y. S., & Chang, M. W. (2017). Microbiome engineering: Current applications and its future. Biotechnology Journal , 12 (3), 1600099. https://doi.org/10.1002/biot.201600099 Fortune Business Insights. (2025, 26 May 2025). Medical Devices Market Size, Share & Industry Analysis, By Type (Orthopedic Devices, Cardiovascular Devices, Diagnostic Imaging Devices, In-vitro Diagnostics (IVD), Minimally Invasive Surgery Devices, Wound Management, Diabetes Care Devices, Ophthalmic Devices, Dental Devices, Nephrology Devices, General Surgery, and Others), By End-User (Hospitals & ASCs, Clinics, and Others), and Regional Forecast, 2025-2032 . https://www.fortunebusinessinsights.com/industry-reports/medical-devices-market-100085 Garcia Gonzalez-Moral, S., Beyer, F. R., Oyewole, A. O., Richmond, C., Wainwright, L., & Craig, D. (2023). Looking at the fringes of MedTech innovation: a mapping review of horizon scanning and foresight methods. BMJ Open , 13 (9), e073730. https://doi.org/10.1136/bmjopen-2023-073730 Grand View Research. (2025). Tissue Engineering Market Size, Share & Trends Analysis Report By Application (Cord Blood & Cell Banking, Cancer, GI & Gynecology, Dental, Orthopedics, Musculoskeletal, & Spine, Urology), By Region And Segment Forecasts, 2025 - 2030 . https://www.grandviewresearch.com/industry-analysis/tissue-engineering-and-regeneration-industry Hines, P., Hiu Yu, L., Guy, R. H., Brand, A., & Papaluca-Amati, M. (2019). Scanning the horizon: a systematic literature review of methodologies. BMJ Open , 9 (5), e026764. https://doi.org/10.1136/bmjopen-2018-026764 Kemp, L., Adam, L., Boehm, C. R., Breitling, R., Casagrande, R., Dando, M., Djikeng, A., Evans, N. G., Hammond, R., Hills, K., Holt, L. A., Kuiken, T., Markotić, A., Millett, P., Napier, J. A., Nelson, C., ÓhÉigeartaigh, S. S., Osbourn, A., Palmer, M. J., Patron, N. J., Perello, E., Piyawattanametha, W., Restrepo-Schild, V., Rios-Rojas, C., Rhodes, C., Roessing, A., Scott, D., Shapira, P., Simuntala, C., Smith, R. D. J., Sundaram, L. S., Takano, E., Uttmark, G., Wintle, B. C., Zahra, N. B., & Sutherland, W. J. (2020). Bioengineering horizon scan 2020. eLife , 9 , e54489. https://doi.org/10.7554/eLife.54489 Kong, X., Gao, P., Wang, J., Fang, Y., & Hwang, K. C. (2023). Advances of medical nanorobots for future cancer treatments. Journal of Hematology & Oncology , 16 (1), 74. https://doi.org/10.1186/s13045-023-01463-z Leclerc O, S. M., The L,. (2022). What are the biotech investment themes that will shape the industry? . https://www.mckinsey.com/industries/life-sciences/our-insights/what-are-the-biotech-investment-themes-that-will-shape-the-industry#/ McGowan, J., Sampson, M., Salzwedel, D. M., Cogo, E., Foerster, V., & Lefebvre, C. (2016). PRESS peer review of electronic search strategies: 2015 guideline statement. Journal of Clinical Epidemiology , 75 , 40-46. https://doi.org/https://doi.org/10.1016/j.jclinepi.2016.01.021 McHugh, M. L. (2012). Interrater reliability: the kappa statistic. Biochem Med (Zagreb) , 22 (3), 276-282. https://pmc.ncbi.nlm.nih.gov/articles/PMC3900052/ Mondal, H., Gupta, G., Sarangi, P. K., Sharma, S., Choudhary, P. K., Juhi, A., Kumari, A., & Mondal, S. (2025). Assessing the Capability of Large Language Model Chatbots in Generating Plain Language Summaries. Cureus , 17 (3), e80976. https://doi.org/10.7759/cureus.80976 Page, M. J., McKenzie, J. E., Bossuyt, P. M., Boutron, I., Hoffmann, T. C., Mulrow, C. D., Shamseer, L., Tetzlaff, J. M., Akl, E. A., Brennan, S. E., Chou, R., Glanville, J., Grimshaw, J. M., Hróbjartsson, A., Lalu, M. M., Li, T., Loder, E. W., Mayo-Wilson, E., McDonald, S., McGuinness, L. A., Stewart, L. A., Thomas, J., Tricco, A. C., Welch, V. A., Whiting, P., & Moher, D. (2021). The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. BMJ , 372 , n71. https://doi.org/10.1136/bmj.n71 Precedence Research. (2025). Genetic Engineering Market Size, Share and Trends 2025 to 2034 [last updated: 22 April 2025] . https://www.precedenceresearch.com/genetic-engineering-market Simpson, S., & (EuroScan) EuroScan International Network. (2014). A toolkit for the identification and assessment of new and emerging health technologies (EuroScan Methods Toolkit, Issue. Singapore Management University. (2022). Using VOSviewer as a bibliometric mapping or analysis tool in Business, Management & accounting . https://library.smu.edu.sg/topics-insights/using-vosviewer-bibliometric-mapping-or-analysis-tool-business-management Small, H., Boyack, K., & Klavans, R. (2013). Identifying emerging topics by combining direct citation and co-citation. 14th International Conference of the International Society for Scientometrics and Informetrics, Telarico, F. A., Krivitsky, P. N., & Hollway, J. (2025). CRAN Task View: Network Analysis. Version 2025-09-24 . https://CRAN.R-project.org/view=NetworkAnalysis Tomaszewski, R. (2023). Visibility, impact, and applications of bibliometric software tools through citation analysis. Scientometrics , 128 (7), 4007-4028. https://doi.org/10.1007/s11192-023-04725-2 van Eck, N. J., & Waltman, L. (2010). Software survey: VOSviewer, a computer program for bibliometric mapping. Scientometrics , 84 (2), 523-538. https://doi.org/10.1007/s11192-009-0146-3 van Eck, N. J., & Waltman, L. (2017). Citation-based clustering of publications using CitNetExplorer and VOSviewer. Scientometrics , 111 (2), 1053-1070. https://doi.org/10.1007/s11192-017-2300-7 van Eck, N. J., Waltman, L., Noyons, E. C. M., & Buter, R. K. (2010). Automatic term identification for bibliometric mapping. Scientometrics , 82 (3), 581-596. https://doi.org/10.1007/s11192-010-0173-0 van Rij, V. (2010). Horizon scanning: monitoring plausible and desirable futures. In R. J. in 't Veld (Ed.), Knowledge Democracy: Consequences for Science, Politics, and Media (pp. 227-240). Springer Berlin Heidelberg. https://doi.org/10.1007/978-3-642-11381-9_16 Zoting, S. (2025). Nanotechnology Market Size, Share, and Trends 2024 to 2034 . https://www.precedenceresearch.com/nanotechnology-market Additional Declarations No competing interests reported. Supplementary Files ScientometricsManuscriptSUPPLEMENTFILE.docx Cite Share Download PDF Status: Posted Version 1 posted 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. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-9401325","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":624523202,"identity":"1c14effa-e248-41a7-86f0-89dbdbfb77b6","order_by":0,"name":"Claire Eastaugh","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABSElEQVRIie2RQUvDMBTHXwm0l5ReO6rdV8goDATBr9JScBeLgiA9CGYI2WXqJxC/woZQ8dYSmJdirz0IYwieJnQMZGgQO910rOqugv1dXvJefvwTAlBS8hdRAMKFbWjOV2uzahcU9Kmgd8Wa9Qn+TVlchQ5dpWjHaMB9H0ztLInG+PmucaW0a9lECKwBGmQS215WdC6TKI7B0lMXGerpg3fdjq3KCSO4QmVLl9jOskIQkKjJwKEpAkNtc6+TegGolGASQh0k5hcVJYuar3B0kXD0kisN0n8MJCEI3gqVp+8VnKdQsEnoygaecJukaoCwnKcAnqYULqZzvBfRnl7rpm5945zyWifeHRvrzML5aF+3bwvP11qtyxE93KyaSXSfDgWvkpueMxoK08xH3Sw7cJeVedhHkdhic/pR9g/CF2LliZKSkpJ/yBt/em7s88HO3gAAAABJRU5ErkJggg==","orcid":"","institution":"Newcastle University","correspondingAuthor":true,"prefix":"","firstName":"Claire","middleName":"","lastName":"Eastaugh","suffix":""},{"id":624523203,"identity":"54912206-0552-4213-b65b-e0a731c85dcd","order_by":1,"name":"Pauline Addis","email":"","orcid":"","institution":"Newcastle University","correspondingAuthor":false,"prefix":"","firstName":"Pauline","middleName":"","lastName":"Addis","suffix":""},{"id":624523204,"identity":"d7920929-802d-40bf-985f-cb092e158dc7","order_by":2,"name":"Megan Fairweather","email":"","orcid":"","institution":"Newcastle University","correspondingAuthor":false,"prefix":"","firstName":"Megan","middleName":"","lastName":"Fairweather","suffix":""},{"id":624523205,"identity":"65d6301d-941d-4a22-8fe4-37b323476832","order_by":3,"name":"Rhiannon Potter","email":"","orcid":"","institution":"Newcastle University","correspondingAuthor":false,"prefix":"","firstName":"Rhiannon","middleName":"","lastName":"Potter","suffix":""},{"id":624523206,"identity":"192e548c-676e-46b2-ba39-d9c715e4963a","order_by":4,"name":"Andrew Mkwashi1","email":"","orcid":"","institution":"Newcastle University","correspondingAuthor":false,"prefix":"","firstName":"Andrew","middleName":"","lastName":"Mkwashi1","suffix":""},{"id":624523207,"identity":"47ae01cc-a560-4157-a3a2-90357e8dd0ad","order_by":5,"name":"Sonia Garcia Gonzalez-Moral","email":"","orcid":"","institution":"Newcastle University","correspondingAuthor":false,"prefix":"","firstName":"Sonia","middleName":"Garcia","lastName":"Gonzalez-Moral","suffix":""}],"badges":[],"createdAt":"2026-04-13 08:57:49","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9401325/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9401325/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":108493629,"identity":"c7f403b5-a2be-4eac-b2a1-a245ae7c7c46","added_by":"auto","created_at":"2026-05-05 10:01:07","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":87249,"visible":true,"origin":"","legend":"\u003cp\u003eVisual summary of the entire process.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-9401325/v1/be7b0b1ee5a3c9e9ba12cf07.png"},{"id":108493718,"identity":"672169db-171f-4417-853c-761f110aa4a8","added_by":"auto","created_at":"2026-05-05 10:01:26","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":318536,"visible":true,"origin":"","legend":"\u003cp\u003eExample of bibliometric visualisation VOSviewer map based on co-occurrence data and full results of Embase (Ovid) search strategy.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-9401325/v1/3bfa70b0d7a6aff995a7aa48.png"},{"id":108404847,"identity":"df619fbd-0378-4bc5-95f4-a5c9040fc68f","added_by":"auto","created_at":"2026-05-04 09:32:07","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":276994,"visible":true,"origin":"","legend":"\u003cp\u003eExample of a VOSviewer map based on co-occurrence data with a custom thesaurus applied to a bibliographic search RIS file showing the links between a selected keyword (hydrogel) and the identified unmet needs.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-9401325/v1/51d238dc773566df1543ccac.png"},{"id":108404848,"identity":"f259c41b-ca5c-4043-84f0-9c375182ea96","added_by":"auto","created_at":"2026-05-04 09:32:07","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":548533,"visible":true,"origin":"","legend":"\u003cp\u003eA close-up of the links between a selected keyword (hydrogel) and the top three applications (extracellular matrix, collagen and cell differentiation). The dark background was selected to emphasise the width of the top three links\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-9401325/v1/c755f25cbb17ff66a8325425.png"},{"id":108404850,"identity":"9947a37e-ccff-4c89-b3ab-05b620d6a1d3","added_by":"auto","created_at":"2026-05-04 09:32:07","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":45391,"visible":true,"origin":"","legend":"\u003cp\u003eExample of distribution of occurrences of keywords against mean publication date. Trendline is a 2nd order polynomial function. Blue dot = most frequent and newest keywords suggesting strong research interest. Orange dot = low-frequency terms suggest emerging technologies\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-9401325/v1/b793d161a13cf3b7fd99b71e.png"},{"id":108803937,"identity":"a32a70d8-b4d1-490e-9abd-007b8d8d251d","added_by":"auto","created_at":"2026-05-08 15:11:59","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1420412,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9401325/v1/dadd7301-7ed6-405c-bd99-6c9f826ce3d0.pdf"},{"id":108404845,"identity":"495bc66d-abf8-4d04-aedf-f76c725799b2","added_by":"auto","created_at":"2026-05-04 09:32:07","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":44734,"visible":true,"origin":"","legend":"","description":"","filename":"ScientometricsManuscriptSUPPLEMENTFILE.docx","url":"https://assets-eu.researchsquare.com/files/rs-9401325/v1/ae53ee060cea9dc7cbb3c189.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Bibliometric analysis and network visualisation for horizon scanning of bioengineering innovations","fulltext":[{"header":"Background ","content":"\u003cp\u003eHorizon scanning is a systematic method for assessing information sources to identify unmet needs, future developments, and emerging innovations before market adoption (Amanatidou et al., 2012; Hines et al., 2019; van Rij, 2010). The process facilitates the integration of new technologies into healthcare by enabling early identification, guiding research priorities, and reducing delays in patient access; and encompasses a range of methods for searching a wide range of sources (Canadian Agency for Drugs and Technologies in Health, 2015; Simpson \u0026amp; (EuroScan) EuroScan International Network, 2014). Methods range from expert-based insights to data-driven or participatory approaches, and may be supported by automation tools (Amanatidou et al., 2012; Douw et al., 2003; Garcia Gonzalez-Moral et al., 2023; Hines et al., 2019).\u003c/p\u003e\n\u003cp\u003eThe process of horizon scanning usually follows a flow of actions starting with identification of weak signals, which are early, subtle indicators that may become significant trends (van Rij, 2010). This is followed by filtration, prioritisation, and finally selection and dissemination (Hines et al., 2019). Starting a horizon scanning exercise when the scope or problem is not clearly defined presents considerable challenges. A better defined and narrower topic allows more effective searches to be designed. Traditional information retrieval methods, often structured around conceptual frameworks like PICOS (Population, Intervention, Comparison, Outcome, Study design) (Eriksen \u0026amp; Frandsen, 2018), are less suited to broad horizon scanning exercises that start with little concept definition, prompting exploration of alternative approaches.\u003c/p\u003e\n\u003cp\u003eNetwork bibliographic visualisation analysis, rooted in scientometrics and bibliometrics, offers a range of benefits that enhance research quality, transparency, and strategic insight. It enables the detection of emerging research themes and topics by employing analytical techniques such as bibliographic coupling and co-occurrence analysis, which transform bibliometric data into visual network maps (Boyack \u0026amp; Klavans, 2010; \u0026Eacute;rdi et al., 2013).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThese maps use nodes (e.g., authors, keywords) and edges (e.g., collaborations, shared keywords), with clusters representing thematically grouped nodes. For instance, a cluster might link \u0026lsquo;tissue engineering\u0026rsquo; with \u0026lsquo;regenerative medicine\u0026rsquo;. Tools like VOSviewer (www.vosviewer.com) enable interactive mapping of homogeneous networks which typically include only one type of entity and only one type of link, such as keywords (van Eck \u0026amp; Waltman, 2017). Such visualisation helps to detect weak signals and emerging trends, enhancing methodological rigor when combined with other data sources (\u0026Eacute;rdi et al., 2013; Small et al., 2013).\u003c/p\u003e\n\u003cp\u003eWe used this new methodology to explore emerging trends in bioengineering, an interdisciplinary field merging biology and engineering, \u0026nbsp;currently of interest in healthcare due to its rapid growth, healthcare impact, and growing value on the global medical device market (BCC Research LLC, 2025; Fortune Business Insights, 2025; Grand View Research, 2025; Precedence Research, 2025; Zoting, 2025). Proactive horizon scanning in bioengineering enables the timely identification of technologies, such as advanced implants or drug delivery systems, with high potential to address pressing healthcare challenges. Given bioengineering\u0026rsquo;s complexity and translational hurdles, integrating network bibliographic visualisation into horizon scanning can transform bibliometric data into intuitive maps that reveal emerging trends and clusters.\u003c/p\u003e\n\u003cp\u003eThis study examines the application of network bibliographic analysis as a method for detecting innovation signals in horizon scanning. The method was developed to support a wider project to identify emerging technologies in bioengineering with the potential to be game-changing; that is, to offer new solutions to health problems or re-purpose an existing technology for a new application. Using bioengineering as a case study, it demonstrates methodological support for projects with broad scope and undefined criteria. Detailed analyses of three bioengineering domains supported by this method (tissues and medical devices, therapeutics and drug delivery systems, and genetically engineered organisms) are available on the NIHR Innovation Observatory website.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003eA multi-step approach was used to identify, filter and prioritise bioengineering innovations; figure 1 provides a visual summary of the methods employed.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFirst, a bioengineering-related search strategy which aimed to maximise sensitivity was developed by an information specialist in the Embase (Ovid) database (supplementary file 1). Results were exported as a RIS file into VOSviewer, where keyword co-occurrence analysis was selected to generate a list of all the keywords with rankings and link strengths. Then, these terms were refined into a custom thesaurus to unify variants, disambiguate meanings, and group keywords into three thematic categories: (1) tissues and medical devices, (2) therapeutics and drug delivery systems, and (3) genetically engineered organisms. Next, visualisations for each category were created in VOSviewer using the original RIS file and custom thesaurus. Finally, two sets of keywords were identified: (1) high-frequency and (2) emerging keywords appearing in more recent literature, and used to guide searches for bioengineering innovations in other sources such as ClinicalTrials.gov and PubMed.\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003eSearch strategy\u003c/h2\u003e\n\u003cp\u003eA bibliographic search was developed by one information specialist (CE) in Embase (Ovid) and peer-reviewed by a second (SGGM) for consistency, performance assessment, and errors following the PRESS checklist (McGowan et al., 2016; Page et al., 2021). Free-text English search strings were used, including terms for ‘bioengineering’, ‘genetically engineered’, and ‘synthetic biology’, developed through recent literature searches (supplementary file 1) (Foo et al., 2017; Kemp et al., 2020; Kong et al., 2023; Leclerc O, 2022).\u0026nbsp;A previously published\u0026nbsp;(Crawford M, 2020)\u0026nbsp;key bioengineering paper outlined trends (2010-2020) and we aimed to build from this. The search was run in January 2025 and a RIS file of the search results generated. EndNote 21 was used as the repository for the bibliometric sources found.\u003c/p\u003e\n\u003cp\u003eTo maximise retrieval, the unspecified search field (.mp) was used, covering multiple fields (title, abstract, headings, keywords) for comprehensive results. Although subject headings provide standardised vocabulary for related topics, free-text terms were chosen after scoping searches to capture emerging innovations not yet included in controlled vocabularies, improving specificity.\u003c/p\u003e\n\u003ch2\u003eVOSviewer\u003c/h2\u003e\n\u003cp\u003eVOSviewer is a free open-source tool that visualises bibliometric networks by mapping keyword co-occurrences into clusters, which reveal thematic patterns (figure 2; (van Eck \u0026amp; Waltman, 2010). For example, in the custom thesaurus for tissues and medical devices (figure 3), ‘hydrogel’ was identified as a cluster with co-occurrence to ‘extracellular matrix’, ‘collagen’ and ‘cell differentiation’ (figure 4).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSemantic analysis was used to identify words and phrases that are strongly associated with specific topics in the literature. These domain-specific terms have high discriminatory power, helping to distinguish concepts and visualise the structure of the research field (van Eck et al., 2010). Identified terms were plotted on a term map based on their similarity: the closer two terms appear, the more frequently they are linked in the dataset. This allows patterns of association to emerge. Highly-related terms are grouped into clusters, which represent interconnected themes or concepts, providing insights into how different areas of research are structured and related (De Jong \u0026amp; Bus, 2023).\u0026nbsp;\u003c/p\u003e\n\u003ch3\u003eKeywords\u0026nbsp;\u003c/h3\u003e\n\u003cp\u003eThe RIS file from the bibliographic search was uploaded to VOSviewer (v1.6.20) for co-occurrence analysis using full counting. A minimum occurrence threshold of 5 was applied to exclude rare keywords, and the number of keywords to be selected was set to the maximum number. VOSviewer generated 4,266 keywords, which were exported to Excel to create a thesaurus of relevant and standardised terms.\u003c/p\u003e\n\u003ch2\u003e\u003cspan id=\"_Toc213845140\"\u003eCreation of custom thesaurus\u0026nbsp;\u003c/span\u003e\u003c/h2\u003e\n\u003cp\u003eThe keyword list underwent screening and refinement to clean the data and improve data quality by removing duplicates, correcting errors and inconsistences, and consolidating different versions of the same term, e.g., UK/US English spelling variations, to ensure uniform treatment. Irrelevant terms were excluded e.g., ‘activated sludge’ and ‘yeast’, as were standardised synonyms. Related terms were grouped e.g., ‘liver’, ‘liver cell’, and ‘liver organoid’ were grouped under ‘liver’.\u003c/p\u003e\n\u003ch2\u003e\u003cspan id=\"_Toc213845141\"\u003eCreation of VOSviewer maps\u003c/span\u003e\u003c/h2\u003e\n\u003cp\u003eAfter refinement, the custom thesaurus and RIS files were uploaded to VOSviewer to generate a keyword map using the same analysis settings and thresholds. Transparent records of the process were maintained, producing a map based on the original Embase (Ovid) search refined by the screened thesaurus (Figure 3).\u003c/p\u003e\n\n\n\n\n\n\n\n\n\n\n\n\n\n"},{"header":"Data analysis ","content":"\u003cp\u003eTo perform analysis at greater depth, an export of the VOSviewer keyword co-occurrence map data as a .csv file was used to perform analysis within each of the three arms. The top three most frequent and three newest keywords were assessed, with the rationale that frequent terms indicate strong research interest (blue dot in figure 5), while newer, low-frequency terms suggest emerging technologies (orange dot in figure 5).\u003c/p\u003e\u003cp\u003ePublication dates were taken from VOSviewer as the mean for each keyword. Validity was checked by comparing six keywords, three most and three least frequent, with EndNote data.\u0026nbsp;Using the data in Table 1 as an example, the maximum difference was 7.2 months, and a paired two-tailed t-test showed no significant difference (P=0.357), indicating that the publication date given by VOSviewer was valid and reliable.\u003c/p\u003e\u003cp\u003eThe three most frequent and three newest keywords were used for additional database searches, and results were reviewed to identify technologies with potential benefits and game-changing impact.\u003c/p\u003e\u003cp\u003eTable 1: Example of mean publication years between VOSviewer and EndNote\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 161px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eKeyword\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 246px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMean publication year\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDifference (months)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVOSviewer\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eEndNote\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003eNanofabrication\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e2021.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e2021.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003eGold nanoparticle\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e2021.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e2021.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e0.24\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003eGraphene\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e2022.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e2022.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e0.60\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003eMicrofluidic device\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e2023.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e2023.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e1.92\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003eTransistor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e2023.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e2022.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e7.20\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003eZirconium\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e2022.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e2022.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e0.78\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\u003ch2\u003eQuality assurance\u003c/h2\u003e\u003cp\u003eTo ensure methodological quality, the search strategy was developed by an experienced information specialist (CE) and peer-reviewed (SGGM). A pilot screen of 100 keywords was independently assessed by three reviewers (PA, MF, and RP) achieving 93.76% inter-rater agreement, well above the 80% minimum standard acceptable interrater agreement (McHugh, 2012). Any disagreements were resolved through discussion and consensus-building. Groupings were refined through peer review, and methods and preliminary results were further reviewed by a senior researcher (AM).\u003c/p\u003e\u003ch2 id=\"_Toc213845144\"\u003eStrengths and limitations\u003c/h2\u003e\u003cp\u003eThis methodology’s strength lies in combining bibliometric keyword analysis with VOSviewer network visualisation, making it effective for broad scientific fields with no narrow scope in horizon scanning projects like bioengineering. By analysing both high-frequency and recent low-frequency keywords, it identifies emerging technologies often missed by traditional searches. Supplementary searches in PubMed and ClinicalTrials.gov add validity, while peer-reviewed strategies and high inter-rater agreement ensure quality. Limitations include the manual thesaurus creation, which is time-consuming and subjective; future improvements could use large language models for synonym detection, thematic grouping, and automated screening, and integrate data from patents or funding to enhance early signal detection.\u003c/p\u003e\u003cp\u003eCompared to advanced tools like Gephi (Bastian et al., 2009) or R’s Network Analysis packages (Telarico et al., 2025), VOSviewer offers limited statistical functions, basic text mining (Mondal et al., 2025) and lacks visualisations such as tree maps. However, these features were unnecessary for this study, and VOSviewer’s simplicity and intuitive design outweighed its limitations. In addition to supporting\u0026nbsp;multiple sources such as\u0026nbsp;PubMed, Lens, and OpenAlex, providing advanced clustering for thematic mapping, and shareable visualisations\u0026nbsp;(Mondal et al., 2025; Singapore Management University, 2022), VOSviewer offers intuitive navigation and efficient processing of large bibliometric datasets, and the option to use a custom thesaurus. VOSviewer is widely cited, indicating its widespread use and recognition as a core analytical tool for mapping keyword co‑occurrences\u0026nbsp;(Tomaszewski, 2023).\u0026nbsp;\u003c/p\u003e"},{"header":"Implications and recommendations","content":"\u003cp\u003eIntegrating VOSviewer with a user-generated custom thesaurus and bibliometric analysis provides a transparent, reproducible, and scalable method for detecting emerging bioengineering technologies. This approach addresses a key methodological gap by enabling the visualisation of related terms and their frequencies, which helps uncover records missed by traditional keyword searches that depend on the explicit inclusion of predetermined terms in the query. Analysing recent, low-frequency keywords highlights potential innovations, while thematic clustering provides a structured view of the field, enabling stakeholders to identify patterns, relationships, and potential areas for targeted investment or research. These insights can guide funding priorities, early-stage research directions, and policy decisions by identifying gaps and future opportunities such as under-researched areas or emerging fields with high potential impact.\u003c/p\u003e\u003cp\u003eRecent, low-frequency keywords should be prioritised for further investigation through structured expert consultations, comprehensive grey literature searches, and targeted exploration of clinical trial databases to validate their potential significance. Future enhancements could include integrating patent and funding data to triangulate results, providing a more robust framework for early signal detection and trend validation.\u003c/p\u003e\u003cp\u003eVOSviewer generated 4,266 keywords whereas there were 4,439 records from the Embase (Ovid) search which would typically undergo a labour-intensive title/abstract screening, full text screening, and data extraction before analysis could take place. Though the keywords were screened for relevance, refinement, correcting, deduplication and consolidating, and quality assurance assessments took place, this was an efficient process and therefore could be conducted in a timely manner.\u003c/p\u003e\u003cp\u003eThe effective use of VOSviewer demonstrates that bibliometric tools can enhance horizon scanning by improving the detection of emerging trends, particularly in research fields where the scope is ambiguous or not well-defined by conceptual frameworks. Furthermore, this methodology can be applied as a longer-term approach because the same keywords can be reused for updated database searches. As a result, the initial effort invested in screening the thesaurus is justified by its capacity to support the continuous update of results.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eCorresponding author\u003c/strong\u003e: Claire H. Eastaugh, National Institute for Health and care Research (NIHR) Innovation Observatory, Newcastle University, The Catalyst, Room 3.12, 3 Science Square, Newcastle Helix, Newcastle Upon Tyne, NE4 5TG [email protected]\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding/support:\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThis study/project is funded by the National Institute for Health and Care Research (NIHR) NIHR IO/project reference HSRIC-2016-10009. The views expressed are those of the author(s) and not necessarily those of the NIHR or the Department of Health and Social Care.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests:\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability statement:\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAll data relevant to the study are included in the article or uploaded as supplemental information.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements:\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWe thank our stakeholders at the Department of Health and Social Care for their expertise and guidance, and Katie Twentyman for her assistance in creating the visual summary.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions:\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eConceptualisation:\u0026nbsp;CE and SGGM\u003c/p\u003e\n\u003cp\u003eMethodology: CE and SGGM\u003c/p\u003e\n\u003cp\u003eValidation: CE, SGGM, PA, MF and RP\u003c/p\u003e\n\u003cp\u003eFormal analysis: PA, MF and RP\u003c/p\u003e\n\u003cp\u003eInvestigation: CE, SGGM, PA, MF and RP\u003c/p\u003e\n\u003cp\u003eResources: CE, SGGM and PA\u003c/p\u003e\n\u003cp\u003eData curation: PA, MF and RP\u003c/p\u003e\n\u003cp\u003eWriting -\u0026nbsp;original\u0026nbsp;draft: CE, SGGM and PA\u003c/p\u003e\n\u003cp\u003eWriting - review \u0026amp; editing: CE, SGGM, PA, MF, RP and AM\u003c/p\u003e\n\u003cp\u003eVisualisation: CE and PA\u003c/p\u003e\n\u003cp\u003eSupervision: SGGM, PA and AM\u003c/p\u003e\n\u003cp\u003eProject administration: PA and AM\u003c/p\u003e\n\u003cp\u003eAll authors read and approved the final manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAmanatidou, E., Butter, M., Carabias, V., K\u0026ouml;nn\u0026ouml;l\u0026auml;, T., Leis, M., Saritas, O., Schaper-Rinkel, P., \u0026amp; van Rij, V. (2012). On concepts and methods in horizon scanning: Lessons from initiating policy dialogues on emerging issues. \u003cem\u003eScience and Public Policy\u003c/em\u003e,\u003cem\u003e 39\u003c/em\u003e(2), 208-221. https://doi.org/10.1093/scipol/scs017\u003c/li\u003e\n\u003cli\u003eBastian, M., Heymann, S., \u0026amp; Jacomy, M. (2009). \u003cem\u003eGephi: An open source software for exploring and manipulating networks\u003c/em\u003e. https://gephi.org\u003c/li\u003e\n\u003cli\u003eBCC Research LLC. (2025). \u003cem\u003eMedical Devices Industry to Surpass $1.3 Trillion by 2029\u003c/em\u003e. https://www.globenewswire.com/news-release/2025/03/18/3044771/0/en/Medical-Devices-Industry-to-Surpass-1-3-Trillion-by-2029.html\u003c/li\u003e\n\u003cli\u003eBoyack, K. W., \u0026amp; Klavans, R. (2010). Co-citation analysis, bibliographic coupling, and direct citation: Which citation approach represents the research front most accurately? \u003cem\u003eJournal of the American Society for Information Science and Technology\u003c/em\u003e,\u003cem\u003e 61\u003c/em\u003e(12), 2389-2404. https://doi.org/https://doi.org/10.1002/asi.21419\u003c/li\u003e\n\u003cli\u003eCanadian Agency for Drugs and Technologies in Health. (2015). \u003cem\u003eHorizon scanning process [Version 1.0]\u003c/em\u003e. https://www.cadth.ca/sites/default/files/externalprocesses_horizonscanningprogram.pdf\u003c/li\u003e\n\u003cli\u003eCrawford M. (2020, 14 October 2022). \u003cem\u003eTop 10 Bioengineering Trends for the 2020s\u003c/em\u003e. https://www.asme.org/topics-resources/content/top-10-bioengineering-trends\u003c/li\u003e\n\u003cli\u003eDe Jong, R., \u0026amp; Bus, D. (2023). \u003cem\u003eVOSviewer: putting research into context\u003c/em\u003e. https://doi.org/10.21428/a1847950.acdc99d6\u003c/li\u003e\n\u003cli\u003eDouw, K., Vondeling, H., Eskildsen, D., \u0026amp; Simpson, S. (2003). Use of the Internet in Scanning the Horizon for New and Emerging Health Technologies: A Survey of Agencies Involved in Horizon Scanning. \u003cem\u003eJ Med Internet Res\u003c/em\u003e,\u003cem\u003e 5\u003c/em\u003e(1), e6. https://doi.org/10.2196/jmir.5.1.e6\u003c/li\u003e\n\u003cli\u003e\u0026Eacute;rdi, P., Makovi, K., Somogyv\u0026aacute;ri, Z., Strandburg, K., Tobochnik, J., Volf, P., \u0026amp; Zal\u0026aacute;nyi, L. (2013). Prediction of emerging technologies based on analysis of the US patent citation network. \u003cem\u003eScientometrics\u003c/em\u003e,\u003cem\u003e 95\u003c/em\u003e(1), 225-242. https://doi.org/10.1007/s11192-012-0796-4\u003c/li\u003e\n\u003cli\u003eEriksen, M. B., \u0026amp; Frandsen, T. F. (2018). The impact of patient, intervention, comparison, outcome (PICO) as a search strategy tool on literature search quality: a systematic review. \u003cem\u003eJ Med Libr Assoc\u003c/em\u003e,\u003cem\u003e 106\u003c/em\u003e(4), 420-431. https://doi.org/10.5195/jmla.2018.345\u003c/li\u003e\n\u003cli\u003eFoo, J. L., Ling, H., Lee, Y. S., \u0026amp; Chang, M. W. (2017). Microbiome engineering: Current applications and its future. \u003cem\u003eBiotechnology Journal\u003c/em\u003e,\u003cem\u003e 12\u003c/em\u003e(3), 1600099. https://doi.org/10.1002/biot.201600099\u003c/li\u003e\n\u003cli\u003eFortune Business Insights. (2025, 26 May 2025). \u003cem\u003eMedical Devices Market Size, Share \u0026amp; Industry Analysis, By Type (Orthopedic Devices, Cardiovascular Devices, Diagnostic Imaging Devices, In-vitro Diagnostics (IVD), Minimally Invasive Surgery Devices, Wound Management, Diabetes Care Devices, Ophthalmic Devices, Dental Devices, Nephrology Devices, General Surgery, and Others), By End-User (Hospitals \u0026amp; ASCs, Clinics, and Others), and Regional Forecast, 2025-2032\u003c/em\u003e. https://www.fortunebusinessinsights.com/industry-reports/medical-devices-market-100085\u003c/li\u003e\n\u003cli\u003eGarcia Gonzalez-Moral, S., Beyer, F. R., Oyewole, A. O., Richmond, C., Wainwright, L., \u0026amp; Craig, D. (2023). Looking at the fringes of MedTech innovation: a mapping review of horizon scanning and foresight methods. \u003cem\u003eBMJ Open\u003c/em\u003e,\u003cem\u003e 13\u003c/em\u003e(9), e073730. https://doi.org/10.1136/bmjopen-2023-073730\u003c/li\u003e\n\u003cli\u003eGrand View Research. (2025). \u003cem\u003eTissue Engineering Market Size, Share \u0026amp; Trends Analysis Report By Application (Cord Blood \u0026amp; Cell Banking, Cancer, GI \u0026amp; Gynecology, Dental, Orthopedics, Musculoskeletal, \u0026amp; Spine, Urology), By Region And Segment Forecasts, 2025 - 2030\u003c/em\u003e. https://www.grandviewresearch.com/industry-analysis/tissue-engineering-and-regeneration-industry\u003c/li\u003e\n\u003cli\u003eHines, P., Hiu Yu, L., Guy, R. H., Brand, A., \u0026amp; Papaluca-Amati, M. (2019). Scanning the horizon: a systematic literature review of methodologies. \u003cem\u003eBMJ Open\u003c/em\u003e,\u003cem\u003e 9\u003c/em\u003e(5), e026764. https://doi.org/10.1136/bmjopen-2018-026764\u003c/li\u003e\n\u003cli\u003eKemp, L., Adam, L., Boehm, C. R., Breitling, R., Casagrande, R., Dando, M., Djikeng, A., Evans, N. G., Hammond, R., Hills, K., Holt, L. A., Kuiken, T., Markotić, A., Millett, P., Napier, J. A., Nelson, C., \u0026Oacute;h\u0026Eacute;igeartaigh, S. S., Osbourn, A., Palmer, M. J., Patron, N. J., Perello, E., Piyawattanametha, W., Restrepo-Schild, V., Rios-Rojas, C., Rhodes, C., Roessing, A., Scott, D., Shapira, P., Simuntala, C., Smith, R. D. J., Sundaram, L. S., Takano, E., Uttmark, G., Wintle, B. C., Zahra, N. B., \u0026amp; Sutherland, W. J. (2020). Bioengineering horizon scan 2020. \u003cem\u003eeLife\u003c/em\u003e,\u003cem\u003e 9\u003c/em\u003e, e54489. https://doi.org/10.7554/eLife.54489\u003c/li\u003e\n\u003cli\u003eKong, X., Gao, P., Wang, J., Fang, Y., \u0026amp; Hwang, K. C. (2023). Advances of medical nanorobots for future cancer treatments. \u003cem\u003eJournal of Hematology \u0026amp; Oncology\u003c/em\u003e,\u003cem\u003e 16\u003c/em\u003e(1), 74. https://doi.org/10.1186/s13045-023-01463-z\u003c/li\u003e\n\u003cli\u003eLeclerc O, S. M., The L,. (2022). \u003cem\u003eWhat are the biotech investment themes that will shape the industry? \u003c/em\u003e. https://www.mckinsey.com/industries/life-sciences/our-insights/what-are-the-biotech-investment-themes-that-will-shape-the-industry#/\u003c/li\u003e\n\u003cli\u003eMcGowan, J., Sampson, M., Salzwedel, D. M., Cogo, E., Foerster, V., \u0026amp; Lefebvre, C. (2016). PRESS peer review of electronic search strategies: 2015 guideline statement. \u003cem\u003eJournal of Clinical Epidemiology\u003c/em\u003e,\u003cem\u003e 75\u003c/em\u003e, 40-46. https://doi.org/https://doi.org/10.1016/j.jclinepi.2016.01.021\u003c/li\u003e\n\u003cli\u003eMcHugh, M. L. (2012). Interrater reliability: the kappa statistic. \u003cem\u003eBiochem Med (Zagreb)\u003c/em\u003e,\u003cem\u003e 22\u003c/em\u003e(3), 276-282. https://pmc.ncbi.nlm.nih.gov/articles/PMC3900052/\u003c/li\u003e\n\u003cli\u003eMondal, H., Gupta, G., Sarangi, P. K., Sharma, S., Choudhary, P. K., Juhi, A., Kumari, A., \u0026amp; Mondal, S. (2025). Assessing the Capability of Large Language Model Chatbots in Generating Plain Language Summaries. \u003cem\u003eCureus\u003c/em\u003e,\u003cem\u003e 17\u003c/em\u003e(3), e80976. https://doi.org/10.7759/cureus.80976\u003c/li\u003e\n\u003cli\u003ePage, M. J., McKenzie, J. E., Bossuyt, P. M., Boutron, I., Hoffmann, T. C., Mulrow, C. D., Shamseer, L., Tetzlaff, J. M., Akl, E. A., Brennan, S. E., Chou, R., Glanville, J., Grimshaw, J. M., Hr\u0026oacute;bjartsson, A., Lalu, M. M., Li, T., Loder, E. W., Mayo-Wilson, E., McDonald, S., McGuinness, L. A., Stewart, L. A., Thomas, J., Tricco, A. C., Welch, V. A., Whiting, P., \u0026amp; Moher, D. (2021). The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. \u003cem\u003eBMJ\u003c/em\u003e,\u003cem\u003e 372\u003c/em\u003e, n71. https://doi.org/10.1136/bmj.n71\u003c/li\u003e\n\u003cli\u003ePrecedence Research. (2025). \u003cem\u003eGenetic Engineering Market Size, Share and Trends 2025 to 2034 [last updated: 22 April 2025]\u003c/em\u003e. https://www.precedenceresearch.com/genetic-engineering-market\u003c/li\u003e\n\u003cli\u003eSimpson, S., \u0026amp; (EuroScan) EuroScan International Network. (2014). \u003cem\u003eA toolkit for the identification and assessment of new and emerging health technologies\u003c/em\u003e (EuroScan Methods Toolkit, Issue.\u003c/li\u003e\n\u003cli\u003eSingapore Management University. (2022). \u003cem\u003eUsing VOSviewer as a bibliometric mapping or analysis tool in Business, Management \u0026amp; accounting\u003c/em\u003e. https://library.smu.edu.sg/topics-insights/using-vosviewer-bibliometric-mapping-or-analysis-tool-business-management\u003c/li\u003e\n\u003cli\u003eSmall, H., Boyack, K., \u0026amp; Klavans, R. (2013). Identifying emerging topics by combining direct citation and co-citation. 14th International Conference of the International Society for Scientometrics and Informetrics,\u003c/li\u003e\n\u003cli\u003eTelarico, F. A., Krivitsky, P. N., \u0026amp; Hollway, J. (2025). \u003cem\u003eCRAN Task View: Network Analysis. Version 2025-09-24\u003c/em\u003e. https://CRAN.R-project.org/view=NetworkAnalysis\u003c/li\u003e\n\u003cli\u003eTomaszewski, R. (2023). Visibility, impact, and applications of bibliometric software tools through citation analysis. \u003cem\u003eScientometrics\u003c/em\u003e,\u003cem\u003e 128\u003c/em\u003e(7), 4007-4028. https://doi.org/10.1007/s11192-023-04725-2\u003c/li\u003e\n\u003cli\u003evan Eck, N. J., \u0026amp; Waltman, L. (2010). Software survey: VOSviewer, a computer program for bibliometric mapping. \u003cem\u003eScientometrics\u003c/em\u003e,\u003cem\u003e 84\u003c/em\u003e(2), 523-538. https://doi.org/10.1007/s11192-009-0146-3\u003c/li\u003e\n\u003cli\u003evan Eck, N. J., \u0026amp; Waltman, L. (2017). Citation-based clustering of publications using CitNetExplorer and VOSviewer. \u003cem\u003eScientometrics\u003c/em\u003e,\u003cem\u003e 111\u003c/em\u003e(2), 1053-1070. https://doi.org/10.1007/s11192-017-2300-7\u003c/li\u003e\n\u003cli\u003evan Eck, N. J., Waltman, L., Noyons, E. C. M., \u0026amp; Buter, R. K. (2010). Automatic term identification for bibliometric mapping. \u003cem\u003eScientometrics\u003c/em\u003e,\u003cem\u003e 82\u003c/em\u003e(3), 581-596. https://doi.org/10.1007/s11192-010-0173-0\u003c/li\u003e\n\u003cli\u003evan Rij, V. (2010). Horizon scanning: monitoring plausible and desirable futures. In R. J. in \u0026apos;t Veld (Ed.), \u003cem\u003eKnowledge Democracy: Consequences for Science, Politics, and Media\u003c/em\u003e (pp. 227-240). Springer Berlin Heidelberg. https://doi.org/10.1007/978-3-642-11381-9_16\u003c/li\u003e\n\u003cli\u003eZoting, S. (2025). \u003cem\u003eNanotechnology Market Size, Share, and Trends 2024 to 2034\u003c/em\u003e. https://www.precedenceresearch.com/nanotechnology-market\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Horizon scanning, bibliometric analysis, network visualisation, keyword visualisation, innovation detection, bioengineering","lastPublishedDoi":"10.21203/rs.3.rs-9401325/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9401325/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Horizon scanning is essential for identifying emerging healthcare technologies before widespread adoption, to guide research priorities and inform policy. Traditional keyword-based retrieval can fail to capture novel bioengineering applications due to the volume, complexity, and diversity of terminology, thereby limiting early detection of impactful innovations. Bioengineering’s breadth presents unique challenges, making bibliometric techniques such as keyword co-occurrence analysis and network visualisation a promising alternative for scalable and reproducible horizon scanning.\n\nThis study develops and evaluates a methodological approach combining bibliometric analysis and network mapping to detect signals of technological innovation in bioengineering. Using Embase (OVID), free-text searches were conducted, and results processed via EndNote for de-duplication. VOSviewer generated co-occurrence networks, supported by a custom thesaurus to standardise terms and group them into three thematic categories: (1) tissues and medical devices, (2) therapeutics and drug delivery, and (3) genetic engineering. High-frequency keywords indicated established research areas, while low-frequency, recent terms signalled emerging trends. Supplementary searches in PubMed (post-2025) and clinicaltrials.gov (post-2020) triangulated findings for relevance and potential application.\n\nThis integrated approach enhances transparency, reproducibility, and scalability in horizon scanning, offering structured insights into the bioengineering landscape. It supports prioritisation of research areas, funding allocation, and policy development, particularly in complex interdisciplinary fields, demonstrating the potential of network bibliographic analysis for horizon scanning in broad and undefined topics.","manuscriptTitle":"Bibliometric analysis and network visualisation for horizon scanning of bioengineering innovations","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-05-04 09:32:03","doi":"10.21203/rs.3.rs-9401325/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"d4d0c18b-129a-4fe6-a550-d8f49efbf4f1","owner":[],"postedDate":"May 4th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-05-04T09:32:03+00:00","versionOfRecord":[],"versionCreatedAt":"2026-05-04 09:32:03","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9401325","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9401325","identity":"rs-9401325","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}