A large-scale quantitative analysis on the antibacterial polymers for use in percutaneous bone-contacting hearing implants

A large-scale quantitative analysis on the antibacterial polymers for use in percutaneous bone-contacting hearing implants | 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 A large-scale quantitative analysis on the antibacterial polymers for use in percutaneous bone-contacting hearing implants K.P. Khadeeja Thanha This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7392862/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 Percutaneous bone-contacting hearing implants face significant challenges from bacterial infection and biofilm formation, threatening their long-term success. While antibacterial polymers are a promising solution, the rapid growth of this research field has created a large, complex body of literature without a comprehensive quantitative overview. This study addresses that gap by performing a data-driven literature analysis on a corpus of 4800 articles sourced from ScienceDirect.com. A large-scale quantitative data analytical workflow was employed using Python, PostgreSQL, and Power BI for data curation and visualization. In particular, advanced machine learning techniques, including Latent Dirichlet Allocation (LDA) and Bidirectional Encoder Representations from Transformers (BERT), were applied to the article abstracts to identify underlying research themes. The results show a steep increase in publications after 2010 and confirm "antibacterial" as the field's foundational concept. Topic modeling successfully identified eight thematic clusters, revealing a strong interplay between clinical applications ("Surgical & Interventional Cases") and materials science ("Biomaterial Surfaces & Coatings"). This study provides a comprehensive map of the research field, offering insights to guide future investigations by highlighting key trends and potential gaps. Antibacterial polymers biofilm hearing implants percutaneous implants topic modeling Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 1. Introduction Percutaneous bone-contacting hearing implants, such as bone-anchored systems, are transformative technologies for treating hearing loss. They offer powerful alternatives when conventional hearing aids are insufficient. Bone conduction systems (like BAHA® and Osia®) vibrate the skull to send sound directly to the inner ear, bypassing issues in the outer or middle ear. This method is highly effective for individuals with conductive hearing loss, mixed hearing loss, or single-sided deafness (Arndt et al. 2024 ; Hagr 2007 ). For many patients, these implants significantly improve hearing, speech comprehension, and overall quality of life (Gawęcki et al. 2016 ). However, a major challenge with any implant that passes through the skin is the high risk of infection. The site where the implant exits the skin creates a direct pathway for bacteria to enter the body, leading to the formation of biofilms (Fu et al. 2025 ). A biofilm is a resilient colony of bacteria that adheres to the implant surface and shields itself with a protective matrix. This structure makes the infection extremely difficult to treat with standard antibiotics and resistant to the body's immune defenses (Sharma et al. 2023 ; Uruén et al. 2020 ). The resulting chronic inflammation can lead to severe complications, including bone infections and complete implant failure, often requiring complex revision surgeries and extensive antibiotic therapy (Chen et al. 2025 ; Ong et al. 2023 ). Preventing infection and biofilm formation is therefore critical for the long-term success of these devices. To address this challenge, researchers are developing antibacterial polymers and coatings as a key solution. By applying these advanced materials to the surface of an implant, they can effectively prevent bacteria from attaching and forming a biofilm in the first place (Chen et al. 2023a ; Nagay et al. 2025 ). Some of these coatings are also designed to release antimicrobial agents in a controlled, localized manner over a long period. This proactive approach helps protect the implant from infection, promising to improve patient outcomes, enhance the safety of the device, and extend its functional lifespan (Ebenezer et al. 2025 ; Ul Haq and Krukiewicz 2023 ; Villegas et al. 2024 ). While research into antibacterial materials for hearing implants is an active and rapidly expanding field, this growth has resulted in a large and complex body of literature. A significant increase in publications has been observed in the last decade, indicating a surge in academic interest. However, this rapid expansion creates a challenge in synthesizing the key findings and understanding the overall intellectual structure of the domain. There is a discernible gap in the form of a comprehensive, quantitative analysis that maps the research field, identifies the dominant thematic clusters, and tracks the evolution of scientific focus over time. Such an analysis is crucial for researchers to identify established trends, find potential research gaps, and strategically direct future investigations to the most impactful areas. This large amount of textual and numerical data is, however, ideally suited for large-scale data analysis. Applying a modern data analytics workflow which uses specialized software such as PostgreSQL for data management, Python for data processing, and Power BI for interactive visualization offers a powerful way to navigate this complexity (Abbasi et al. 2024 ; Krishna Kishor Tirupati et al. 2024; Wade 2020 ). To address the gap, the present study aims to provide a data-driven literature analysis of antibacterial polymers for use in percutaneous bone-contacting hearing implants. A final corpus of 4800 unique research articles, sourced from ScienceDirect.com, was subjected to an appropriate analytical methodology. The workflow involved systematic data curation and processing using Python and PostgreSQL, followed by trend analysis and visualization in Power BI. To uncover the underlying thematic structure of the research, advanced machine learning techniques, specifically Latent Dirichlet Allocation (LDA) and Bidirectional Encoder Representations from Transformers (BERT), were applied to the article abstracts (Dillan and Fudholi 2023 ; George and Sumathy 2023 ; Gupta et al. 2022 ; Ma et al. 2025 ). This multifaceted approach provides a novel and objective overview of the key topics, their relationships, and their development, thereby offering a comprehensive map of the current state of the field. 2. Software, programs, and methods 2.1. Software and programs Zotero reference manager, from which raw data was exported. This data was then housed in a PostgreSQL database, managed via the pgAdmin 4 environment, which served as the central repository for cleaning and structuring the dataset, including the removal of duplicate and incomplete records. Python (3.12) played a crucial role in data preparation and advanced machine learning, using the pandas library for initial data processing and the gensim, nltk, and Hugging Face Transformers libraries for topic modeling with Latent Dirichlet Allocation (LDA) and Bidirectional Encoder Representations from Transformers (BERT). The computationally intensive Bidirectional Encoder Representations from Transformers (BERT) analysis was accelerated on a NVIDIA T4 GPU within the Google Colab environment. Microsoft Power BI Desktop was a major tool for data analysis and visualization. 2.2. Literature search and data collection In the present study, only research papers selected from ScienceDirect.com was considered. Also, research papers alone were considered for the study. The following systematic search queries were conducted on 06-August-2025. (abs:"photo-activated" OR abs:"light-activated" OR abs:"photodynamic") AND (abs:"antimicrobial" OR abs:"antibacterial" OR abs:"biofilm") (ti:"wound" OR abs:"wound healing" OR abs:"wound dressing") AND (abs:"photo-activated" OR abs:"photodynamic") AND (abs:"antimicrobial" OR abs:"biofilm") (abs:"antimicrobial" OR abs:"biofilm") AND (abs:"photo-activated" OR abs:"photodynamic") AND (abs:"mechanical properties" OR abs:"adhesive" OR abs:"biocompatibility" OR abs:"cytotoxicity") (abs:"wound" OR abs:"medical device") AND (abs:"photo-activated antimicrobial") AND (abs:"mechanical" OR abs:"adhesive" OR abs:"biofilm" OR abs:"in vitro") (antimicrobial OR antibacterial) AND (polymer OR coating) AND ("cochlear implant" OR "bone-anchored hearing" OR "percutaneous implant") (antimicrobial OR antibacterial) AND (polymer OR coating) AND (percutaneous OR osseointegration OR "skin-integrating") (infection OR biofilm) AND ("cochlear implant" OR "bone-anchored hearing" OR "percutaneous implant") The RIS files of the research papers screened were downloaded for the queries. The RIS files were uploaded to Zotero. Then, the raw data were downloaded as a .csv file. 2.3. Data processing and preparation for literature review A systematic procedure used to process and prepare the bibliographic data for analysis. The primary objective was to transform raw data exported from Zotero into a structured format suitable for keyword-based literature review and subsequent loading into a PostgreSQL database. The process was performed using a custom script in Python, leveraging the robust data manipulation capabilities of the pandas library. The data preparation procedure began with the import of the initial dataset, a Comma-Separated Values (CSV) file, which was exported from a reference management tool using the RIS format as an intermediary. This source file was placed in a dedicated local directory to establish a controlled processing environment. From the raw dataset, which contained numerous extraneous columns, a targeted subset was programmatically created by selecting only eight essential fields: Publication Year, Author, Title, Publication Title, DOI, Abstract Note, Journal Abbreviation, and Manual Tags. A key objective was to restructure the data based on keywords contained in the Manual Tags column, where they were listed as a single string separated by semicolons. The script parsed this string, splitting the content into individual keywords and assigning each one to a new, named column (keyword_1 through keyword_29). After this extraction, the original Manual Tags column was removed to avoid redundancy. A critical language validation step was then performed to ensure the corpus was restricted to English-language literature. This involved programmatically detecting the language of both the Abstract Note and the combined text of all keyword columns for each entry. Any record where either the abstract or the keywords were identified as non-English was removed from the dataset. The final, processed dataset was then saved as a new CSV file, in the same directory. This systematic methodology resulted in a clean, well-structured table, with each row representing a scholarly article and each keyword isolated in its own column, ensuring the data was accurate, reproducible, and ready for import into a PostgreSQL database for advanced analysis. 2.3. Database and table creation in PostgreSQL The foundation for this study's data management was established using PostgreSQL, an open-source object-relational database system. All operations were performed using the standard SQL Query Tool provided within the pgAdmin 4 environment. A new, dedicated database was created to house the entire bibliographic dataset. The database served as a distinct and isolated environment for all subsequent data processing and analysis tasks. This was accomplished using the graphical user interface within pgAdmin. Within the database, a single table was created to store the structured data. The schema was explicitly designed with 36 columns to accommodate all relevant fields from the cleaned dataset. An id column was configured as a serial primary key to automatically assign a unique, sequential integer to each record, ensuring data integrity. Seven columns were defined to hold the primary bibliographic information (publication_year, author, title, publication_title, doi, abstract_note, or journal_abbreviation), while the remaining twenty-nine columns (keyword_1 through keyword_29) were established to accommodate the maximum number of keywords associated with any single article in the dataset. 2.4. Data cleaning and preparation in PostgreSQL systematic data processing and refinement protocol used to prepare the bibliographic dataset for analysis. The primary objective was to ensure data integrity, completeness, and structural consistency prior to visualization and analysis in Power BI. The entire data cleaning workflow was performed within a PostgreSQL database environment using its standard Query Tool. The processed CSV file, was imported into the newly created table. The COPY command was used for this bulk data transfer, as it is the most efficient and robust method. The command was configured to recognize the file's header row and handle UTF-8 encoding. To ensure each publication was represented only once, duplicate entries were identified and removed. The Digital Object Identifier (DOI) was used as the unique key for identifying duplicates. The number of duplicate records was first assessed to quantify the issue. Then, a Common Table Expression (CTE) employing the ROW_NUMBER() window function was used to delete all but the first instance of each duplicate record, based on the lowest id. The identification query was run again post-deletion. A result of zero rows confirmed the successful removal of all duplicates. An assessment of incomplete records was also carried out. The analysis excluded entries that were missing one or more of the following essential fields: publication_year, author, title, publication_title, doi, abstract_note, or journal_abbreviation. Before deletion, the number of records missing data in each of the seven key fields was quantified using a FILTER clause with COUNT(). A DELETE command was performed to remove any row where one or more of the seven key fields were found to be either NULL or an empty string. Following the comprehensive cleaning and preparation protocol, the resulting dataset in the table was considered complete, unique, and structurally sound. The database was then ready to serve as a direct data source for further evaluation and interactive visualization in Microsoft Power BI. 2.5. Data analysis and visualization using Power BI The final data analysis was conducted in Microsoft Power BI. A direct connection was established to the PostgreSQL database, and the cleaned table was loaded into the Power Query Editor. The primary data transformation was the unpivoting of the 29 keyword columns (keyword_1 through keyword_29) to convert the dataset from a wide to a long format. This critical step consolidated all keywords into a single categorical column, enabling effective frequency analysis and visualization. The resulting table was then loaded into the Power BI data model. To ensure analytical integrity, blank keyword entries were filtered on a visual-by-visual basis rather than globally, preserving the complete article count for overall metrics. Consequently, all aggregations for counting publications were explicitly set to use Count (Distinct) on the unique article identifier (id) to provide an accurate representation of the total number of articles in the dataset. 2.6. Topic modeling using machine learning The text data from abstract_note was used for topic modelling using machine learning by means of Latent Dirichlet Allocation (LDA) and Bidirectional Encoder Representations from Transformers (BERT) 2.6.1. Latent Dirichlet Allocation (LDA) This study used a quantitative text analysis approach using Latent Dirichlet Allocation (LDA) topic modeling to identify latent thematic structures within the abstracts of scholarly articles. The entire analysis was conducted using Python 3.12 and the gensim and nltk libraries. The data corpus was constructed from a CSV file containing bibliographic information. Specifically, the text from the abstract_note column was extracted for analysis, resulting in a corpus of 4800 article abstracts. To prepare the text data for modeling, a multi-step preprocessing pipeline was implemented to ensure cleanliness and standardization. Initially, all text was normalized by converting it to lowercase for uniformity. Following this, numerical digits, punctuation, and special characters were removed using regular expressions, retaining only alphabetic characters. The resulting cleaned text was then segmented into individual words (tokens) using the word_tokenize function from the Natural Language Toolkit (NLTK) library. To filter out common, non-substantive words, a standard list of English stopwords from NLTK was applied, and any remaining tokens with three or fewer characters were also discarded to reduce noise. Finally, to group words with a common root, each token was lemmatized using NLTK's WordNetLemmatizer, which, for example, would convert "studies," "studying," and "studied" all to the base form "study." The preprocessed corpus was transformed into a numerical format suitable for modeling. A document-term matrix was created using a bag-of-words (BoW) model, where each document is represented by the frequency of each word it contains. A dictionary mapping each unique token to a numerical ID was constructed. To optimize the model's performance, this dictionary was filtered to exclude very rare and very common words, specifically removing terms that appeared in fewer than 15 documents or in more than 50% of the entire corpus. The LDA model was trained using the gensim library implementation. The model was configured with several key parameters, including 10 passes over the entire corpus to ensure convergence and setting the alpha parameter to 'auto' to learn this hyperparameter from the data. An 8-topic model was selected for the final analysis. This number was chosen after preliminary testing as it provided a set of coherent and clearly interpretable topics suitable for the scope of this study. Each of the 8 topics was analyzed qualitatively by examining its top 20 most probable keywords. The thematic meaning of each topic was inferred from these keyword lists. The model's outputs—including topic-term probabilities and document-topic weights—were exported to CSV files for further analysis and visualization in Microsoft Power BI. This allowed for the creation of interactive dashboards to explore the prevalence of each topic and the specific thematic composition of individual documents. 2.6.2. Bidirectional Encoder Representations from Transformers (BERT) The analysis was conducted on a dataset of article abstracts extracted from the abstract_note column of a provided CSV file. To convert the textual data into quantitative features, I used the pre-trained bert-base-uncased model via the Hugging Face Transformers library. Each abstract was tokenized and processed to derive a 768-dimensional semantic vector from the final hidden state of the [CLS] token, with computations accelerated on a NVIDIA T4 GPU in Google Colab. For visualization of the high-dimensional data, these vectors were projected into a two-dimensional space using the t-Distributed Stochastic Neighbor Embedding (t-SNE) algorithm, as implemented in scikit-learn. The t-SNE model was configured with the following key parameters to reduce the data to two dimensions: n_components = 2, perplexity = 30, n_iter = 300, and random_state = 42 to ensure reproducibility. The resulting 2D coordinates were used to generate a scatter plot for thematic analysis, where visual clusters correspond to distinct topics. Finally, these coordinates were appended to the original metadata and exported as a new, consolidated CSV file. To formally categorize the thematic groups visually identified in the t-SNE plot, a partitional clustering algorithm, K-Means, was applied. The two-dimensional coordinates (tsne_x, tsne_y) derived from the t-SNE reduction were used as the input features for the clustering process. Based on visual inspection of the scatter plot, the number of clusters was specified as eight (k = 8). The K-Means algorithm, as implemented in the scikit-learn library, was then used to assign each abstract to one of the eight clusters. The resulting cluster assignments were appended to the dataset as a new topic_cluster column, creating a final, enriched data file suitable for categorical analysis and interactive visualization using Power BI sofware. 3. Results and discussion 3.1. Data processing Initially 6560 entries were there. After data processing and cleaning using python, 5904 entries were obtained and saved. A total of 4800 entries were obtained after the data cleaning and processing and saved for the final analysis using Power BI and topic modeling. 3.2. Data analysis and visualization using Power BI The following information and visualizations were obtained using Power BI for the data. This study analyzed a final corpus of 4800 unique articles to map the trends and thematic focus of the research field. The findings reveal a field experiencing significant growth, centered around a well-defined set of core themes and publication venues. The temporal analysis of publications demonstrates a field that has gained substantial momentum over the past two decades ( Fig. 1 ) . While initial research was sporadic, a significant inflection point occurred around 2010, after which the number of publications began a steady and steep ascent. The annual output peaked in 2024 with 536 articles, indicating a recent surge in research activity. This growth trend is characteristic of a maturing research field that is attracting increased academic and potentially commercial interest. The literature is largely concentrated within a specific set of reputed journals. The journal International Journal of Pediatric Otorhinolaryngology stands out as the clear leader, having published 320 articles, nearly double that of the next leading journal Surface and Coatings Technology (181). This concentration suggests that a core group of specialized journals serves as the primary hub for disseminating key findings and shaping the discourse within this field. A thematic analysis of keywords reveals a strong and consistent focus on the intersection of material science and regenerative medicine (Fig. 2 ). The most dominant keyword is overwhelmingly antibacterial , appearing 236 times, which solidifies it as the foundational concept of this research domain. Following this, the keywords biocompatibility (193), titanium (193), and osseointegration (191) form a clear thematic cluster, indicating that the primary application of these biomaterials is the creation of biocompatible structures for tissue regeneration. These core challenges are addressed through three primary coating strategies: anti-adhesion, contact-killing, and releasing-type (Chen et al. 2023b ). The emergence of keywords like surface modification (113) and chitosan (106) in the top ten highlights a significant sub-field focused on developing materials that can serve as coating materials. This suggests a research trend moving beyond passive structural support towards creating functional materials. Such coating technologies can be broadly classified into passive surface modifications that prevent bacterial adhesion and active surfaces that release bactericidal agents (Romanò et al. 2015 ). The literature search for this analysis also targeted research on "photo-activated", "light-activated", or "photodynamic", investigating their ability to prevent biofilm formation on medical devices. The appearance of keywords like photodynamic therapy (49), photothermal therapy (23), and photosensitizer (16) showed rapidly emerging research focus on using light to trigger antibacterial action on implant surfaces. This emerging focus is indicative of a broader trend, where photodynamic therapy is increasingly recognized as a promising, non-invasive strategy for reducing microbial burden and biofilm formation in chronic infections (Warrier et al. 2021 ). The results provide a clear picture of an active and rapidly expanding research field. The exponential growth in publications since 2010 reflects major advancements in material science and an increasing demand for sophisticated biomedical solutions. The dominance of a few reputed journals suggests a well-established and consolidated academic community. 3.3. Topic modeling using machine learning 3.3.1. Latent Dirichlet Allocation (LDA) The application of Latent Dirichlet Allocation (LDA) to the corpus of article abstracts has successfully showed eight distinct thematic areas that characterize the research area of implantable medical devices (Table 1 , Fig. 3 , and Fig. 4 ). My analysis reveals a field defined by a dynamic interplay between fundamental materials science, advanced biomedical engineering, and direct clinical application. The topics are not isolated ones but rather form a connected network that reflects the bench-to-bedside path of modern medical innovation. Table 1 Thematic topics identified from abstracts using LDA Topic ID Interpretive name Top 10 keywords Topic 0 Surgical & Interventional Cases patient, pain, year, treatment, disease, stent, percutaneous, valve, congenital, implantation Topic 1 Biomaterial Surfaces & Coatings surface, coating, implant, cell, property, alloy, titanium, antibacterial, layer, using Topic 2 Device-Related & Bacterial Infections group, infection, control, bacterial, tissue, method, catheter, wound, treatment, meningitis Topic 3 Cellular & Neural Auditory Response cell, auditory, response, skin, cochlear, effect, stimulation, electrode, hair, cochlea Topic 4 Pediatric & Genetic Hearing Loss hearing, loss, child, patient, screening, case, disease, diagnosis, year, mutation Topic 5 Medical Technology & Clinical Practice system, clinical, technology, device, review, health, treatment, research, used, medical Topic 6 Bone Tissue Engineering bone, scaffold, implant, antibacterial, cell, release, activity, infection, property, tissue Topic 7 Clinical Outcomes & Follow-up patient, group, year, implant, month, outcome, child, complication, surgery, method The results underscore the dominance of two primary research pillars. The most prevalent theme, Topic 0 ('Surgical & Interventional Cases'), which accounts for nearly 30% of the corpus, highlights that the literature is heavily anchored in clinical practice. The focus on keywords such as patient, disease, stent, and implantation suggests a strong emphasis on procedural case studies and the treatment of specific pathologies. This is complemented by Topic 7 ('Clinical Outcomes & Follow-up'), which focuses on the longitudinal assessment of these interventions through keywords like outcome, complication, and surgery. Together, these topics represent the ultimate goal of the field: treating patients and evaluating the efficacy of those treatments. Running parallel to this clinical focus is the foundational research in materials, captured by Topic 1 ('Biomaterial Surfaces & Coatings'). The high frequency of terms like surface, coating, alloy, and titanium confirms that the development of novel materials is a cornerstone of innovation in this domain. This topic represents the "how" behind the clinical applications—the engineering of materials with specific biological and mechanical properties required for successful implantation. One of the most powerful insights from this topic model is its ability to map the relationships between different research thrusts. I observed several key thematic clusters. A clear link exists between research into materials and the persistent clinical problem of infection. Topic 2 ('Device-Related & Bacterial Infections') and Topic 6 ('Bone Tissue Engineering') both contain the keyword infection. Furthermore, Topic 6 and Topic 1 share the keywords antibacterial, implant, and cell. This clustering suggests that a significant portion of research in bone tissue engineering and biomaterial development is directly motivated by the need to create antimicrobial surfaces and prevent the biofilm formation detailed in Topic 2. The central challenge driving this research is that bacterial adhesion and subsequent biofilm formation are the root causes of implant-related infections, rendering traditional antibiotic therapies ineffective (Chen et al. 2023b ). The model clearly separates the clinical and basic science aspects of hearing-related research. Topic 4 ('Pediatric & Genetic Hearing Loss') focuses on the clinical dimension with terms like screening, diagnosis, and mutation. This is thematically linked to Topic 3 ('Cellular & Neural Auditory Response'), which contains basic science terms like cell, auditory, cochlea, and stimulation. The proximity of these topics in the visualization suggests a strong connection between the diagnosis of hearing disorders and the fundamental research aimed at understanding and treating them at a cellular level. Topic 5 ('Medical Technology & Clinical Practice') acts as a link, containing general terms like review, technology, health, research, and clinical. This topic likely represents review articles, guidelines, and perspective pieces that synthesize findings from more specific areas and translate them into the broader context of clinical practice and healthcare systems. This thematic analysis provides a structured overview of the field, confirming that research is not monolithic but is composed of distinct yet interconnected sub-disciplines. The findings highlight that progress in clinical outcomes is extremely dependent on simultaneous advances in materials science, cellular biology, and engineering. For researchers, this map can help identify areas of intense focus as well as potential gaps where interdisciplinary collaboration could be fostered, for example, by more strongly linking the antimicrobial strategies from Topic 2 with the specific device applications in Topic 0. It is important to acknowledge the limitations of this study. The analysis was conducted on abstracts, which may not fully capture all the details of the full-text articles. Moving beyond literature analysis requires advanced experimental systems, such as complex 3D in vitro models, which have been developed to better replicate the multifaceted interactions between host cells, bacteria, and implant materials (Brümmer et al. 2025 ). Furthermore, the selection of an 8-topic model, while providing a coherent overview, is one of many possible representations of the data. Nevertheless, the thematic structure revealed here provides a valuable and data-driven overview of the key topics and research priorities within the literature on implantable medical devices. 3.3.2. Bidirectional Encoder Representations from Transformers (BERT) The application of BERT for semantic feature extraction followed by t-SNE for dimensionality reduction successfully mapped the high-dimensional abstract embeddings into a two-dimensional space. The resulting visualization (Fig. 5 ) reveals a distinct and non-uniform topographical structure, indicating a clear thematic organization within the dataset. The primary feature of this area is a large, dense central cluster, which likely represents the core and predominant research theme of the abstract collection. The varying densities within this central mass suggest the presence of multiple, closely related sub-topics that constitute the mainstream subject area. Surrounding this core theme are several smaller, discrete peripheral clusters. These "islands" represent specialized or niche topics that, while part of the broader domain, are semantically distinct from the central body of research. The separation between these clusters and the main continent signifies a measurable thematic divergence. The most notable feature is a dense, elongated cluster on the left of the plot, which indicates a group of abstracts with very high internal similarity. This unsupervised approach effectively uncovered the thematic structure of the abstract collection, demonstrating the efficacy of using pre-trained language models to map and explore the conceptual field of scientific literature. Subsequent K-Means clustering algorithmically partitioned the dataset into eight distinct thematic groups, detailed in Table 2 and illustrated in Fig. 6 . Analysis of the top keywords for each cluster revealed two primary, high-level domains within the literature: clinical applications of hearing implants and materials science of implant surfaces. The clinical domain was composed of several specialized topics, including general cochlear implant treatments (Topic 0), pediatric cases (Topic 2), and long-term patient outcomes (Topic 5). A separate cluster (Topic 4) was identified as consisting primarily of clinical reviews. The materials science domain was similarly divided into specific research focuses, such as implant bio-compatibility (Topic 1), the development of antibacterial and corrosion-resistant coatings (Topic 3), and the properties of antibacterial surfaces for titanium implants (Topics 6 & 7). This automated analysis effectively segregated the abstracts into coherent, interpretable thematic clusters, providing a clear and structured overview of the research areas present in the dataset. Table 2 Top topic keywords identified for each cluster ID Cluster ID Top Keywords 0 group, hearing, bone, patients, study, implant, auditory, cochlear, implants, loss 1 bone, surface, implant, implants, antibacterial, cells, coating, cell, bacterial, properties 2 patients, hearing, children, loss, cochlear, hearing loss, study, treatment, implantation, results 3 coating, coatings, surface, corrosion, ti, antibacterial, ha, alloy, properties, ag 4 hearing, patients, clinical, implant, review, implants, treatment, bone, materials, surface 5 patients, hearing, children, group, loss, hearing loss, ci, study, years, cochlear 6 surface, coatings, antibacterial, coating, ti, ha, bone, ag, properties, release 7 surface, coating, antibacterial, coatings, ti, bone, titanium, properties, implant, implants To explore the thematic analysis, an interactive data visualization dashboard was developed in Power BI (Fig. 7 ). A central feature of this dashboard is a donut chart that quantifies the proportional distribution of articles across the eight topic clusters, revealing the relative prevalence of each research theme. The analysis indicates that materials science topics and clinical applications represent the most substantial portions of the dataset. Complementing this, a stacked area chart illustrates the evolution of these topics over time by plotting the annual publication volume for each cluster. This temporal analysis highlights a significant increase in publications related to materials science in recent years, suggesting a growing research focus in this area, while more established clinical topics show stable publication rates. The dashboard is integrated with slicers for publication year and topic, enabling dynamic filtering and a granular exploration of the data, thereby transforming the cluster analysis into a multi-faceted, exploratory platform. 4. Conclusions This study successfully conducted a large-scale literature analysis to map the research field of antibacterial polymers for percutaneous bone-contacting hearing implants. By employing a data analytics workflow on a corpus of 4800 articles, several key characteristics of the field were identified. The findings reveal a domain experiencing rapid, exponential growth in publications since 2010, with a high concentration of research in a select group of specialized journals. Thematic analysis confirmed that "antibacterial" properties are the foundational concept of this research area. Furthermore, machine learning-driven topic modeling demonstrated that the field is built upon two primary and interconnected pillars: clinical applications focused on patient outcomes and surgical cases, and fundamental materials science centered on the development of advanced surfaces and coatings. The significance of this analysis lies in its creation of a structured, data-driven overview of a complex and evolving field. The clear distinction of clinical and materials science domains, and the data-supported links between them, highlights that progress in clinical outcomes is dependent on simultaneous advances in engineering and biology. This thematic map provides a valuable resource for researchers and clinicians to understand the current state of the art and identify where interdisciplinary collaboration is most active. It highlights how the persistent clinical challenge of device-related infections directly motivates foundational research into novel biomaterials. Meanwhile, it is important to note the limitations of this study. The analysis was confined to the abstracts of articles from a single database, ScienceDirect.com, and the 8-topic model represents one of many possible thematic interpretations. Future research could build upon this work by expanding the data corpus to include other major scientific databases for a more comprehensive overview. Additionally, a full-text analysis could provide more meaningful insights than are possible from abstracts alone. The thematic clusters identified here can serve as a guide for future systematic reviews, helping to identify specific research gaps, for instance, by more explicitly linking the antimicrobial strategies identified in materials science topics with the long-term clinical outcomes detailed in clinical topics. In conclusion, this quantitative literature analysis provides an objective overview of important topics and research priorities in the vital field of antibacterial coatings for hearing implants, offering a valuable framework for navigating future research. Declarations Data availability Data will be made available on request. Declaration of competing interest None to declare. References Abbasi, M., Bernardo, M. V., Váz, P., Silva, J., & Martins, P. (2024). Adaptive and Scalable Database Management with Machine Learning Integration: A PostgreSQL Case Study. Information , 15 (9), 574. https://doi.org/10.3390/info15090574 Arndt, S., Wesarg, T., Aschendorff, A., Speck, I., Hocke, T., Jakob, T. F., & Rauch, A.-K. (2024). Prediction of postoperative speech comprehension with the transcutaneous partially implantable bone conduction hearing system Osia®. HNO , 72 (S1), 1–9. https://doi.org/10.1007/s00106-023-01337-3 Brümmer, N., Doll-Nikutta, K., Schadzek, P., Mikolai, C., Kampmann, A., Wirth, D., et al. (2025). Better models, better treatment? a systematic review of current three dimensional (3D) in vitro models for implant-associated infections. Frontiers in Bioengineering and Biotechnology , 13 , 1569211. https://doi.org/10.3389/fbioe.2025.1569211 Chen, X., Zhang, S., Peng, S., Qian, Y., & Zhou, J. (2025). Piezoelectric materials for bone implants: Opportunities and challenges. Nano Energy , 138 , 110841. https://doi.org/10.1016/j.nanoen.2025.110841 Chen, X., Zhou, J., Qian, Y., & Zhao, L. (2023a). Antibacterial coatings on orthopedic implants. Materials Today Bio , 19 , 100586. https://doi.org/10.1016/j.mtbio.2023.100586 Chen, X., Zhou, J., Qian, Y., & Zhao, L. (2023b). Antibacterial coatings on orthopedic implants. Materials Today Bio , 19 , 100586. https://doi.org/10.1016/j.mtbio.2023.100586 Dillan, T., & Fudholi, D. H. (2023). LDAViewer: An Automatic Language-Agnostic System for Discovering State-of-the-Art Topics in Research Using Topic Modeling, Bidirectional Encoder Representations From Transformers, and Entity Linking. IEEE Access , 11 , 59142–59163. https://doi.org/10.1109/ACCESS.2023.3285116 Ebenezer, P., Kumara, S. P. S. N. B. S., Senevirathne, S. W. M. A. I., Bray, L. J., Wangchuk, P., Mathew, A., & Yarlagadda, P. K. D. V. (2025). Advancements in Antimicrobial Surface Coatings Using Metal/Metaloxide Nanoparticles, Antibiotics, and Phytochemicals. Nanomaterials , 15 (13), 1023. https://doi.org/10.3390/nano15131023 Fu, Y., Zhu, M., Shi, A., Zhang, B., & Xu, P. (2025). Stimulus-responsive antibacterial strategies for construction of anti-infection bone implants. Next Materials , 8 , 100554. https://doi.org/10.1016/j.nxmate.2025.100554 Gawęcki, W., Stieler, O. M., Balcerowiak, A., Komar, D., Gibasiewicz, R., Karlik, M., et al. (2016). Surgical, functional and audiological evaluation of new Baha® Attract system implantations. European Archives of Oto-Rhino-Laryngology , 273 (10), 3123–3130. https://doi.org/10.1007/s00405-016-3917-5 George, L., & Sumathy, P. (2023). An integrated clustering and BERT framework for improved topic modeling. International Journal of Information Technology , 15 (4), 2187–2195. https://doi.org/10.1007/s41870-023-01268-w Gupta, R. K., Agarwalla, R., Naik, B. H., Evuri, J. R., Thapa, A., & Singh, T. D. (2022). Prediction of research trends using LDA based topic modeling. Global Transitions Proceedings , 3 (1), 298–304. https://doi.org/10.1016/j.gltp.2022.03.015 Hagr, A. (2007). BAHA: Bone-Anchored Hearing Aid. International Journal of Health Sciences , 1 (2), 265–276. Krishna Kishor Tirupati, Archit Joshi, Dr S P Singh, Akshun Chhapola, Shalu Jain, & Dr. Alok Gupta. (2024). Leveraging Power BI for Enhanced Data Visualization and Business Intelligence. Universal Research Reports , 10 (2), 676–711. https://doi.org/10.36676/urr.v10.i2.1375 Ma, L., Chen, R., Ge, W., Rogers, P., Lyn-Cook, B., Hong, H., et al. (2025). AI-powered topic modeling: comparing LDA and BERTopic in analyzing opioid-related cardiovascular risks in women. Experimental Biology and Medicine (Maywood, N.J.) , 250 , 10389. https://doi.org/10.3389/ebm.2025.10389 Nagay, B. E., Malheiros, S. S., Borges, M. H. R., Aparicio, C., Van Den Beucken, J. J. J. P., & Barão, V. A. R. (2025). Progress in visible-light-activated photocatalytic coatings to combat implant-related infections: From mechanistic to translational roadmap. Bioactive Materials , 51 , 83–137. https://doi.org/10.1016/j.bioactmat.2025.04.037 Ong, J., Nazarian, A., Tam, J., Farinelli, W., Korupolu, S., Drake, L., et al. (2023). An antimicrobial blue light device to manage infection at the skin-implant interface of percutaneous osseointegrated implants. PLOS ONE , 18 (8), e0290347. https://doi.org/10.1371/journal.pone.0290347 Romanò, C. L., Scarponi, S., Gallazzi, E., Romanò, D., & Drago, L. (2015). Antibacterial coating of implants in orthopaedics and trauma: a classification proposal in an evolving panorama. Journal of Orthopaedic Surgery and Research , 10 (1), 157. https://doi.org/10.1186/s13018-015-0294-5 Sharma, S., Mohler, J., Mahajan, S. D., Schwartz, S. A., Bruggemann, L., & Aalinkeel, R. (2023). Microbial Biofilm: A Review on Formation, Infection, Antibiotic Resistance, Control Measures, and Innovative Treatment. Microorganisms , 11 (6), 1614. https://doi.org/10.3390/microorganisms11061614 Ul Haq, I., & Krukiewicz, K. (2023). Antimicrobial approaches for medical implants coating to prevent implants associated infections: Insights to develop durable antimicrobial implants. Applied Surface Science Advances , 18 , 100532. https://doi.org/10.1016/j.apsadv.2023.100532 Uruén, C., Chopo-Escuin, G., Tommassen, J., Mainar-Jaime, R. C., & Arenas, J. (2020). Biofilms as Promoters of Bacterial Antibiotic Resistance and Tolerance. Antibiotics , 10 (1), 3. https://doi.org/10.3390/antibiotics10010003 Villegas, M., Bayat, F., Kramer, T., Schwarz, E., Wilson, D., Hosseinidoust, Z., & Didar, T. F. (2024). Emerging Strategies to Prevent Bacterial Infections on Titanium‐Based Implants. Small , 20 (46), 2404351. https://doi.org/10.1002/smll.202404351 Wade, R. (2020). Advanced Analytics in Power BI with R and Python: Ingesting, Transforming, Visualizing . Berkeley, CA: Apress. https://doi.org/10.1007/978-1-4842-5829-3 Warrier, A., Mazumder, N., Prabhu, S., Satyamoorthy, K., & Murali, T. S. (2021). Photodynamic therapy to control microbial biofilms. Photodiagnosis and Photodynamic Therapy , 33 , 102090. https://doi.org/10.1016/j.pdpdt.2020.102090 Additional Declarations The authors declare no competing interests. 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-7392862","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":501500362,"identity":"47a4cb0c-914e-4dcf-a2b3-aa8d740ea921","order_by":0,"name":"K.P. Khadeeja Thanha","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABEUlEQVRIiWNgGAWjYBACCQkgZmBgA5PMDAw2ciDRAw+I0cID0ZJmDNaSQFgLA0zL4cQGEA+fFsnZzQ9vfKjgk7eXbn72uKAmLX1+2OGHQFvs5HQbsGuRljlmbDnjDJthj8wxc+MZx2xyN95OMwBqSTY2O4Bdi5xEgpk0bxsbYw+IwcOWlrtxdgJIy4HEbTi1pH+T/vuPzb4HxOD5dzjdcHb6B7xapCVyzKQZG9gSe0AM3rbDCfLSOfhtkZyRU2zZc4wtuedGTpk0b1+a4QbpnIIDCQa4/SJxI33jjR81x2zbZ6Rvk+b5ZiMvPzt984cPFXZyuLRAwTEE0wCs0gCvchCoQTDlGwiqHgWjYBSMghEGAKhhXmID0fcdAAAAAElFTkSuQmCC","orcid":"https://orcid.org/0009-0000-4517-5371","institution":"Government Medical College, Kozhikode","correspondingAuthor":true,"prefix":"","firstName":"K.P.","middleName":"Khadeeja","lastName":"Thanha","suffix":""}],"badges":[],"createdAt":"2025-08-17 14:20:52","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-7392862/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7392862/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":89389655,"identity":"31bdd218-6f47-49ea-bff5-0dcd35fccbaa","added_by":"auto","created_at":"2025-08-19 12:49:49","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":75292,"visible":true,"origin":"","legend":"\u003cp\u003eVisual showing total number of research papers, publication trend over time, and top 14 publication journals\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7392862/v1/1844a6730942a60a091ecc0f.png"},{"id":89390767,"identity":"2c5f46de-ee4c-43a4-87b9-3619883b171b","added_by":"auto","created_at":"2025-08-19 12:57:49","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":2047875,"visible":true,"origin":"","legend":"\u003cp\u003eVisuals on (\u003cstrong\u003ea\u003c/strong\u003e) most common keywords and (\u003cstrong\u003eb\u003c/strong\u003e) top 30 keywords\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-7392862/v1/ebe4cb0f7e0bf9aad2574bf2.png"},{"id":89388460,"identity":"58c892e6-2520-4513-b4c0-5e647abc6599","added_by":"auto","created_at":"2025-08-19 12:41:49","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":15226,"visible":true,"origin":"","legend":"\u003cp\u003eColumn chart showing topic prevalence by applying Latent Dirichlet Allocation (LDA) to the corpus of article abstracts\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-7392862/v1/53e685a1e100ac15acfd3336.png"},{"id":89388469,"identity":"a2c5e6bc-212e-4237-a571-de44e5cc4bb3","added_by":"auto","created_at":"2025-08-19 12:41:49","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":58875,"visible":true,"origin":"","legend":"\u003cp\u003eThe results of Latent Dirichlet Allocation (LDA) using visualization library (pyLDAvis). The bubbles 1-8 corresponds to Topics 0-7, respectively.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-7392862/v1/e6f2c5d0dd1cf8d843b1c2aa.png"},{"id":89389657,"identity":"63a3ce0b-8933-4774-8860-ebdf2afd8242","added_by":"auto","created_at":"2025-08-19 12:49:49","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":251682,"visible":true,"origin":"","legend":"\u003cp\u003eThematic Map of Research Abstracts Visualized using t-SNE. The plot displays the two-dimensional representation of abstract embeddings. Each point corresponds to a single abstract, and the proximity between points reflects the semantic similarity of their content. The visualization was generated by first converting abstracts into 768-dimensional vectors using a pre-trained BERT model, followed by dimensionality reduction with the t-SNE algorithm. Dense clusters of points represent distinct thematic topics within the dataset, while isolated points represent thematically unique documents\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-7392862/v1/593bc8d4602c5811f212e816.png"},{"id":89391286,"identity":"31b84dfd-8351-45df-8406-48b3ccd59d85","added_by":"auto","created_at":"2025-08-19 13:05:50","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":1407999,"visible":true,"origin":"","legend":"\u003cp\u003eThematic clustering of research abstracts. Each point represents a single abstract, plotted in a 2D space generated by applying the t-SNE algorithm to its BERT embedding. The color of each point corresponds to one of eight thematic clusters identified using the K-Means algorithm. Proximity between points indicates high semantic similarity, revealing the thematic structure of the dataset.\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-7392862/v1/cc691d8fb60af2605d833696.png"},{"id":89389662,"identity":"9e062f3c-b344-4454-bb51-9a1fab23f02c","added_by":"auto","created_at":"2025-08-19 12:49:50","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":127476,"visible":true,"origin":"","legend":"\u003cp\u003eA Power BI dashboard visualizing the thematic analysis results. The donut chart shows the proportional distribution of articles across the eight topic clusters identified by K-Means analysis. The stacked area chart illustrates the temporal evolution of these research themes, tracking the annual publication count for each cluster over time\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-7392862/v1/f59d8b9b3832c633a876c19e.png"},{"id":89392312,"identity":"b10ed734-4f53-4660-94df-c9677a7d513d","added_by":"auto","created_at":"2025-08-19 13:13:52","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":6272360,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7392862/v1/8b770e39-6590-4b7a-98ce-08b2be616b43.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eA large-scale quantitative analysis on the antibacterial polymers for use in percutaneous bone-contacting hearing implants\u003c/strong\u003e\u003c/p\u003e","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003ePercutaneous bone-contacting hearing implants, such as bone-anchored systems, are transformative technologies for treating hearing loss. They offer powerful alternatives when conventional hearing aids are insufficient. Bone conduction systems (like BAHA\u0026reg; and Osia\u0026reg;) vibrate the skull to send sound directly to the inner ear, bypassing issues in the outer or middle ear. This method is highly effective for individuals with conductive hearing loss, mixed hearing loss, or single-sided deafness (Arndt et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Hagr \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). For many patients, these implants significantly improve hearing, speech comprehension, and overall quality of life (Gawęcki et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2016\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eHowever, a major challenge with any implant that passes through the skin is the high risk of infection. The site where the implant exits the skin creates a direct pathway for bacteria to enter the body, leading to the formation of biofilms (Fu et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). A biofilm is a resilient colony of bacteria that adheres to the implant surface and shields itself with a protective matrix. This structure makes the infection extremely difficult to treat with standard antibiotics and resistant to the body's immune defenses (Sharma et al. \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Uru\u0026eacute;n et al. \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). The resulting chronic inflammation can lead to severe complications, including bone infections and complete implant failure, often requiring complex revision surgeries and extensive antibiotic therapy (Chen et al. \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Ong et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Preventing infection and biofilm formation is therefore critical for the long-term success of these devices.\u003c/p\u003e\u003cp\u003eTo address this challenge, researchers are developing antibacterial polymers and coatings as a key solution. By applying these advanced materials to the surface of an implant, they can effectively prevent bacteria from attaching and forming a biofilm in the first place (Chen et al. \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2023a\u003c/span\u003e; Nagay et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Some of these coatings are also designed to release antimicrobial agents in a controlled, localized manner over a long period. This proactive approach helps protect the implant from infection, promising to improve patient outcomes, enhance the safety of the device, and extend its functional lifespan (Ebenezer et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Ul Haq and Krukiewicz \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Villegas et al. \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eWhile research into antibacterial materials for hearing implants is an active and rapidly expanding field, this growth has resulted in a large and complex body of literature. A significant increase in publications has been observed in the last decade, indicating a surge in academic interest. However, this rapid expansion creates a challenge in synthesizing the key findings and understanding the overall intellectual structure of the domain. There is a discernible gap in the form of a comprehensive, quantitative analysis that maps the research field, identifies the dominant thematic clusters, and tracks the evolution of scientific focus over time. Such an analysis is crucial for researchers to identify established trends, find potential research gaps, and strategically direct future investigations to the most impactful areas. This large amount of textual and numerical data is, however, ideally suited for large-scale data analysis. Applying a modern data analytics workflow which uses specialized software such as PostgreSQL for data management, Python for data processing, and Power BI for interactive visualization offers a powerful way to navigate this complexity (Abbasi et al. \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Krishna Kishor Tirupati et al. 2024; Wade \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eTo address the gap, the present study aims to provide a data-driven literature analysis of antibacterial polymers for use in percutaneous bone-contacting hearing implants. A final corpus of 4800 unique research articles, sourced from ScienceDirect.com, was subjected to an appropriate analytical methodology. The workflow involved systematic data curation and processing using Python and PostgreSQL, followed by trend analysis and visualization in Power BI. To uncover the underlying thematic structure of the research, advanced machine learning techniques, specifically Latent Dirichlet Allocation (LDA) and Bidirectional Encoder Representations from Transformers (BERT), were applied to the article abstracts (Dillan and Fudholi \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; George and Sumathy \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Gupta et al. \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Ma et al. \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). This multifaceted approach provides a novel and objective overview of the key topics, their relationships, and their development, thereby offering a comprehensive map of the current state of the field.\u003c/p\u003e"},{"header":"2. Software, programs, and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1. Software and programs\u003c/h2\u003e\u003cp\u003eZotero reference manager, from which raw data was exported. This data was then housed in a PostgreSQL database, managed via the pgAdmin 4 environment, which served as the central repository for cleaning and structuring the dataset, including the removal of duplicate and incomplete records. Python (3.12) played a crucial role in data preparation and advanced machine learning, using the pandas library for initial data processing and the gensim, nltk, and Hugging Face Transformers libraries for topic modeling with Latent Dirichlet Allocation (LDA) and Bidirectional Encoder Representations from Transformers (BERT). The computationally intensive Bidirectional Encoder Representations from Transformers (BERT) analysis was accelerated on a NVIDIA T4 GPU within the Google Colab environment. Microsoft Power BI Desktop was a major tool for data analysis and visualization.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.2. Literature search and data collection\u003c/h2\u003e\u003cp\u003eIn the present study, only research papers selected from ScienceDirect.com was considered. Also, research papers alone were considered for the study. The following systematic search queries were conducted on 06-August-2025.\u003c/p\u003e\u003cp\u003e\u003col\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003e(abs:\"photo-activated\" OR abs:\"light-activated\" OR abs:\"photodynamic\") AND (abs:\"antimicrobial\" OR abs:\"antibacterial\" OR abs:\"biofilm\")\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003e(ti:\"wound\" OR abs:\"wound healing\" OR abs:\"wound dressing\") AND (abs:\"photo-activated\" OR abs:\"photodynamic\") AND (abs:\"antimicrobial\" OR abs:\"biofilm\")\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003e(abs:\"antimicrobial\" OR abs:\"biofilm\") AND (abs:\"photo-activated\" OR abs:\"photodynamic\") AND (abs:\"mechanical properties\" OR abs:\"adhesive\" OR abs:\"biocompatibility\" OR abs:\"cytotoxicity\")\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003e(abs:\"wound\" OR abs:\"medical device\") AND (abs:\"photo-activated antimicrobial\") AND (abs:\"mechanical\" OR abs:\"adhesive\" OR abs:\"biofilm\" OR abs:\"in vitro\")\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003e(antimicrobial OR antibacterial) AND (polymer OR coating) AND (\"cochlear implant\" OR \"bone-anchored hearing\" OR \"percutaneous implant\")\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003e(antimicrobial OR antibacterial) AND (polymer OR coating) AND (percutaneous OR osseointegration OR \"skin-integrating\")\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003e(infection OR biofilm) AND (\"cochlear implant\" OR \"bone-anchored hearing\" OR \"percutaneous implant\")\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003c/ol\u003e\u003c/p\u003e\u003cp\u003eThe RIS files of the research papers screened were downloaded for the queries. The RIS files were uploaded to Zotero. Then, the raw data were downloaded as a .csv file.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e2.3. Data processing and preparation for literature review\u003c/h2\u003e\u003cp\u003eA systematic procedure used to process and prepare the bibliographic data for analysis. The primary objective was to transform raw data exported from Zotero into a structured format suitable for keyword-based literature review and subsequent loading into a PostgreSQL database. The process was performed using a custom script in Python, leveraging the robust data manipulation capabilities of the pandas library.\u003c/p\u003e\u003cp\u003eThe data preparation procedure began with the import of the initial dataset, a Comma-Separated Values (CSV) file, which was exported from a reference management tool using the RIS format as an intermediary. This source file was placed in a dedicated local directory to establish a controlled processing environment. From the raw dataset, which contained numerous extraneous columns, a targeted subset was programmatically created by selecting only eight essential fields: Publication Year, Author, Title, Publication Title, DOI, Abstract Note, Journal Abbreviation, and Manual Tags.\u003c/p\u003e\u003cp\u003eA key objective was to restructure the data based on keywords contained in the Manual Tags column, where they were listed as a single string separated by semicolons. The script parsed this string, splitting the content into individual keywords and assigning each one to a new, named column (keyword_1 through keyword_29). After this extraction, the original Manual Tags column was removed to avoid redundancy. A critical language validation step was then performed to ensure the corpus was restricted to English-language literature. This involved programmatically detecting the language of both the Abstract Note and the combined text of all keyword columns for each entry. Any record where either the abstract or the keywords were identified as non-English was removed from the dataset. The final, processed dataset was then saved as a new CSV file, in the same directory. This systematic methodology resulted in a clean, well-structured table, with each row representing a scholarly article and each keyword isolated in its own column, ensuring the data was accurate, reproducible, and ready for import into a PostgreSQL database for advanced analysis.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003e2.3. Database and table creation in PostgreSQL\u003c/h2\u003e\u003cp\u003eThe foundation for this study's data management was established using PostgreSQL, an open-source object-relational database system. All operations were performed using the standard SQL Query Tool provided within the pgAdmin 4 environment. A new, dedicated database was created to house the entire bibliographic dataset. The database served as a distinct and isolated environment for all subsequent data processing and analysis tasks. This was accomplished using the graphical user interface within pgAdmin.\u003c/p\u003e\u003cp\u003eWithin the database, a single table was created to store the structured data. The schema was explicitly designed with 36 columns to accommodate all relevant fields from the cleaned dataset. An id column was configured as a serial primary key to automatically assign a unique, sequential integer to each record, ensuring data integrity. Seven columns were defined to hold the primary bibliographic information (publication_year, author, title, publication_title, doi, abstract_note, or journal_abbreviation), while the remaining twenty-nine columns (keyword_1 through keyword_29) were established to accommodate the maximum number of keywords associated with any single article in the dataset.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003e2.4. Data cleaning and preparation in PostgreSQL\u003c/h2\u003e\u003cp\u003esystematic data processing and refinement protocol used to prepare the bibliographic dataset for analysis. The primary objective was to ensure data integrity, completeness, and structural consistency prior to visualization and analysis in Power BI. The entire data cleaning workflow was performed within a PostgreSQL database environment using its standard Query Tool.\u003c/p\u003e\u003cp\u003eThe processed CSV file, was imported into the newly created table. The COPY command was used for this bulk data transfer, as it is the most efficient and robust method. The command was configured to recognize the file's header row and handle UTF-8 encoding.\u003c/p\u003e\u003cp\u003eTo ensure each publication was represented only once, duplicate entries were identified and removed. The Digital Object Identifier (DOI) was used as the unique key for identifying duplicates. The number of duplicate records was first assessed to quantify the issue. Then, a Common Table Expression (CTE) employing the ROW_NUMBER() window function was used to delete all but the first instance of each duplicate record, based on the lowest id. The identification query was run again post-deletion. A result of zero rows confirmed the successful removal of all duplicates.\u003c/p\u003e\u003cp\u003eAn assessment of incomplete records was also carried out. The analysis excluded entries that were missing one or more of the following essential fields: publication_year, author, title, publication_title, doi, abstract_note, or journal_abbreviation. Before deletion, the number of records missing data in each of the seven key fields was quantified using a FILTER clause with COUNT(). A DELETE command was performed to remove any row where one or more of the seven key fields were found to be either NULL or an empty string.\u003c/p\u003e\u003cp\u003eFollowing the comprehensive cleaning and preparation protocol, the resulting dataset in the table was considered complete, unique, and structurally sound. The database was then ready to serve as a direct data source for further evaluation and interactive visualization in Microsoft Power BI.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003e2.5. Data analysis and visualization using Power BI\u003c/h2\u003e\u003cp\u003eThe final data analysis was conducted in Microsoft Power BI. A direct connection was established to the PostgreSQL database, and the cleaned table was loaded into the Power Query Editor. The primary data transformation was the unpivoting of the 29 keyword columns (keyword_1 through keyword_29) to convert the dataset from a wide to a long format. This critical step consolidated all keywords into a single categorical column, enabling effective frequency analysis and visualization. The resulting table was then loaded into the Power BI data model. To ensure analytical integrity, blank keyword entries were filtered on a visual-by-visual basis rather than globally, preserving the complete article count for overall metrics. Consequently, all aggregations for counting publications were explicitly set to use Count (Distinct) on the unique article identifier (id) to provide an accurate representation of the total number of articles in the dataset.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003e2.6. Topic modeling using machine learning\u003c/h2\u003e\u003cp\u003eThe text data from abstract_note was used for topic modelling using machine learning by means of Latent Dirichlet Allocation (LDA) and Bidirectional Encoder Representations from Transformers (BERT)\u003c/p\u003e\u003cdiv id=\"Sec10\" class=\"Section3\"\u003e\u003ch2\u003e2.6.1. Latent Dirichlet Allocation (LDA)\u003c/h2\u003e\u003cp\u003eThis study used a quantitative text analysis approach using Latent Dirichlet Allocation (LDA) topic modeling to identify latent thematic structures within the abstracts of scholarly articles. The entire analysis was conducted using Python 3.12 and the gensim and nltk libraries.\u003c/p\u003e\u003cp\u003eThe data corpus was constructed from a CSV file containing bibliographic information. Specifically, the text from the abstract_note column was extracted for analysis, resulting in a corpus of 4800 article abstracts.\u003c/p\u003e\u003cp\u003eTo prepare the text data for modeling, a multi-step preprocessing pipeline was implemented to ensure cleanliness and standardization. Initially, all text was normalized by converting it to lowercase for uniformity. Following this, numerical digits, punctuation, and special characters were removed using regular expressions, retaining only alphabetic characters. The resulting cleaned text was then segmented into individual words (tokens) using the word_tokenize function from the Natural Language Toolkit (NLTK) library. To filter out common, non-substantive words, a standard list of English stopwords from NLTK was applied, and any remaining tokens with three or fewer characters were also discarded to reduce noise. Finally, to group words with a common root, each token was lemmatized using NLTK's WordNetLemmatizer, which, for example, would convert \"studies,\" \"studying,\" and \"studied\" all to the base form \"study.\"\u003c/p\u003e\u003cp\u003eThe preprocessed corpus was transformed into a numerical format suitable for modeling. A document-term matrix was created using a bag-of-words (BoW) model, where each document is represented by the frequency of each word it contains. A dictionary mapping each unique token to a numerical ID was constructed. To optimize the model's performance, this dictionary was filtered to exclude very rare and very common words, specifically removing terms that appeared in fewer than 15 documents or in more than 50% of the entire corpus.\u003c/p\u003e\u003cp\u003eThe LDA model was trained using the gensim library implementation. The model was configured with several key parameters, including 10 passes over the entire corpus to ensure convergence and setting the alpha parameter to 'auto' to learn this hyperparameter from the data.\u003c/p\u003e\u003cp\u003eAn 8-topic model was selected for the final analysis. This number was chosen after preliminary testing as it provided a set of coherent and clearly interpretable topics suitable for the scope of this study. Each of the 8 topics was analyzed qualitatively by examining its top 20 most probable keywords. The thematic meaning of each topic was inferred from these keyword lists. The model's outputs\u0026mdash;including topic-term probabilities and document-topic weights\u0026mdash;were exported to CSV files for further analysis and visualization in Microsoft Power BI. This allowed for the creation of interactive dashboards to explore the prevalence of each topic and the specific thematic composition of individual documents.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec11\" class=\"Section3\"\u003e\u003ch2\u003e2.6.2. Bidirectional Encoder Representations from Transformers (BERT)\u003c/h2\u003e\u003cp\u003eThe analysis was conducted on a dataset of article abstracts extracted from the abstract_note column of a provided CSV file. To convert the textual data into quantitative features, I used the pre-trained bert-base-uncased model via the Hugging Face Transformers library. Each abstract was tokenized and processed to derive a 768-dimensional semantic vector from the final hidden state of the [CLS] token, with computations accelerated on a NVIDIA T4 GPU in Google Colab. For visualization of the high-dimensional data, these vectors were projected into a two-dimensional space using the t-Distributed Stochastic Neighbor Embedding (t-SNE) algorithm, as implemented in scikit-learn. The t-SNE model was configured with the following key parameters to reduce the data to two dimensions: n_components\u0026thinsp;=\u0026thinsp;2, perplexity\u0026thinsp;=\u0026thinsp;30, n_iter\u0026thinsp;=\u0026thinsp;300, and random_state\u0026thinsp;=\u0026thinsp;42 to ensure reproducibility. The resulting 2D coordinates were used to generate a scatter plot for thematic analysis, where visual clusters correspond to distinct topics. Finally, these coordinates were appended to the original metadata and exported as a new, consolidated CSV file.\u003c/p\u003e\u003cp\u003eTo formally categorize the thematic groups visually identified in the t-SNE plot, a partitional clustering algorithm, K-Means, was applied. The two-dimensional coordinates (tsne_x, tsne_y) derived from the t-SNE reduction were used as the input features for the clustering process. Based on visual inspection of the scatter plot, the number of clusters was specified as eight (k\u0026thinsp;=\u0026thinsp;8). The K-Means algorithm, as implemented in the scikit-learn library, was then used to assign each abstract to one of the eight clusters. The resulting cluster assignments were appended to the dataset as a new topic_cluster column, creating a final, enriched data file suitable for categorical analysis and interactive visualization using Power BI sofware.\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e"},{"header":"3. Results and discussion","content":"\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003e3.1. Data processing\u003c/h2\u003e\u003cp\u003eInitially 6560 entries were there. After data processing and cleaning using python, 5904 entries were obtained and saved. A total of 4800 entries were obtained after the data cleaning and processing and saved for the final analysis using Power BI and topic modeling.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003e3.2. Data analysis and visualization using Power BI\u003c/h2\u003e\u003cp\u003eThe following information and visualizations were obtained using Power BI for the data. This study analyzed a final corpus of 4800 unique articles to map the trends and thematic focus of the research field. The findings reveal a field experiencing significant growth, centered around a well-defined set of core themes and publication venues.\u003c/p\u003e\u003cp\u003eThe temporal analysis of publications demonstrates a field that has gained substantial momentum over the past two decades \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e. While initial research was sporadic, a significant inflection point occurred around 2010, after which the number of publications began a steady and steep ascent. The annual output peaked in 2024 with 536 articles, indicating a recent surge in research activity. This growth trend is characteristic of a maturing research field that is attracting increased academic and potentially commercial interest.\u003c/p\u003e\u003cp\u003eThe literature is largely concentrated within a specific set of reputed journals. The journal \u003cem\u003eInternational Journal of Pediatric Otorhinolaryngology\u003c/em\u003e stands out as the clear leader, having published 320 articles, nearly double that of the next leading journal \u003cem\u003eSurface and Coatings Technology\u003c/em\u003e (181). This concentration suggests that a core group of specialized journals serves as the primary hub for disseminating key findings and shaping the discourse within this field.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eA thematic analysis of keywords reveals a strong and consistent focus on the intersection of material science and regenerative medicine (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The most dominant keyword is overwhelmingly \u003cem\u003eantibacterial\u003c/em\u003e, appearing 236 times, which solidifies it as the foundational concept of this research domain. Following this, the keywords \u003cem\u003ebiocompatibility\u003c/em\u003e (193), \u003cem\u003etitanium\u003c/em\u003e (193), and \u003cem\u003eosseointegration\u003c/em\u003e (191) form a clear thematic cluster, indicating that the primary application of these biomaterials is the creation of biocompatible structures for tissue regeneration. These core challenges are addressed through three primary coating strategies: anti-adhesion, contact-killing, and releasing-type (Chen et al. \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2023b\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe emergence of keywords like \u003cem\u003esurface modification\u003c/em\u003e (113) and \u003cem\u003echitosan\u003c/em\u003e (106) in the top ten highlights a significant sub-field focused on developing materials that can serve as coating materials. This suggests a research trend moving beyond passive structural support towards creating functional materials. Such coating technologies can be broadly classified into passive surface modifications that prevent bacterial adhesion and active surfaces that release bactericidal agents (Roman\u0026ograve; et al. \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). The literature search for this analysis also targeted research on \"photo-activated\", \"light-activated\", or \"photodynamic\", investigating their ability to prevent biofilm formation on medical devices. The appearance of keywords like \u003cem\u003ephotodynamic therapy\u003c/em\u003e (49), \u003cem\u003ephotothermal therapy\u003c/em\u003e (23), and \u003cem\u003ephotosensitizer\u003c/em\u003e (16) showed rapidly emerging research focus on using light to trigger antibacterial action on implant surfaces. This emerging focus is indicative of a broader trend, where photodynamic therapy is increasingly recognized as a promising, non-invasive strategy for reducing microbial burden and biofilm formation in chronic infections (Warrier et al. \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eThe results provide a clear picture of an active and rapidly expanding research field. The exponential growth in publications since 2010 reflects major advancements in material science and an increasing demand for sophisticated biomedical solutions. The dominance of a few reputed journals suggests a well-established and consolidated academic community.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003e3.3. Topic modeling using machine learning\u003c/h2\u003e\u003cdiv id=\"Sec16\" class=\"Section3\"\u003e\u003ch2\u003e3.3.1. Latent Dirichlet Allocation (LDA)\u003c/h2\u003e\u003cp\u003eThe application of Latent Dirichlet Allocation (LDA) to the corpus of article abstracts has successfully showed eight distinct thematic areas that characterize the research area of implantable medical devices (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, and Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). My analysis reveals a field defined by a dynamic interplay between fundamental materials science, advanced biomedical engineering, and direct clinical application. The topics are not isolated ones but rather form a connected network that reflects the bench-to-bedside path of modern medical innovation.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eThematic topics identified from abstracts using LDA\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"3\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTopic ID\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eInterpretive name\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eTop 10 keywords\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTopic 0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSurgical \u0026amp; Interventional Cases\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003epatient, pain, year, treatment, disease, stent, percutaneous, valve, congenital, implantation\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTopic 1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBiomaterial Surfaces \u0026amp; Coatings\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003esurface, coating, implant, cell, property, alloy, titanium, antibacterial, layer, using\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTopic 2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDevice-Related \u0026amp; Bacterial Infections\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003egroup, infection, control, bacterial, tissue, method, catheter, wound, treatment, meningitis\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTopic 3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCellular \u0026amp; Neural Auditory Response\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ecell, auditory, response, skin, cochlear, effect, stimulation, electrode, hair, cochlea\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTopic 4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePediatric \u0026amp; Genetic Hearing Loss\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ehearing, loss, child, patient, screening, case, disease, diagnosis, year, mutation\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTopic 5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMedical Technology \u0026amp; Clinical Practice\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003esystem, clinical, technology, device, review, health, treatment, research, used, medical\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTopic 6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBone Tissue Engineering\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ebone, scaffold, implant, antibacterial, cell, release, activity, infection, property, tissue\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTopic 7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eClinical Outcomes \u0026amp; Follow-up\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003epatient, group, year, implant, month, outcome, child, complication, surgery, method\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThe results underscore the dominance of two primary research pillars. The most prevalent theme, Topic 0 ('Surgical \u0026amp; Interventional Cases'), which accounts for nearly 30% of the corpus, highlights that the literature is heavily anchored in clinical practice. The focus on keywords such as patient, disease, stent, and implantation suggests a strong emphasis on procedural case studies and the treatment of specific pathologies. This is complemented by Topic 7 ('Clinical Outcomes \u0026amp; Follow-up'), which focuses on the longitudinal assessment of these interventions through keywords like outcome, complication, and surgery. Together, these topics represent the ultimate goal of the field: treating patients and evaluating the efficacy of those treatments.\u003c/p\u003e\u003cp\u003eRunning parallel to this clinical focus is the foundational research in materials, captured by Topic 1 ('Biomaterial Surfaces \u0026amp; Coatings'). The high frequency of terms like surface, coating, alloy, and titanium confirms that the development of novel materials is a cornerstone of innovation in this domain. This topic represents the \"how\" behind the clinical applications\u0026mdash;the engineering of materials with specific biological and mechanical properties required for successful implantation.\u003c/p\u003e\u003cp\u003eOne of the most powerful insights from this topic model is its ability to map the relationships between different research thrusts. I observed several key thematic clusters. A clear link exists between research into materials and the persistent clinical problem of infection. Topic 2 ('Device-Related \u0026amp; Bacterial Infections') and Topic 6 ('Bone Tissue Engineering') both contain the keyword infection. Furthermore, Topic 6 and Topic 1 share the keywords antibacterial, implant, and cell. This clustering suggests that a significant portion of research in bone tissue engineering and biomaterial development is directly motivated by the need to create antimicrobial surfaces and prevent the biofilm formation detailed in Topic 2. The central challenge driving this research is that bacterial adhesion and subsequent biofilm formation are the root causes of implant-related infections, rendering traditional antibiotic therapies ineffective (Chen et al. \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2023b\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe model clearly separates the clinical and basic science aspects of hearing-related research. Topic 4 ('Pediatric \u0026amp; Genetic Hearing Loss') focuses on the clinical dimension with terms like screening, diagnosis, and mutation. This is thematically linked to Topic 3 ('Cellular \u0026amp; Neural Auditory Response'), which contains basic science terms like cell, auditory, cochlea, and stimulation. The proximity of these topics in the visualization suggests a strong connection between the diagnosis of hearing disorders and the fundamental research aimed at understanding and treating them at a cellular level.\u003c/p\u003e\u003cp\u003eTopic 5 ('Medical Technology \u0026amp; Clinical Practice') acts as a link, containing general terms like review, technology, health, research, and clinical. This topic likely represents review articles, guidelines, and perspective pieces that synthesize findings from more specific areas and translate them into the broader context of clinical practice and healthcare systems.\u003c/p\u003e\u003cp\u003eThis thematic analysis provides a structured overview of the field, confirming that research is not monolithic but is composed of distinct yet interconnected sub-disciplines. The findings highlight that progress in clinical outcomes is extremely dependent on simultaneous advances in materials science, cellular biology, and engineering. For researchers, this map can help identify areas of intense focus as well as potential gaps where interdisciplinary collaboration could be fostered, for example, by more strongly linking the antimicrobial strategies from Topic 2 with the specific device applications in Topic 0.\u003c/p\u003e\u003cp\u003eIt is important to acknowledge the limitations of this study. The analysis was conducted on abstracts, which may not fully capture all the details of the full-text articles. Moving beyond literature analysis requires advanced experimental systems, such as complex 3D \u003cem\u003ein vitro\u003c/em\u003e models, which have been developed to better replicate the multifaceted interactions between host cells, bacteria, and implant materials (Br\u0026uuml;mmer et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Furthermore, the selection of an 8-topic model, while providing a coherent overview, is one of many possible representations of the data. Nevertheless, the thematic structure revealed here provides a valuable and data-driven overview of the key topics and research priorities within the literature on implantable medical devices.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec17\" class=\"Section3\"\u003e\u003ch2\u003e3.3.2. Bidirectional Encoder Representations from Transformers (BERT)\u003c/h2\u003e\u003cp\u003eThe application of BERT for semantic feature extraction followed by t-SNE for dimensionality reduction successfully mapped the high-dimensional abstract embeddings into a two-dimensional space. The resulting visualization (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e) reveals a distinct and non-uniform topographical structure, indicating a clear thematic organization within the dataset. The primary feature of this area is a large, dense central cluster, which likely represents the core and predominant research theme of the abstract collection. The varying densities within this central mass suggest the presence of multiple, closely related sub-topics that constitute the mainstream subject area.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eSurrounding this core theme are several smaller, discrete peripheral clusters. These \"islands\" represent specialized or niche topics that, while part of the broader domain, are semantically distinct from the central body of research. The separation between these clusters and the main continent signifies a measurable thematic divergence. The most notable feature is a dense, elongated cluster on the left of the plot, which indicates a group of abstracts with very high internal similarity. This unsupervised approach effectively uncovered the thematic structure of the abstract collection, demonstrating the efficacy of using pre-trained language models to map and explore the conceptual field of scientific literature.\u003c/p\u003e\u003cp\u003eSubsequent K-Means clustering algorithmically partitioned the dataset into eight distinct thematic groups, detailed in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e. Analysis of the top keywords for each cluster revealed two primary, high-level domains within the literature: clinical applications of hearing implants and materials science of implant surfaces. The clinical domain was composed of several specialized topics, including general cochlear implant treatments (Topic 0), pediatric cases (Topic 2), and long-term patient outcomes (Topic 5). A separate cluster (Topic 4) was identified as consisting primarily of clinical reviews. The materials science domain was similarly divided into specific research focuses, such as implant bio-compatibility (Topic 1), the development of antibacterial and corrosion-resistant coatings (Topic 3), and the properties of antibacterial surfaces for titanium implants (Topics 6 \u0026amp; 7). This automated analysis effectively segregated the abstracts into coherent, interpretable thematic clusters, providing a clear and structured overview of the research areas present in the dataset.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eTop topic keywords identified for each cluster ID\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"2\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCluster ID\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTop Keywords\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003egroup, hearing, bone, patients, study, implant, auditory, cochlear, implants, loss\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ebone, surface, implant, implants, antibacterial, cells, coating, cell, bacterial, properties\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003epatients, hearing, children, loss, cochlear, hearing loss, study, treatment, implantation, results\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ecoating, coatings, surface, corrosion, ti, antibacterial, ha, alloy, properties, ag\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ehearing, patients, clinical, implant, review, implants, treatment, bone, materials, surface\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003epatients, hearing, children, group, loss, hearing loss, ci, study, years, cochlear\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003esurface, coatings, antibacterial, coating, ti, ha, bone, ag, properties, release\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003esurface, coating, antibacterial, coatings, ti, bone, titanium, properties, implant, implants\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eTo explore the thematic analysis, an interactive data visualization dashboard was developed in Power BI (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e). A central feature of this dashboard is a donut chart that quantifies the proportional distribution of articles across the eight topic clusters, revealing the relative prevalence of each research theme. The analysis indicates that materials science topics and clinical applications represent the most substantial portions of the dataset. Complementing this, a stacked area chart illustrates the evolution of these topics over time by plotting the annual publication volume for each cluster. This temporal analysis highlights a significant increase in publications related to materials science in recent years, suggesting a growing research focus in this area, while more established clinical topics show stable publication rates. The dashboard is integrated with slicers for publication year and topic, enabling dynamic filtering and a granular exploration of the data, thereby transforming the cluster analysis into a multi-faceted, exploratory platform.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e"},{"header":"4. Conclusions","content":"\u003cp\u003eThis study successfully conducted a large-scale literature analysis to map the research field of antibacterial polymers for percutaneous bone-contacting hearing implants. By employing a data analytics workflow on a corpus of 4800 articles, several key characteristics of the field were identified. The findings reveal a domain experiencing rapid, exponential growth in publications since 2010, with a high concentration of research in a select group of specialized journals. Thematic analysis confirmed that \"antibacterial\" properties are the foundational concept of this research area. Furthermore, machine learning-driven topic modeling demonstrated that the field is built upon two primary and interconnected pillars: clinical applications focused on patient outcomes and surgical cases, and fundamental materials science centered on the development of advanced surfaces and coatings.\u003c/p\u003e\u003cp\u003eThe significance of this analysis lies in its creation of a structured, data-driven overview of a complex and evolving field. The clear distinction of clinical and materials science domains, and the data-supported links between them, highlights that progress in clinical outcomes is dependent on simultaneous advances in engineering and biology. This thematic map provides a valuable resource for researchers and clinicians to understand the current state of the art and identify where interdisciplinary collaboration is most active. It highlights how the persistent clinical challenge of device-related infections directly motivates foundational research into novel biomaterials.\u003c/p\u003e\u003cp\u003eMeanwhile, it is important to note the limitations of this study. The analysis was confined to the abstracts of articles from a single database, ScienceDirect.com, and the 8-topic model represents one of many possible thematic interpretations. Future research could build upon this work by expanding the data corpus to include other major scientific databases for a more comprehensive overview. Additionally, a full-text analysis could provide more meaningful insights than are possible from abstracts alone. The thematic clusters identified here can serve as a guide for future systematic reviews, helping to identify specific research gaps, for instance, by more explicitly linking the antimicrobial strategies identified in materials science topics with the long-term clinical outcomes detailed in clinical topics. In conclusion, this quantitative literature analysis provides an objective overview of important topics and research priorities in the vital field of antibacterial coatings for hearing implants, offering a valuable framework for navigating future research.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData will be made available on request.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eDeclaration of competing interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNone to declare.\u003c/p\u003e\n"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAbbasi, M., Bernardo, M. V., V\u0026aacute;z, P., Silva, J., \u0026amp; Martins, P. (2024). Adaptive and Scalable Database Management with Machine Learning Integration: A PostgreSQL Case Study. \u003cem\u003eInformation\u003c/em\u003e, \u003cem\u003e15\u003c/em\u003e(9), 574. https://doi.org/10.3390/info15090574\u003c/li\u003e\n\u003cli\u003eArndt, S., Wesarg, T., Aschendorff, A., Speck, I., Hocke, T., Jakob, T. F., \u0026amp; Rauch, A.-K. (2024). Prediction of postoperative speech comprehension with the transcutaneous partially implantable bone conduction hearing system Osia\u0026reg;. \u003cem\u003eHNO\u003c/em\u003e, \u003cem\u003e72\u003c/em\u003e(S1), 1\u0026ndash;9. https://doi.org/10.1007/s00106-023-01337-3\u003c/li\u003e\n\u003cli\u003eBr\u0026uuml;mmer, N., Doll-Nikutta, K., Schadzek, P., Mikolai, C., Kampmann, A., Wirth, D., et al. (2025). Better models, better treatment? a systematic review of current three dimensional (3D) in vitro models for implant-associated infections. \u003cem\u003eFrontiers in Bioengineering and Biotechnology\u003c/em\u003e, \u003cem\u003e13\u003c/em\u003e, 1569211. https://doi.org/10.3389/fbioe.2025.1569211\u003c/li\u003e\n\u003cli\u003eChen, X., Zhang, S., Peng, S., Qian, Y., \u0026amp; Zhou, J. (2025). Piezoelectric materials for bone implants: Opportunities and challenges. \u003cem\u003eNano Energy\u003c/em\u003e, \u003cem\u003e138\u003c/em\u003e, 110841. https://doi.org/10.1016/j.nanoen.2025.110841\u003c/li\u003e\n\u003cli\u003eChen, X., Zhou, J., Qian, Y., \u0026amp; Zhao, L. (2023a). Antibacterial coatings on orthopedic implants. \u003cem\u003eMaterials Today Bio\u003c/em\u003e, \u003cem\u003e19\u003c/em\u003e, 100586. https://doi.org/10.1016/j.mtbio.2023.100586\u003c/li\u003e\n\u003cli\u003eChen, X., Zhou, J., Qian, Y., \u0026amp; Zhao, L. (2023b). Antibacterial coatings on orthopedic implants. \u003cem\u003eMaterials Today Bio\u003c/em\u003e, \u003cem\u003e19\u003c/em\u003e, 100586. https://doi.org/10.1016/j.mtbio.2023.100586\u003c/li\u003e\n\u003cli\u003eDillan, T., \u0026amp; Fudholi, D. H. (2023). LDAViewer: An Automatic Language-Agnostic System for Discovering State-of-the-Art Topics in Research Using Topic Modeling, Bidirectional Encoder Representations From Transformers, and Entity Linking. \u003cem\u003eIEEE Access\u003c/em\u003e, \u003cem\u003e11\u003c/em\u003e, 59142\u0026ndash;59163. https://doi.org/10.1109/ACCESS.2023.3285116\u003c/li\u003e\n\u003cli\u003eEbenezer, P., Kumara, S. P. S. N. B. S., Senevirathne, S. W. M. A. I., Bray, L. J., Wangchuk, P., Mathew, A., \u0026amp; Yarlagadda, P. K. D. V. (2025). Advancements in Antimicrobial Surface Coatings Using Metal/Metaloxide Nanoparticles, Antibiotics, and Phytochemicals. \u003cem\u003eNanomaterials\u003c/em\u003e, \u003cem\u003e15\u003c/em\u003e(13), 1023. https://doi.org/10.3390/nano15131023\u003c/li\u003e\n\u003cli\u003eFu, Y., Zhu, M., Shi, A., Zhang, B., \u0026amp; Xu, P. (2025). Stimulus-responsive antibacterial strategies for construction of anti-infection bone implants. \u003cem\u003eNext Materials\u003c/em\u003e, \u003cem\u003e8\u003c/em\u003e, 100554. https://doi.org/10.1016/j.nxmate.2025.100554\u003c/li\u003e\n\u003cli\u003eGawęcki, W., Stieler, O. M., Balcerowiak, A., Komar, D., Gibasiewicz, R., Karlik, M., et al. (2016). Surgical, functional and audiological evaluation of new Baha\u0026reg; Attract system implantations. \u003cem\u003eEuropean Archives of Oto-Rhino-Laryngology\u003c/em\u003e, \u003cem\u003e273\u003c/em\u003e(10), 3123\u0026ndash;3130. https://doi.org/10.1007/s00405-016-3917-5\u003c/li\u003e\n\u003cli\u003eGeorge, L., \u0026amp; Sumathy, P. (2023). An integrated clustering and BERT framework for improved topic modeling. \u003cem\u003eInternational Journal of Information Technology\u003c/em\u003e, \u003cem\u003e15\u003c/em\u003e(4), 2187\u0026ndash;2195. https://doi.org/10.1007/s41870-023-01268-w\u003c/li\u003e\n\u003cli\u003eGupta, R. K., Agarwalla, R., Naik, B. H., Evuri, J. R., Thapa, A., \u0026amp; Singh, T. D. (2022). Prediction of research trends using LDA based topic modeling. \u003cem\u003eGlobal Transitions Proceedings\u003c/em\u003e, \u003cem\u003e3\u003c/em\u003e(1), 298\u0026ndash;304. https://doi.org/10.1016/j.gltp.2022.03.015\u003c/li\u003e\n\u003cli\u003eHagr, A. (2007). BAHA: Bone-Anchored Hearing Aid. \u003cem\u003eInternational Journal of Health Sciences\u003c/em\u003e, \u003cem\u003e1\u003c/em\u003e(2), 265\u0026ndash;276.\u003c/li\u003e\n\u003cli\u003eKrishna Kishor Tirupati, Archit Joshi, Dr S P Singh, Akshun Chhapola, Shalu Jain, \u0026amp; Dr. Alok Gupta. (2024). Leveraging Power BI for Enhanced Data Visualization and Business Intelligence. \u003cem\u003eUniversal Research Reports\u003c/em\u003e, \u003cem\u003e10\u003c/em\u003e(2), 676\u0026ndash;711. https://doi.org/10.36676/urr.v10.i2.1375\u003c/li\u003e\n\u003cli\u003eMa, L., Chen, R., Ge, W., Rogers, P., Lyn-Cook, B., Hong, H., et al. (2025). AI-powered topic modeling: comparing LDA and BERTopic in analyzing opioid-related cardiovascular risks in women. \u003cem\u003eExperimental Biology and Medicine (Maywood, N.J.)\u003c/em\u003e, \u003cem\u003e250\u003c/em\u003e, 10389. https://doi.org/10.3389/ebm.2025.10389\u003c/li\u003e\n\u003cli\u003eNagay, B. E., Malheiros, S. S., Borges, M. H. R., Aparicio, C., Van Den Beucken, J. J. J. P., \u0026amp; Bar\u0026atilde;o, V. A. R. (2025). Progress in visible-light-activated photocatalytic coatings to combat implant-related infections: From mechanistic to translational roadmap. \u003cem\u003eBioactive Materials\u003c/em\u003e, \u003cem\u003e51\u003c/em\u003e, 83\u0026ndash;137. https://doi.org/10.1016/j.bioactmat.2025.04.037\u003c/li\u003e\n\u003cli\u003eOng, J., Nazarian, A., Tam, J., Farinelli, W., Korupolu, S., Drake, L., et al. (2023). An antimicrobial blue light device to manage infection at the skin-implant interface of percutaneous osseointegrated implants. \u003cem\u003ePLOS ONE\u003c/em\u003e, \u003cem\u003e18\u003c/em\u003e(8), e0290347. https://doi.org/10.1371/journal.pone.0290347\u003c/li\u003e\n\u003cli\u003eRoman\u0026ograve;, C. L., Scarponi, S., Gallazzi, E., Roman\u0026ograve;, D., \u0026amp; Drago, L. (2015). Antibacterial coating of implants in orthopaedics and trauma: a classification proposal in an evolving panorama. \u003cem\u003eJournal of Orthopaedic Surgery and Research\u003c/em\u003e, \u003cem\u003e10\u003c/em\u003e(1), 157. https://doi.org/10.1186/s13018-015-0294-5\u003c/li\u003e\n\u003cli\u003eSharma, S., Mohler, J., Mahajan, S. D., Schwartz, S. A., Bruggemann, L., \u0026amp; Aalinkeel, R. (2023). Microbial Biofilm: A Review on Formation, Infection, Antibiotic Resistance, Control Measures, and Innovative Treatment. \u003cem\u003eMicroorganisms\u003c/em\u003e, \u003cem\u003e11\u003c/em\u003e(6), 1614. https://doi.org/10.3390/microorganisms11061614\u003c/li\u003e\n\u003cli\u003eUl Haq, I., \u0026amp; Krukiewicz, K. (2023). Antimicrobial approaches for medical implants coating to prevent implants associated infections: Insights to develop durable antimicrobial implants. \u003cem\u003eApplied Surface Science Advances\u003c/em\u003e, \u003cem\u003e18\u003c/em\u003e, 100532. https://doi.org/10.1016/j.apsadv.2023.100532\u003c/li\u003e\n\u003cli\u003eUru\u0026eacute;n, C., Chopo-Escuin, G., Tommassen, J., Mainar-Jaime, R. C., \u0026amp; Arenas, J. (2020). Biofilms as Promoters of Bacterial Antibiotic Resistance and Tolerance. \u003cem\u003eAntibiotics\u003c/em\u003e, \u003cem\u003e10\u003c/em\u003e(1), 3. https://doi.org/10.3390/antibiotics10010003\u003c/li\u003e\n\u003cli\u003eVillegas, M., Bayat, F., Kramer, T., Schwarz, E., Wilson, D., Hosseinidoust, Z., \u0026amp; Didar, T. F. (2024). Emerging Strategies to Prevent Bacterial Infections on Titanium‐Based Implants. \u003cem\u003eSmall\u003c/em\u003e, \u003cem\u003e20\u003c/em\u003e(46), 2404351. https://doi.org/10.1002/smll.202404351\u003c/li\u003e\n\u003cli\u003eWade, R. (2020). \u003cem\u003eAdvanced Analytics in Power BI with R and Python: Ingesting, Transforming, Visualizing\u003c/em\u003e. Berkeley, CA: Apress. https://doi.org/10.1007/978-1-4842-5829-3\u003c/li\u003e\n\u003cli\u003eWarrier, A., Mazumder, N., Prabhu, S., Satyamoorthy, K., \u0026amp; Murali, T. S. (2021). Photodynamic therapy to control microbial biofilms. \u003cem\u003ePhotodiagnosis and Photodynamic Therapy\u003c/em\u003e, \u003cem\u003e33\u003c/em\u003e, 102090. https://doi.org/10.1016/j.pdpdt.2020.102090\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":"Antibacterial polymers, biofilm, hearing implants, percutaneous implants, topic modeling","lastPublishedDoi":"10.21203/rs.3.rs-7392862/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7392862/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003ePercutaneous bone-contacting hearing implants face significant challenges from bacterial infection and biofilm formation, threatening their long-term success. While antibacterial polymers are a promising solution, the rapid growth of this research field has created a large, complex body of literature without a comprehensive quantitative overview. This study addresses that gap by performing a data-driven literature analysis on a corpus of 4800 articles sourced from ScienceDirect.com. A large-scale quantitative data analytical workflow was employed using Python, PostgreSQL, and Power BI for data curation and visualization. In particular, advanced machine learning techniques, including Latent Dirichlet Allocation (LDA) and Bidirectional Encoder Representations from Transformers (BERT), were applied to the article abstracts to identify underlying research themes. The results show a steep increase in publications after 2010 and confirm \"antibacterial\" as the field's foundational concept. Topic modeling successfully identified eight thematic clusters, revealing a strong interplay between clinical applications (\"Surgical \u0026amp; Interventional Cases\") and materials science (\"Biomaterial Surfaces \u0026amp; Coatings\"). This study provides a comprehensive map of the research field, offering insights to guide future investigations by highlighting key trends and potential gaps.\u003c/p\u003e","manuscriptTitle":"A large-scale quantitative analysis on the antibacterial polymers for use in percutaneous bone-contacting hearing implants","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-08-19 12:41:45","doi":"10.21203/rs.3.rs-7392862/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":"8f64ca88-e131-43ae-b8cc-d09b5a079a67","owner":[],"postedDate":"August 19th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-08-19T12:41:45+00:00","versionOfRecord":[],"versionCreatedAt":"2025-08-19 12:41:45","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7392862","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7392862","identity":"rs-7392862","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}