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

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.