A deep dive into AI integration and advanced nanobiosensor technologies for enhanced bacterial infection monitoring

Abstract

Abstract The emergence of smart and nanobiosensor (NB) technologies has transformed the monitoring and management of bacterial infections. These developments offer remarkable accuracy and precision for detecting infectious pathogens. Smart artificial intelligence (AI)-assisted and NB-based methods are used as powerful tools in biomedicine for bacterial detection, combatting multidrug resistance, and diagnosing infections. In this study, we delve into the advancements in these technologies, focusing on AI-based techniques for NBs in detecting bacterial infections from 2019 to 2024. We analyze the contributions of machine learning and deep learning techniques to enhance performance and reliability. The new approaches to improve the effectiveness and versatility of antibacterial treatments are critically analyzed. Our study includes the observations of carbon nanoparticles that selectively target bacteria using photothermal properties and the production of hybrid hydrogel composites with capabilities. Furthermore, the study emphasizes the crucial significance of NBs in propelling the progress of diagnostic methods, biosensing technologies, and treatments, thereby transforming the healthcare industry and the way diseases are managed. In addition, we explore pathogen-based infections, bacterial diagnosis, and treatment using engineered NBs enhanced with various modalities such as electrochemistry, acoustics, electromagnetism, and photothermal resonance. Our comprehensive review highlights the potential and throws light on future research directions for effective management and control of bacterial infections.