AI-Driven Improvements in Electrochemical Biosensors for Effective Pathogen Detection at Point-of-Care

The rapid and accurate detection of pathogens is vital for effective disease management and control. This paper introduces a novel approach to pathogen detection by integrating artificial intelligence (AI) into electrochemical biosensors. Real-world samples can present background interference from other analytes and unwanted noise in the signal, particularly when utilizing portable point-of-care devices. To overcome these challenges, we propose an intelligent electrochemical device optimized for improved performance in detecting viral pathogens. Our approach involves two key AI strategies. First, a denoising autoencoder is employed to effectively remove noise from the electrochemical signals, bringing the performance of portable devices on par with their standalone counterparts. This enhancement is crucial for point-of-care applications where environmental and operational factors often compromise data quality. Second, we utilize an Artificial Neural Network (ANN) to detect the presence of background interference. Smartphones are often used as interface for portable electrochemical devices, our approach leverages the computational capabilities of smartphones to run the AI algorithms for processing the electrochemical signals in real-time. The proposed system has been validated using COVID-19 data, demonstrating its potential as a powerful tool in the rapid and accurate detection of SARS-CoV-2 and other pathogens. The integration of AI into electrochemical biosensing offers a more reliable and accessible option for healthcare professionals and researchers.