AI-driven multiplex biosensor for rapid pathogen detection in clinical labs

Thesis Abstract

In modern clinical laboratories, the timely and accurate detection of multiple pathogens from diverse specimens remains a critical bottleneck due to reliance on isolated assays, consumable delays, and limited multiplexing capabilities. This study addresses the problem by developing and validating an AI-driven multiplex biosensor platform capable of simultaneous, rapid detection of bacterial and viral pathogens with minimal sample preparation. The aim is to enhance diagnostic throughput, accuracy, and decision-support in routine lab workflows. Specific objectives include (1) designing a chip-scale biosensor integrating optical and electrochemical transducers with machine learning–enabled signal deconvolution, (2) optimizing a multiplex panel for ten clinically relevant pathogens, (3) evaluating analytical performance against gold-standard methods, and (4) assessing workflow integration and operator usability in real-world laboratory settings. A mixed-methods methodology is employed. The research adopts an explanatory sequential design beginning with technical validation in a controlled laboratory environment, followed by a pragmatic field evaluation in three tertiary clinical laboratories. The study population comprises clinical samples from suspected infectious cases 600 patient specimens (blood, urine, sputum, and nasopharyngeal swabs) collected over 12 months, with stratified sampling to ensure representation of common pathogens including Escherichia coli, Staphylococcus aureus, Klebsiella pneumoniae, Pseudomonas aeruginosa, Streptococcus pneumoniae, Mycoplasma pneumoniae, influenza A/B, SARS-CoV-2, and adenovirus. Instrumentation includes a microfabricated multiplex biosensor chip integrating plasmonic and electrochemical readouts, coupled with an embedded AI inference engine. Data collection employs standardized qPCR/next-generation sequencing references for pathogen confirmation and operator surveys for usability data. Analytical techniques include descriptive statistics, sensitivity, specificity, positive and negative predictive values, and receiver operating characteristic (ROC) analysis for each target. Regression analysis and Bland-Altman plots assess agreement with reference methods; machine learning algorithms (random forest, gradient boosting, and convolutional neural networks) are used for signal deconvolution, noise reduction, and pathogen call decisions. The theoretical framework leverages the Technology Acceptance Model to evaluate adoption factors and the Diffusion of Innovations theory to interpret integration outcomes, with a conceptual model linking sensor performance, AI accuracy, and laboratory workflow efficiency. Key expected findings include demonstrated analytical sensitivity and specificity above 95% for the multiplex panel, with limit of detection in the low-to-mid range for bacterial targets and comparable performance for viral targets relative to gold-standard assays. AI-driven signal processing is anticipated to outperform conventional threshold-based decoding, achieving robust performance across variable matrix effects and inhibition. The study expects high concordance (kappa > 0.85) with reference methods and improved turnaround times from 6–8 hours to under 2 hours for most panels. Usability assessments are projected to reveal acceptable to high ease of use and minimal perceived threat to workflow disruption, with identified facilitators and barriers informing deployment strategies. The research will also elucidate sources of misclassification, particularly in low-abundance scenarios or mixed infections, and quantify the impact of sample type on performance. The contribution to knowledge includes (i) a validated AI-enhanced multiplex biosensor architecture suitable for deployment in routine clinical laboratories, (ii) a rigorously characterized analytical performance profile across diverse specimen types and pathogens, (iii) a data-driven framework for real-time pathogen calling and uncertainty estimation, and (iv) empirical insights into implementation determinants framed by TAM and Diffusion of Innovations theory. The study concludes that the AI-driven multiplex biosensor accelerates pathogen detection without compromising accuracy, enabling timely therapeutic decisions and improved patient outcomes. Recommendations emphasize iterative refinement of the AI models with continual learning from new pathogen data, standardization of pre-analytical protocols to minimize matrix effects, and phased implementation with clinical governance, quality assurance, and staff training to ensure sustainable integration into laboratory practice.