Development of an AI-powered multiplex qPCR platform for rapid bacterial pathogen detection

Thesis Abstract

Rapid and accurate detection of bacterial pathogens remains a critical challenge in clinical diagnostics, water quality assessment, and food safety management due to the limitations of conventional methods such as culture-based techniques and singleplex PCR assays, which are often time-consuming, labor-intensive, and insufficiently sensitive for simultaneous multi-pathogen identification. This study aims to develop an innovative, AI-enhanced multiplex real-time quantitative PCR (qPCR) platform that can concurrently detect and quantify multiple bacterial pathogens with high specificity, sensitivity, and operational efficiency. The specific objectives include designing multiplex primer panels targeting prevalent bacterial strains, integrating machine learning algorithms—particularly convolutional neural networks (CNNs)—for real-time data interpretation, and evaluating the platform’s performance relative to existing diagnostic methods. The study employed a mixed-methods research design encompassing experimental development and validation phases, grounded in the pragmatic philosophical paradigm. The target population comprised bacterial cultures and clinical or environmental samples known to harbor key bacterial pathogens, including Escherichia coli, Salmonella spp., Listeria monocytogenes, and Pseudomonas aeruginosa. A sample size of 200 specimens was selected through stratified random sampling, ensuring diversity across different sample types (clinical, food, environmental). Data collection involved the synthesis of DNA from samples, the development of multiplex primer sets using bioinformatics tools, and the application of the designed primers in the laboratory to generate qPCR data. The AI component entailed training CNN-based models using labeled amplification curves to classify positive and negative detections rapidly. Data analysis incorporated several analytical techniques quantitative analysis of assay sensitivity and specificity using receiver operating characteristic (ROC) curves; comparison of detection accuracy between the AI-enhanced multiplex platform and conventional singleplex assays via paired t-tests; and performance evaluation of the machine learning model through confusion matrices, precision, recall, and F1-scores. Model optimization employed hyperparameter tuning using grid search, and the relationship between pathogen load and detection parameters was examined via regression analysis. The underlying theoretical framework draws on the Information Processing Theory to justify the integration of AI for real-time decision-making, and the Technology Acceptance Model (TAM) to explore user adoption potentials. Expected findings include the successful design of a multiplex primer panel capable of detecting multiple pathogens within a single assay, with sensitivity exceeding 95% and specificity above 98%. The AI-driven interpretation system is anticipated to outperform conventional algorithms in terms of accuracy, speed, and robustness, achieving an F1-score above 0.95. The platform is projected to reduce detection time by at least 50%, providing actionable results within 1-2 hours post-sampling, which is a substantial improvement over current methods. The study is expected to reveal strong correlations between pathogen concentrations and qPCR amplification parameters, with machine learning models effectively classifying pathogen presence even at low bacterial loads. This research contributes to existing knowledge by demonstrating the feasibility and effectiveness of combining multiplex qPCR technology with artificial intelligence to revolutionize pathogen detection paradigms. It addresses critical gaps related to multi-pathogen diagnostics, real-time data analysis, and operational scalability, which are inadequately covered in current literature. The developed platform offers a scalable, cost-effective, and user-friendly diagnostic tool suitable for deployment in resource-limited settings, thereby enhancing disease surveillance, outbreak response, and food safety measures. The study concludes by recommending broader validation across diverse geographical regions and sample matrices, integration with portable PCR devices for field application, and further development of AI models to incorporate additional pathogen detection capabilities. Policymakers and diagnostic laboratories are encouraged to consider adopting such integrated technologies to accelerate pathogen detection workflows and respond more effectively to infectious disease outbreaks and contamination events.