Development of AI-based Diagnostic Tool for Rapid Identification of Bacterial Pathogens

Thesis Abstract

The rapid and accurate identification of bacterial pathogens remains a critical challenge in clinical microbiology, with conventional diagnostic methods often hampered by lengthy processing times, limited sensitivity, and the need for specialized expertise, thereby delaying effective treatment and contributing to the rising threat of antimicrobial resistance. This study aims to develop an advanced artificial intelligence (AI)-based diagnostic tool optimized for the swift identification of bacterial pathogens directly from clinical samples. The specific objectives include (1) designing a machine learning model capable of classifying bacterial species using genomic and phenotypic data, (2) integrating sensor and image processing techniques to enhance data acquisition accuracy, and (3) evaluating the diagnostic performance of the AI tool against existing laboratory protocols. The research adopts a mixed-methods approach, employing a quantitative research design predominantly centered on supervised machine learning algorithms, complemented by qualitative assessments of implementation feasibility. The population comprises bacterial isolates obtained from 1,000 clinical patient samples collected over a 12-month period across three major microbiology laboratories within a metropolitan healthcare network. A stratified random sampling technique was used to select samples representing twenty common bacterial pathogens such as Escherichia coli, Staphylococcus aureus, Pseudomonas aeruginosa, and Salmonella spp. Data collection involved high-throughput genomic sequencing, MALDI-TOF mass spectrometry, and microscopy imaging, alongside metadata including antimicrobial susceptibility profiles. The primary data analysis utilizes convolutional neural networks (CNNs) for image and spectral data interpretation, and gradient boosting machines for genomic feature classification. Data will be processed and validated through k-fold cross-validation and Receiver Operating Characteristic (ROC) curve analysis to assess diagnostic accuracy, sensitivity, specificity, and F1 scores. The expected findings include the development of a robust AI model capable of achieving at least 95% accuracy in bacterial identification within 30 minutes post-sample collection, significantly reducing turnaround times compared to traditional culture-based methods. The model is anticipated to outperform existing diagnostic algorithms with an increase in sensitivity and specificity by approximately 10-15%. The AI tool is also expected to demonstrate high reproducibility and scalability, facilitating its potential for deployment in resource-limited settings and point-of-care environments. Furthermore, insights from the integrated sensor and image processing modules are expected to improve data quality, thereby enhancing model performance. This research makes a substantial contribution to the existing knowledge by demonstrating the feasibility and effectiveness of AI-driven diagnostic systems in microbiology, particularly emphasizing the integration of multi-modal data sources—genomic, spectral, and imaging—for pathogen identification. It advances the application of deep learning techniques in clinical diagnostics and provides a validated framework for deploying automated microbial identification systems in healthcare settings. Theoretical frameworks underpinning this study include the Information Processing Theory, explaining how data inputs are transformed into diagnostic outputs, and the Diffusion of Innovations Theory, guiding the adoption of AI technologies in microbiological practice. The main conclusion underscores that AI-based diagnostic tools offer a transformative approach to bacterial pathogen identification, markedly improving diagnostic speed and accuracy while reducing costs and labor. It is recommended that healthcare institutions adopt such AI solutions to augment traditional microbiological techniques, with further research focusing on expanding pathogen databases, enhancing model interpretability, and assessing real-world clinical impacts. This study provides a foundation for subsequent development of comprehensive, autonomous diagnostic platforms and offers policy guidelines for integrating AI methodologies into routine microbiological diagnostics, ultimately contributing to improved patient outcomes and antimicrobial stewardship programs.