AI-assisted metagenomic mining for rapid pathogen detection in clinical microbiology

Thesis Abstract

In the clinical microbiology landscape, rapid and accurate pathogen detection is critical for timely patient management and infection control, yet traditional culture-based methods and conventional molecular assays are often limited by time-to-result, sensitivity, and the ability to detect novel or rare pathogens. This study aims to develop and evaluate an AI-assisted metagenomic mining framework to accelerate and improve pathogen detection from clinical samples, integrating high-throughput sequencing data with machine learning inference to yield rapid, actionable results. Specific objectives include (1) to curate a comprehensive metagenomic reference database combining viral, bacterial, fungal, and archaeal genomes with curated metadata; (2) to design and train deep learning models capable of real-time taxonomic classification and virulence factor detection from shotgun metagenomic reads; (3) to implement an optimized bioinformatics pipeline that reduces computational time while maintaining or enhancing diagnostic accuracy; (4) to validate the framework on a diverse set of clinical specimens (blood, respiratory, and cerebrospinal fluid) with known culture or molecular confirmation; and (5) to assess user-oriented performance metrics including turnaround time, interpretability of model outputs, and integration feasibility within existing clinical laboratory workflows. The research adopts a cross-sectional diagnostic study design, leveraging retrospective and prospective clinical samples. The population comprises clinical specimens submitted for infectious disease evaluation over a 24-month period from three tertiary care centers. A stratified random sampling approach yields 1,500 samples (500 blood, 500 respiratory, 500 cerebrospinal fluid) with confirmed etiologies to serve as the validation cohort, alongside 300 anonymized negative controls. Data collection involves (i) whole-genome shotgun sequencing using Illumina NovaSeq platforms to generate high-depth metagenomic reads, (ii) associated clinical metadata, and (iii) gold-standard reference results (culture, PCR, or sequencing-based confirmed diagnoses). The primary instruments are sequencing libraries, standard bioinformatics software for preprocessing, and the custom AI-assisted mining pipeline. Model development employs supervised learning with labeled taxa and virulence determinants, incorporating techniques such as convolutional neural networks for read-level classification, transformer-based models for contextual taxonomic inference, and ensemble methods for robust decision-making. Feature engineering draws on k-mer profiles, alignment-free similarity measures, and functional annotations from curated virulence factor databases. Validation includes 5-fold cross-validation, receiver operating characteristic (ROC) analysis, area under the curve (AUC) metrics, precision-recall curves, and calibration plots to assess probabilistic outputs. Statistical comparisons use McNemar’s test for binary decisions and DeLong’s test for correlated AUCs, with significance set at p < 0.05. Anticipated findings indicate that the AI-assisted metagenomic framework will achieve superior sensitivity and specificity for clinically relevant pathogens compared with conventional metagenomic pipelines, reducing mean turnaround time from 24–48 hours to approximately 6–8 hours per sample. The model is expected to maintain stable performance across specimen types, with AUCs exceeding 0.92 for the primary pathogens of interest and high concordance with culture-confirmed results. The approach should demonstrate effective detection of non-culturable or rare organisms, and accurate discernment of virulence determinants that inform clinical risk stratification and therapeutic decisions. The study will also elucidate how model explainability techniques, such as saliency maps and feature attribution, can provide interpretable reasoning to laboratory personnel and clinicians, thereby enhancing trust and uptake. Contributions to knowledge include a validated, scalable AI-driven framework for rapid pathogen detection in clinical microbiology, an integrated reference database with enhanced taxonomic and virulence annotation, and a comparative performance assessment against standard metagenomic workflows. The research will offer practical guidance for implementing AI-assisted metagenomics in routine diagnostics, including data governance, quality assurance, and regulatory considerations, while highlighting potential biases and the importance of continuous model updating with new pathogen genomes. The principal conclusion posits that AI-enabled metagenomic mining can substantially shorten diagnostic timelines and improve detection breadth without compromising accuracy, and it recommends phased clinical deployment, ongoing performance monitoring, inter-laboratory proficiency testing, and collaboration with regulatory bodies to establish standardized evaluation frameworks for AI-assisted infectious disease diagnostics.