AI-assisted CRISPR-based multiplex diagnostics for rapid pathogen profiling in clinical microbiology

Thesis Abstract

The emergence of antimicrobial resistance and diversified pathogen spectra necessitate rapid, accurate, and scalable diagnostic tools in clinical microbiology; current workflows are often time-consuming, require multiple assays, or lack concurrent pathogen profiling capability, leading to delays in targeted therapy and infection control. This study investigates AI-assisted CRISPR-based multiplex diagnostics to enable rapid profiling of clinically relevant pathogens directly from patient samples. The aim is to develop and evaluate an integrated workflow that combines CRISPR-CRIDR (CRISPR-based identification with dynamic reporter multiplexing) with machine learning to interpret multiplex readouts for simultaneous pathogen detection and resistance gene profiling. Specific objectives include (1) benchmarking a CRISPR-based multiplex assay against standard culture and PCR panels for 20 common bacterial and fungal pathogens using simulated and real clinical specimens; (2) developing a neural network model to classify pathogen identity and resistance determinants from fluorescence surrogate signals and kinetic readouts; (3) optimizing assay conditions to minimize cross-reactivity and enhance sensitivity to 1–100 CFU/mL across mixed infections; (4) validating the end-to-end diagnostic pipeline on a prospective cohort of 400 patient samples across two tertiary care centers; and (5) evaluating turnaround time, cost, and impact on preliminary clinical decision-making. A mixed-methods design is employed, integrating experimental assay development with quantitative predictive modelling and qualitative workflow assessments. The population comprises clinical isolates and de-identified patient specimens collected from adult inpatients with suspected bloodstream, intra-abdominal, or respiratory infections. A two-stage sampling approach is implemented stage one uses archived specimens (n=300) representing diverse etiologies and resistance profiles; stage two prospectively collects consecutive samples (n=400) over 12 months. Data collection instruments include standardized CRISPR-based multiplex assays with fluorescent reporters, microfluidic cartridge systems, a high-sensitivity plate reader for kinetic fluorescence capture, and a portable sequencing-alternative validation module for confirmatory analysis. The analytical framework integrates regression analyses to quantify assay performance metrics (sensitivities, specificities, PPV, NPV), time-to-result distributions, and limits of detection, as well as neural network models (convolutional and recurrent layers) trained on time-series fluorescence traces and reporter intensities to predict pathogen identity and resistance gene presence. Model training employs cross-validation, grid search for hyperparameters, and calibration against reference methods such as culture-based identification and whole-genome sequencing. Additional analyses include receiver operating characteristic (ROC) curve assessment, Bland-Altman agreement for quantitative readouts, and cost-effectiveness modeling using decision-analytic techniques. The expected findings include that AI-assisted CRISPR multiplex diagnostics achieve sensitivity and specificity comparable to conventional culture with substantially reduced turnaround time (median 2.5 hours versus 48–72 hours) and accurate simultaneous detection of at least 20 pathogens with major resistance determinants. The machine learning component is anticipated to yield high classification accuracy (>90%) for pathogen identification and resistance profiling from multiplex readouts, robust to mixed-infection scenarios. The study also aims to demonstrate stable performance across sample types (blood, sputum, abscess fluid) and acceptable repeatability (intra-assay CV <10%, inter-assay CV <15%). The theoretical contribution centers on integrating CRISPR-based diagnostics with data-driven interpretation, advancing the conceptual model of rapid, multiplexed point-of-care microbiology aligned with information theory and diagnostic stewardship. Contributions to knowledge include (i) a validated, scalable AI-enabled CRISPR multiplex platform for rapid pathogen profiling, (ii) an interpretable machine learning framework that maps fluorescence kinetics to clinically actionable results, and (iii) empirical evidence on workflow impact, including reduced empiric therapy time and potential improvements in antimicrobial stewardship. The study also extends the theoretical understanding of how dynamic reporter readouts can be leveraged by supervised learning to resolve multiplex signals in heterogeneous clinical samples, informed by prior theories of diagnostic error and information theory in biomedical sensing. The main conclusion anticipated is that AI-assisted CRISPR-based multiplex diagnostics can provide rapid, accurate, breadth-wide pathogen profiling with resistance determinants, enabling timely and targeted therapeutic decisions. Recommendations include integrating the pipeline into hospital diagnostic laboratories with robust quality management, expanding the panel to emerging pathogens, conducting multicenter trials across diverse healthcare settings, and exploring regulatory pathways for CLIA/ISO-compliant deployment.