GEN-CRISP: Non-Invasive Diagnostic Imaging Using CRISPR Nanoparticle Hyperspectral AI for Rapid Detection of Antibiotic Resistance Genes (ARGs) in Pathogenic Bacterial Infections

Abstract

The global escalation of antimicrobial resistance (AMR) represents a critical challenge to modern healthcare systems, driven by the proliferation of antibiotic resistance genes (ARGs) in pathogenic bacteria. This study aims to analyze the potential of a non-invasive diagnostic approach integrating CRISPR-based nanoparticles, hyperspectral imaging, and artificial intelligence (AI) for rapid ARG detection. A qualitative approach was employed using library research methods, content analysis, and theoretical review of recent scientific literature. Epidemiological data indicate that AMR was directly responsible for approximately 1.27 million deaths and associated with 4.95 million deaths globally, while more than 2.8 million resistant infections occur annually in the United States. In Southeast Asia, resistance prevalence in Escherichia coli exceeds 50% for third-generation cephalosporins, highlighting diagnostic urgency. The analysis reveals that conventional diagnostic methods, such as culture and PCR, are limited by time constraints and operational complexity. In contrast, the proposed integration of CRISPR-nanoparticle biosensors with hyperspectral imaging enables non-invasive detection via exhaled breath, producing fluorescence signals in the near-infrared spectrum. AI-based computer vision further enhances real-time analysis with reported diagnostic accuracy reaching 97–98% and processing time under 20 minutes. The findings suggest that this integrated system significantly improves early detection, reduces diagnostic delays, and supports targeted antibiotic therapy. In conclusion, non-invasive CRISPR-based hyperspectral AI diagnostics present a promising, efficient, and scalable solution to mitigate AMR impact and strengthen global health resilience.