Sepsis Diagnostics via Biosensors: Engineering Platforms, Artificial Intelligence Integration, and Clinical...

Area of Science:

• Biomedical Engineering
• Clinical Diagnostics
• Translational Medicine

Background:

• Sepsis diagnosis is complex due to heterogeneity and lack of biomarkers.
• Current culture-based methods are slow and lack real-time capabilities.
• Biosensors offer a promising alternative for rapid, multiplexed sepsis detection.

Purpose of the Study:

• To review recent advances in biosensor technology for sepsis diagnostics.
• To explore the role of artificial intelligence in augmenting biosensor performance.
• To identify and address translational bottlenecks hindering clinical deployment.

Main Methods:

• Synthesis of advances in substrate engineering, nanomaterial amplification, and biorecognition elements (aptamers, AMPs, PNAs, XNAs, CRISPR).
• Analysis of real-world case studies demonstrating clinical feasibility.
• Delineation of AI models for biosensor signal interpretation versus EHR-driven prediction.

Main Results:

• Significant progress in biosensor components and amplification strategies.
• Demonstrated clinical feasibility through case studies.
• Distinction between AI for signal interpretation and broader predictive frameworks.

Conclusions:

• Biosensors, enhanced by AI, represent a paradigm shift in sepsis diagnostics.
• Addressing translational gaps (performance, regulatory, reimbursement) is crucial for deployment.
• A collaborative approach is needed to accelerate biosensor integration into sepsis care.