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Sepsis Diagnostics via Biosensors: Engineering Platforms; Artificial Intelligence Integration; and Clinical Translation
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DOI：10.1021/acssensors.5c01917.png)
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Abstract
En 中文
Sepsis remains one of the most diagnostically challenging syndromes due to its clinical heterogeneity, overlapping host−pathogen responses, and lack of a singular biomarker. Biosensors are redefining this landscape by moving beyond slow, culture-based methods toward real-time, multiplexed, and clinically validated diagnostic platforms. In this review, we synthesize advances across substrate engineering, nanomaterial-enabled amplification, and next-generation biorecognition elements, including aptamers, AMPs, PNAs, XNAs, and CRISPR systems, supported by real-world case studies that demonstrate clinical feasibility. Furthermore, the role of artificial intelligence in augmenting sepsis diagnostics is delineated by distinguishing models that enhance biosensor signal interpretation from broader EHR-driven prediction frameworks. A dedicated section consists of the translational bottlenecks: analytical-to-clinical performance gaps, regulatory barriers, diagnostic coding mismatches, and reimbursement challenges under DRG and NTAP pathways. Finally, we propose a forward-looking blueprint for accelerating biosensor deployment in sepsis care, emphasizing the need for harmonized clinical validation, design-for-deployment strategies, and collaborative innovation ecosystems. Unlike generic biosensor or AI reviews, this work advances explicitly against sepsis’s diagnostic heterogeneity, offering a disease-centered translational roadmap.
Keywords:
Anatomy
Biomarkers
Biotechnology
Diagnostic imaging
Sensors
multiplexed sepsis biomarker detection
AI-integration
clinical matrix compatibility
translational biosensor readiness
regulatory and reimbursement barriers
wearable sepsis sensors
contextual diagnostic intelligence
point-of-care biosensing
temporal diagnostics
Journal
IF:
9.1
Papers:
976
Citations:
2.6W