Related Experiment Video
Updated: Jun 26, 2026

Multiplex Cytokine Profiling of Stimulated Mouse Splenocytes Using a Cytometric Bead-based Immunoassay Platform
Published on: November 9, 2017
Transforming cytokine diagnostics: AI, multiplexing, and point-of-care biosensing technologies
Kutay Icoz1, Zehra Tas2, Fawaz Azizieh3
1College of Engineering and Energy, Abdullah Al Salem University, Khaldiya, Kuwait. kutay.icoz@aasu.edu.kw.
Insights
New biosensor technology combines artificial intelligence (AI) with multiplexed cytokine detection for rapid point-of-care (POC) diagnostics. These AI-enabled systems offer faster, more sensitive immune monitoring, improving clinical decisions and precision medicine.
Area of Science:
- Biomedical Engineering
- Immunology
- Artificial Intelligence
Background:
- Cytokines are crucial immune biomarkers for diseases like cancer and infections.
- Traditional cytokine detection methods are slow and lack portability for clinical use.
- Point-of-care (POC) biosensors offer rapid, decentralized cytokine detection.
Purpose of the Study:
- To review the integration of multiplexed biosensing and AI for cytokine diagnostics.
- To highlight advancements in POC platforms for cytokine detection.
- To discuss the potential of AI in enhancing biosensor performance and clinical utility.
Main Methods:
- Review of literature on traditional and advanced cytokine detection platforms.
- Summary of emerging review articles on cytokine biosensing in various diseases.
- Examination of experimental studies on POC-compatible multiplexed cytokine detection.
- Focus on next-generation biosensors integrating machine learning (ML) algorithms.
Main Results:
- AI-enabled multiplex POC platforms achieve high sensitivity (0.01-100 pg/mL) and rapid results (5-30 min).
- These systems use small sample volumes (1-50 µL) and offer wider dynamic ranges.
- AI enhances biosensor performance with predictive outputs, uncertainty estimates, and drift monitoring.
- ML algorithms like CNNs and decision-tree models enable autonomous signal processing.
Conclusions:
- The convergence of multiplexed biosensing and AI is transforming cytokine diagnostics.
- POC biosensors with AI offer faster, more actionable immune monitoring.
- These technologies hold significant potential for precision medicine and global health.
- Challenges in validation and explainability require further research.
Abstract:
Cytokines are central regulators of immune responses and have emerged as key biomarkers in diverse pathological conditions, including infections, autoimmune disorders, and cancer. Conventional laboratory methods for cytokine detection, while accurate, often lack the speed, portability, and multiplexing capacity required for timely clinical decision-making. Recent advances in biosensor technology-particularly at the point-of-care (POC)-are reshaping this landscape by enabling rapid, decentralized, and sensitive detection of cytokine panels in complex biological samples. AI-enabled multiplex POC platforms now achieve limits of detection as low as 0.01-100 pg/mL, with dynamic ranges spanning 3-4 orders of magnitude, using 1-50 µL of sample and delivering results within 5-30 min. Compared with centralized single-plex workflows, these systems provide faster, lower-volume, and more clinically actionable testing. Artificial intelligence further strengthens performance by providing calibrated predictive outputs, uncertainty estimates, and drift monitoring. This review highlights the convergence of multiplexed biosensing strategies with artificial intelligence (AI) to enhance the analytical performance, interpretability, and clinical utility of cytokine diagnostics. We first discuss the evolution from traditional platforms to portable and miniaturized systems, and then summarize emerging review literature addressing cytokine biosensing in contexts such as sepsis, metabolic disorders, and systemic inflammation. Next, we examine experimental studies demonstrating POC-compatible platforms for multiplexed cytokine detection, and finally focus on next-generation biosensors that integrate machine learning (ML) algorithms-including convolutional neural networks (CNNs) and decision-tree models-for autonomous signal processing and decision support. Despite challenges in validation, hardware integration, and explainability, these technologies hold transformative potential for real-time immune monitoring, precision medicine, and global health applications.
Related Concept Videos
Microbial Biosensors
Automated Microbial Diagnostics

