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Publications

Journal Article Review Epublish 8 citations

PubMed: 40735651 DOI: 10.1016/j.btre.2025.e00905

Enhancing cell characterization with microfluidics and AI: a comprehensive review of mechanical, electrical, and hybrid techniques.

Biotechnol Rep (Amst) 47:e00905

Authors: Marwan Taha, Anna-Maria Pappa, Hani Saleh, Anas Alazzam

Affiliations

  • Department of Mechanical and Nuclear Engineering, Khalifa University, Abu Dhabi 127788, United Arab Emirates. System on Chip Lab, Khalifa University, Abu Dhabi, 127788, United Arab Emirates.
  • Department of Biomedical Engineering, Khalifa University, Abu Dhabi, United Arab Emirates.
  • Center of Catalysis and Separations, Khalifa University, Abu Dhabi, United Arab Emirates.
  • Healthcare Engineering Innovation Center, Khalifa University, Abu Dhabi, United Arab Emirates.
  • Computer & Communication Engineering, Khalifa University, Abu Dhabi, United Arab Emirates.
  • System on Chip Lab, Khalifa University, Abu Dhabi, 127788, United Arab Emirates.
  • Department of Mechanical and Nuclear Engineering, Khalifa University, Abu Dhabi 127788, United Arab Emirates.

Abstract

This paper examines recent advancements in cell characterization using microfluidic devices, emphasizing mechanical, electrical, and hybrid methodologies. These technologies have substantially improved throughput, precision, and the range of cell types they can analyze. Key microfluidic technologies for cell characterization are reviewed, including label-free electrical and mechanical methods designed for high-throughput, real-time analysis. Microfluidic advancements in cell characterization are critically assessed, along with challenges such as operational complexity and the need for more adaptable, user-friendly platforms. The integration of AI and machine learning in microfluidic systems is also discussed, highlighting their crucial role in automating data analysis and enhancing classification accuracy, with implications for personalized medicine and advanced cellular assays.

MeSH

Cells, Microfluidics, Artificial Intelligence