A review on recent advances of AI-integrated microfluidics for analytical and bioanalytical applications

• Published in: TrAC, Trends in analytical chemistry (Regular ed.) Vol. 181; p. 118004
• Main Authors: Asadian, Elham, Bahramian, Farshad, Siavashy, Saeed, Movahedi, Sara, Keçili, Rüstem, Hussain, Chaudhery Mustansar, Ghorbani-Bidkorpeh, Fatemeh
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.12.2024
• Subjects: Artificial intelligence; Cancer; Complete blood count; Deep learning; Microfluidics platform; Point-of-care; Deep learning; Complete blood count; Microfluidics platform; Point-of-care; Artificial intelligence; Cancer
• ISSN: 0165-9936
• DOI: 10.1016/j.trac.2024.118004

In today's biomedical research, the pursuit of diagnostic tools boasting maximum precision and accuracy while minimizing sample volume and pre-treatment requirements, has intensified. In this regard, microfluidic devices offer promising solutions by reducing sample size and overall research costs. However, the intricate and time-consuming nature of the data obtained from such devices poses significant challenges. To overcome these difficulties, researchers have increasingly turned to the integration of artificial intelligence (AI) with microfluidic platforms, resulting in the emergence of “AI-integrated microfluidics”. Recent advances in computer-related fields can transform AI from a theoretical science to a useful tool for various studies which is anticipated to become an integral part of human life. This review provides a comprehensive overview of various approaches for combining AI algorithms with microfluidic platforms for analytical and bioanalytical assessments. Highlighting applications ranging from cell classification and disease detection to point-of-care diagnostics, the paper underscores the transformative potential of AI-integrated microfluidics in advancing biomedical research and clinical diagnostics. [Display omitted] •This review explores transformative potential of AI in advancing bioanalytics.•AI-integrated microfluidics enhance precision in biomedical diagnostics and research.•AI algorithms improve data analysis for complex microfluidic datasets effectively.•Applications include disease detection, cell classification, and POC diagnostics.•Platforms merge cost-effectiveness with AI's automated data handling and analysis.