| Literature DB >> 36026569 |
Samaneh Zare Harofte1, Madjid Soltani1,2,3,4,5, Saeed Siavashy1, Kaamran Raahemifar6,7,8.
Abstract
Nowadays, artificial intelligence (AI) creates numerous promising opportunities in the life sciences. AI methods can be significantly advantageous for analyzing the massive datasets provided by biotechnology systems for biological and biomedical applications. Microfluidics, with the developments in controlled reaction chambers, high-throughput arrays, and positioning systems, generate big data that is not necessarily analyzed successfully. Integrating AI and microfluidics can pave the way for both experimental and analytical throughputs in biotechnology research. Microfluidics enhances the experimental methods and reduces the cost and scale, while AI methods significantly improve the analysis of huge datasets obtained from high-throughput and multiplexed microfluidics. This review briefly presents a survey of the role of AI and microfluidics in biotechnology. Also, the incorporation of AI with microfluidics is comprehensively investigated. Specifically, recent studies that perform flow cytometry cell classification, cell isolation, and a combination of them by gaining from both AI methods and microfluidic techniques are covered. Despite all current challenges, various fields of biotechnology can be remarkably affected by the combination of AI and microfluidic technologies. Some of these fields include point-of-care systems, precision, personalized medicine, regenerative medicine, prognostics, diagnostics, and treatment of oncology and non-oncology-related diseases.Entities:
Keywords: artificial intelligence-on-a-chip; deep learning; lab-on-a-chip; machine learning; microfluidics
Mesh:
Year: 2022 PMID: 36026569 DOI: 10.1002/smll.202203169
Source DB: PubMed Journal: Small ISSN: 1613-6810 Impact factor: 15.153