Artificial Intelligence-assisted Microfluidic Bio-imaging - from Sensors to Applications: A Review

• Published in: IEEE sensors journal 2024-12, p.1-1
• Main Authors: Chen, Jin, Fu, Liangzun, Xiang, Rikui, Luo, Shaobo, Huang, Xiwei, Yu, Hao, Sun, Lingling
• Format: Article
• Language: English
• Subjects: Artificial intelligence; Biosensors; CMOS image sensors; Dynamic Vision Sensor; Frame-based Image Sensor; Image sensors; Imaging; Microfluidic Bio-imaging; Microfluidic Flow Cytometry; Microfluidics; Noise; Optical sensors; Sensor systems; Sensors
• ISSN: 1530-437X
• DOI: 10.1109/JSEN.2024.3507081

In recent years, the convergence of Artificial Intelligence (AI) and microfluidic technologies has given rise to unprecedented advancements in various fields, ranging from healthcare to environmental monitoring. Among which the AI AI-assisted microfluidic biological imaging has been one most widely applied example due to its advantages such as high-throughput and high-content imaging capability as well as the outstanding abilities in analyzing and mining massive data generated by microfluidic bio-imaging systems. AI exhibits significant potential in assisting microfluidic bio-imaging by enhancing imaging resolution and improving classification and detection performances. Therefore, in this review, we focus on some key technologies and recent advancements in AI-assisted microfluidic bio-imaging sensors and presenting discussions from three aspects: sensing devices, AI, and corresponding applications. In the aspect of sensing devices, we offer a detailed introduction to the structure and design of commonly used imaging sensors, including frame-based image sensors and event-based image sensors, and we present two types of frame-based image sensors: charge-coupled devices (CCD) and Complementary Metal-Oxide Semiconductor (CMOS) image sensors. In terms of AI, we present the development process of AI, summarizing various machine learning and deep learning algorithms commonly used in the field of bio-imaging, such as super-resolution, classification, and detection, etc. In terms of application, we provide a list of recent practical applications that integrate various AI techniques with diverse imaging sensors. Finally, we conclude with discussions on the current challenges faced in the field and present potential directions in the future.