Enhancing single-cell biology through advanced AI-powered microfluidics

被引:5
|
作者
Gao, Zhaolong [1 ]
Li, Yiwei [1 ]
机构
[1] Huazhong Univ Sci & Technol, Coll Life Sci & Technol, Dept Biomed Engn, Key Lab Biomed Photon MOE,Wuhan Natl Lab Optoelect, Wuhan 430074, Peoples R China
基金
中国国家自然科学基金;
关键词
Cell signaling - Cells - Clinical research - Cytology - Diagnosis;
D O I
10.1063/5.0170050
中图分类号
Q5 [生物化学];
学科分类号
071010 ; 081704 ;
摘要
Microfluidic technology has largely benefited both fundamental biological research and translational clinical diagnosis with its advantages in high-throughput, single-cell resolution, high integrity, and wide-accessibility. Despite the merits we obtained from microfluidics in the last two decades, the current requirement of intelligence in biomedicine urges the microfluidic technology to process biological big data more efficiently and intelligently. Thus, the current readout technology based on the direct detection of the signals in either optics or electrics was not able to meet the requirement. The implementation of artificial intelligence (AI) in microfluidic technology matches up with the large-scale data usually obtained in the high-throughput assays of microfluidics. At the same time, AI is able to process the multimodal datasets obtained from versatile microfluidic devices, including images, videos, electric signals, and sequences. Moreover, AI provides the microfluidic technology with the capability to understand and decipher the obtained datasets rather than simply obtaining, which eventually facilitates fundamental and translational research in many areas, including cell type discovery, cell signaling, single-cell genetics, and diagnosis. In this Perspective, we will highlight the recent advances in employing AI for single-cell biology and present an outlook on the future direction with more advanced AI algorithms.
引用
收藏
页数:6
相关论文
共 50 条
  • [31] Single-cell biology
    Nature, 2017, 547 : 19 - 19
  • [32] AI-Powered Image Security: Utilizing Autoencoders for Advanced Medical Image Encryption
    Alqahtani, Fehaid
    CMES-COMPUTER MODELING IN ENGINEERING & SCIENCES, 2024, 141 (02): : 1709 - 1724
  • [33] AI-Powered Integrated With Encoding Mechanism Enhancing Privacy, Security, and Performance for IoT Ecosystem
    Farea, Ali Hamid
    Alhazmi, Omar H.
    Samet, Refik
    Guzel, Mehmet Serdar
    IEEE ACCESS, 2024, 12 : 121368 - 121386
  • [34] Enhancing User Experience in Chinese Initial Text Conversations with Personalised AI-Powered Assistant
    Wang, Jindi
    Ivrissimtzis, Ioannis
    Li, Zhaoxing
    Shi, Lei
    EXTENDED ABSTRACTS OF THE 2024 CHI CONFERENCE ON HUMAN FACTORS IN COMPUTING SYSTEMS, CHI 2024, 2024,
  • [35] Enhancing Medical Image Analysis with AI-Powered Image Recognition: A Deep Learning Approach
    Wei, Jingxuan
    Yan, Jing
    Sun, Qing
    Lin, Na
    WIENER KLINISCHE WOCHENSCHRIFT, 2024, 136 : S458 - S459
  • [36] Enhancing Peer Review with AI-Powered Suggestion Generation Assistance: Investigating the Design Dynamics
    Neshaei, Seyed Parsa
    Rietsche, Roman
    Su, Xiaotian
    Wambsganss, Thiemo
    PROCEEDINGS OF 2024 29TH ANNUAL CONFERENCE ON INTELLIGENT USER INTERFACES, IUI 2024, 2024, : 88 - 102
  • [37] AI-Powered AOP: Enhancing Runtime Monitoring with Large Language Models and Statistical Learning
    Alsobeh, Anas
    Shatnawi, Amani
    Al-Ahmad, Bilal
    Aljmal, Alhan
    Khamaiseh, Samer
    INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS, 2024, 15 (11) : 121 - 133
  • [38] Sports Production Through AI-Powered Sports Action Tracking and PTZ Cameras
    Edwards D.
    Imming S.
    SMPTE Motion Imaging Journal, 2023, 132 (10): : 6 - 12
  • [39] Drone Insights: Unveiling Beach Usage through AI-Powered People Counting
    Herrera, Cesar
    Connolly, Rod M.
    Rasmussen, Jasmine A.
    McNamara, Gerrard
    Murray, Thomas P.
    Lopez-Marcano, Sebastian
    Moore, Matthew
    Campbell, Max D.
    Alvarez, Fernando
    DRONES, 2024, 8 (10)
  • [40] Digitalization of railway transportation through AI-powered services: digital twin trains
    Sarp, Salih
    Kuzlu, Murat
    Jovanovic, Vukica
    Polat, Zekeriya
    Guler, Ozgur
    EUROPEAN TRANSPORT RESEARCH REVIEW, 2024, 16 (01)