國家衛生研究院 NHRI:Item 3990099045/15225

NHRI > Institute of Biomedical Engineering and Nanomedicine > Lun-De Liao > Periodical Articles > Item 3990099045/15225

Please use this identifier to cite or link to this item: http://ir.nhri.org.tw/handle/3990099045/15225

Title:	Unlocking the potential of zebrafish research with artificial intelligence: Advancements in tracking, processing, and visualization
Authors:	Fan, YL;Hsu, FR;Wang, YL;Liao, LD
Contributors:	Institute of Biomedical Engineering and Nanomedicine
Abstract:	Zebrafish have become a widely accepted model organism for biomedical research due to their strong cortisol stress response, behavioral strain differences, and sensitivity to both drug treatments and predators. However, experimental zebrafish studies generate substantial data that must be analyzed through objective, accurate, and repeatable analysis methods. Recently, advancements in artificial intelligence (AI) have enabled automated tracking, image recognition, and data analysis, leading to more efficient and insightful investigations. In this review, we examine key AI applications in zebrafish research, including behavior analysis, genomics, and neuroscience. With the development of deep learning technology, AI algorithms have been used to precisely analyze and identify images of zebrafish, enabling automated testing and analysis. By applying AI algorithms in genomics research, researchers have elucidated the relationship between genes and biology, providing a better basis for the development of disease treatments and gene therapies. Additionally, the development of more effective neuroscience tools could help researchers better understand the complex neural networks in the zebrafish brain. In the future, further advancements in AI technology are expected to enable more extensive and in-depth medical research applications in zebrafish, improving our understanding of this important animal model. This review highlights the potential of AI technology in achieving the full potential of zebrafish research by enabling researchers to efficiently track, process, and visualize the outcomes of their experiments.
Date:	2023-11
Relation:	Medical and Biological Engineering and Computing. 2023 Nov;61(11):2797-2814.
Link to:	http://dx.doi.org/10.1007/s11517-023-02903-1
JIF/Ranking 2023:	http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcAuth=NHRI&SrcApp=NHRI_IR&KeyISSN=0140-0118&DestApp=IC2JCR
Cited Times(WOS):	https://www.webofscience.com/wos/woscc/full-record/WOS:001044655500001
Cited Times(Scopus):	https://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=85167357922
Appears in Collections:	[廖倫德] 期刊論文

Files in This Item:

File	Description	Size	Format
ISI001044655500001.pdf		3001Kb	Adobe PDF	127	View/Open

Loading...