Work

Deep learning-based framework for cardiac function assessment in embryonic zebrafish from heart beating videos

Year: 2021

Type: article

Source: Computers in Biology and Medicine

DOI: 10.1016/j.compbiomed.2021.104565

Authors: Amir Mohammad Naderi, Haisong Bu, Jingcheng Su, Mao-Hsiang Huang, Khuong Vo, Ramses Seferino Trigo Torres, J.-C. Chiao, Juhyun Lee, Michael P.H. Lau, Xiaolei Xu, Hung Cao

Institutions: University of California, Irvine, Mayo Clinic, National Taiwan University, Southern Methodist University, The University of Texas at Arlington

Language: English

Topic: Zebrafish Biomedical Research Applications

Subfield: Cell Biology

Field: Biochemistry, Genetics and Molecular Biology

Domain: Life Sciences

Open Access status: bronze

Funders: National Science Foundation, U.S. Department of Education, China Scholarship Council, National Institutes of Health, National Heart, Lung, and Blood Institute

Awards: G1098824131, Cloud-based High-throughput Acquisition and Analytics of Zebrafish Electrocardiogram for Cardiac Studies and Drug Development, G6671297155, Elucidation of bioelectromechanics in cardiomyocyte proliferation after injury, CAREER: Wireless Flexible Micro-sensors to Monitor Cardiac Phenotypes in Zebrafish Models of Heart Regeneration, Genetic Studies of Sarcomere-based Cardiac Diseases, Epicardial remodeling in cardiomyopathy and cardiac aging

OpenAlex ID: W3168742615

FWCI: 2.219

Cites: 25

Cited by: 26

Related to: 10