Development of Fully Automated Deep Learning Methods for Characterising Cardiac Function in Patients with Atrial Fibrillation

Duan, Jinming (Principal Investigator)

Computer Science

Short title	Development of Fully Automated Deep Learning Methods for Characterising Cardiac Function in Patients with Atrial Fibrillation
Status	Finished
Effective start/end date	1/12/20 → 31/05/22

Funding

British Heart Foundation

1 Article

Mechanosensitive biochemical imprinting of the talin interaction with DLC1 regulates RhoA activity and cardiomyocyte remodeling
Marhuenda, E., Xanthis, I., O. Smith, P., Prakash , A., Kallem, T., Pandey, P., Wilson, D. G. S., Azad, A., Richter, M., Pavlovic, D., Gehmlich, K., Faggian, G., Swiatlowska, P., Ehler, E., Levitt, J., Poland, S. P., Ameer-Beg, S., Goult , B. T. & Iskratsch, T., 5 Sept 2025, In: Science Advances. 11, 36, 16 p.
Research output: Contribution to journal › Article › peer-review

Open Access
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