Deep Learning-Enabled Assessment of Left Heart Structure and Function Predicts Cardiovascular Outcomes.

Autor: Lau ES; Division of Cardiology, Department of Medicine, Massachusetts General Hospital, Boston, Massachusetts, USA; Cardiovascular Research Center, Massachusetts General Hospital, Boston, Massachusetts, USA; Cardiovascular Disease Initiative, Broad Institute of Harvard University and the Massachusetts Institute of Technology, Cambridge, Massachusetts, USA., Di Achille P; Data Sciences Platform, Broad Institute of Harvard University and the Massachusetts Institute of Technology, Cambridge, Massachusetts, USA., Kopparapu K; Data Sciences Platform, Broad Institute of Harvard University and the Massachusetts Institute of Technology, Cambridge, Massachusetts, USA., Andrews CT; Division of Cardiology, Department of Medicine, Massachusetts General Hospital, Boston, Massachusetts, USA., Singh P; Data Sciences Platform, Broad Institute of Harvard University and the Massachusetts Institute of Technology, Cambridge, Massachusetts, USA., Reeder C; Data Sciences Platform, Broad Institute of Harvard University and the Massachusetts Institute of Technology, Cambridge, Massachusetts, USA., Al-Alusi M; Division of Cardiology, Department of Medicine, Massachusetts General Hospital, Boston, Massachusetts, USA; Cardiovascular Research Center, Massachusetts General Hospital, Boston, Massachusetts, USA; Cardiovascular Disease Initiative, Broad Institute of Harvard University and the Massachusetts Institute of Technology, Cambridge, Massachusetts, USA., Khurshid S; Division of Cardiology, Department of Medicine, Massachusetts General Hospital, Boston, Massachusetts, USA; Cardiovascular Research Center, Massachusetts General Hospital, Boston, Massachusetts, USA; Cardiovascular Disease Initiative, Broad Institute of Harvard University and the Massachusetts Institute of Technology, Cambridge, Massachusetts, USA., Haimovich JS; Division of Cardiology, Department of Medicine, Massachusetts General Hospital, Boston, Massachusetts, USA; Cardiovascular Research Center, Massachusetts General Hospital, Boston, Massachusetts, USA; Cardiovascular Disease Initiative, Broad Institute of Harvard University and the Massachusetts Institute of Technology, Cambridge, Massachusetts, USA., Ellinor PT; Division of Cardiology, Department of Medicine, Massachusetts General Hospital, Boston, Massachusetts, USA; Cardiovascular Research Center, Massachusetts General Hospital, Boston, Massachusetts, USA; Cardiovascular Disease Initiative, Broad Institute of Harvard University and the Massachusetts Institute of Technology, Cambridge, Massachusetts, USA., Picard MH; Division of Cardiology, Department of Medicine, Massachusetts General Hospital, Boston, Massachusetts, USA., Batra P; Cardiovascular Disease Initiative, Broad Institute of Harvard University and the Massachusetts Institute of Technology, Cambridge, Massachusetts, USA; Data Sciences Platform, Broad Institute of Harvard University and the Massachusetts Institute of Technology, Cambridge, Massachusetts, USA., Lubitz SA; Division of Cardiology, Department of Medicine, Massachusetts General Hospital, Boston, Massachusetts, USA; Cardiovascular Research Center, Massachusetts General Hospital, Boston, Massachusetts, USA; Cardiovascular Disease Initiative, Broad Institute of Harvard University and the Massachusetts Institute of Technology, Cambridge, Massachusetts, USA., Ho JE; Cardiovascular Disease Initiative, Broad Institute of Harvard University and the Massachusetts Institute of Technology, Cambridge, Massachusetts, USA; CardioVascular Institute and Division of Cardiology, Department of Medicine, Beth Israel Deaconess Medical Center, Boston, Massachusetts, USA. Electronic address: jho@bidmc.harvard.edu.

Publikováno v: Journal of the American College of Cardiology [J Am Coll Cardiol] 2023 Nov 14; Vol. 82 (20), pp. 1936-1948.

Assessment of an Artificial Intelligence Tool for Estimating Left Ventricular Ejection Fraction in Echocardiograms from Apical and Parasternal Long-Axis Views.

Autor: Vega, Roberto¹ (AUTHOR) rromero@exo.inc, Kwok, Cherise² (AUTHOR) ctkwok@ualberta.ca, Rakkunedeth Hareendranathan, Abhilash² (AUTHOR) hareendr@ualberta.ca, Nagdev, Arun³ (AUTHOR) arun@exo.inc, Jaremko, Jacob L.² (AUTHOR) jjaremko@ualberta.ca

Publikováno v: Diagnostics (2075-4418). Aug2024, Vol. 14 Issue 16, p1719. 10p.

Edge Federated Optimization for Heterogeneous Data.

Autor: Lin, Hsin-Tung¹ (AUTHOR) g110064764@mail.nchu.edu.tw, Wen, Chih-Yu¹ (AUTHOR) cwen@dragon.nchu.edu.tw

Publikováno v: Future Internet. Apr2024, Vol. 16 Issue 4, p142. 23p.

Federated learning-based intrusion detection system for Internet of Things.

Autor: Hamdi, Najet¹ (AUTHOR) elhamdi.najet@gmail.com

Publikováno v: International Journal of Information Security. Dec2023, Vol. 22 Issue 6, p1937-1948. 12p.

Federated transfer learning for auxiliary classifier generative adversarial networks: framework and industrial application.

Autor: Guo, Wei¹ (AUTHOR), Wang, Yijin¹ (AUTHOR), Chen, Xin¹ (AUTHOR), Jiang, Pingyu¹ (AUTHOR) pjiang@mail.xjtu.edu.cn

Publikováno v: Journal of Intelligent Manufacturing. Apr2024, Vol. 35 Issue 4, p1439-1454. 16p.