ESC Professional Premium Access

Electrocardiographic artificial intelligence model for timely detection of preeclampsia

Congress Presentation

About the speaker

Doctor Oguz Akbilgic

Wake Forest University School of Medicine, Winston-Salem (United States of America)
1 presentation
0 follower

7 more presentations in this session

Combining hypothesis-driven ECG indices with machine learning improves ventricular arrhythmic risk prediction in a low-risk population

Speaker: Doctor J. Ramirez (Zaragoza, ES)


Performance of survival neural networks in predicting 10-year cardiovascular disease risk in UK and Chinese populations

Speaker: Ms X. LIU (Beijing, CN)


The exploratory clinical trial of AI-enabled Holter electrocardiogram for diagnosis of paroxysmal atrial fibrillation from sinus rhythm waveforms

Speaker: Professor Y. Tamura (Tokyo, JP)


Artificial intelligence-assisted examination of cardiac health status through electrocardiogram

Speaker: Mr D. Sung (Taipei, TW)


ECG-based deep learning for detecting epicardial coronary occlusion in acute myocardial infarction

Speaker: Doctor R. Herman (Aalst, BE)


Access the full session

Allow artificial intelligence to assist with diagnostic heavy lifting

Speakers: Doctor O. Akbilgic, Doctor J. Ramirez, Ms X. LIU, Professor Y. Tamura, Mr D. Sung...

About the event


ESC Congress 2023

25 August - 28 August 2023

Sessions Presentations

ESC 365 is supported by