Assessment of aortic valve function in over 47,000 people using deep learning

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Assessment of aortic valve function in over 47,000 people using deep learning

Session: Is artificial intelligence gaining ground in valvular heart disease?

Topic: Aortic Valve Stenosis

Speaker: Doctor S. Kany (Cambridge, US)

Event: ESC Congress 2023

Congress Presentation

About the speaker

Doctor Shinwan Kany

Broad Institute, Cambridge (United States of America)

1 presentation

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5 more presentations in this session

Artificial intelligence-enabled electrocardiogram in the detection of patients at risk of atrial functional mitral regurgitation

Speaker: Doctor J. Naser (Rochester, US)

Decoding calcific mitral valve disease: a novel deep learning model uncovers the role of calcium burden

Speaker: Doctor S. Arif (Montreal, CA)

An artificial intelligence algorithm for detection of severe aortic stenosis: a clinical cohort study

Speaker: Assistant Professor J. Strom (Boston, US)

Patient-specific computer modelling to predict anatomical risk factors preventing post transcatheter aortic valve implantation coronary re-access in bicuspid aortic valve; a modelling study.

Speaker: Doctor J. Dargan (London, GB)

Impact of hypertension on mean transvalvular gradients in aortic stenosis Lessons learned from in silico modelling

Speaker: Doctor J. Jacques (Cape Town, ZA)

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Is artificial intelligence gaining ground in valvular heart disease?

Speakers: Doctor S. Kany, Doctor J. Naser, Doctor S. Arif, Assistant Professor J. Strom, Doctor J. Dargan...

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About the event

ESC Congress 2023

25 August - 28 August 2023

Sessions Presentations

Congress

Assessment of aortic valve function in over 47,000 people using deep learning

About the speaker

Doctor Shinwan Kany

5 more presentations in this session

Artificial intelligence-enabled electrocardiogram in the detection of patients at risk of atrial functional mitral regurgitation

Decoding calcific mitral valve disease: a novel deep learning model uncovers the role of calcium burden

An artificial intelligence algorithm for detection of severe aortic stenosis: a clinical cohort study

Patient-specific computer modelling to predict anatomical risk factors preventing post transcatheter aortic valve implantation coronary re-access in bicuspid aortic valve; a modelling study.

Impact of hypertension on mean transvalvular gradients in aortic stenosis Lessons learned from in silico modelling

Access the full session

Is artificial intelligence gaining ground in valvular heart disease?

About the event

ESC Congress 2023

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