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.

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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.

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

Topic: Intervention

Speaker: Doctor J. Dargan (London, GB)

Event: ESC Congress 2023

Congress Presentation

About the speaker

Doctor James Dargan

St George's University Hospital NHS Foundation Trust, London (United Kingdom of Great Britain & Northern Ireland)

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5 more presentations in this session

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

Speaker: Doctor S. Kany (Cambridge, US)

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)

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

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About the event

ESC Congress 2023

25 August - 28 August 2023

Sessions Presentations

Congress

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.

About the speaker

Doctor James Dargan

5 more presentations in this session

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

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

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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