Research Presentation Sessions: Artificial Intelligence & Machine Learning & Imaging Informatics

RPS 105 - AI-enabled workflow optimisation in cardiac imaging

Lectures

1
Evaluation of zero-click whole-workflow of CMR cardiac function and strain analyses pipeline on cardio-oncology patients

Evaluation of zero-click whole-workflow of CMR cardiac function and strain analyses pipeline on cardio-oncology patients

07:00Gregory Mark Lanza, St. Louis / US

2
A deep learning-based approach to automatically choose a protocol of cardiothoracic CT examinations: understanding the decision process using a rate-distortion framework

A deep learning-based approach to automatically choose a protocol of cardiothoracic CT examinations: understanding the decision process using a rate-distortion framework

07:00Martin Segeroth, Basel / CH

3
Automated segment-level coronary artery calcium scoring on non-contrast CT using deep learning

Automated segment-level coronary artery calcium scoring on non-contrast CT using deep learning

07:00Bernhard Föllmer, Berlin / DE

4
Diagnostic performance of a deep-learning model to detect coronary stenoses on CTA images in emergency patients presenting with acute chest pain

Diagnostic performance of a deep-learning model to detect coronary stenoses on CTA images in emergency patients presenting with acute chest pain

07:00Carl Guillaume Glessgen, Geneva / CH

5
A direct deep-learning approach for prediction of patient survival in patients undergoing transcatheter aortic valve replacement based on CT data without the need for muscle segmentation

A direct deep-learning approach for prediction of patient survival in patients undergoing transcatheter aortic valve replacement based on CT data without the need for muscle segmentation

07:00Maike Theis, Cologne / DE

6
Radiomics based on steady-state free precession (SSFP) cine sequences for predicting major adverse cardiac events in patients with dilated cardiomyopathy

Radiomics based on steady-state free precession (SSFP) cine sequences for predicting major adverse cardiac events in patients with dilated cardiomyopathy

11:00Xue Li, Chengdu, Sichuan Province / CN

7
Transfer learning from cine to late gadolinium enhancement MRI for myocardial segmentation in patients with acute myocardial infarction

Transfer learning from cine to late gadolinium enhancement MRI for myocardial segmentation in patients with acute myocardial infarction

07:00Saud Ahmad Khan, Paris / FR

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