Research Presentation Sessions: Imaging Informatics / Artificial Intelligence and Machine Learning

Integration of artificial intelligence (AI) in radiological workflow and structured reporting

Lectures

1
Introduction by the moderator - Daniel Pinto dos Santos, Cologne / DE

Introduction by the moderator - Daniel Pinto dos Santos, Cologne / DE

04:00

2
Standardising workflow nomenclature in radiology - Knud Nairz, Bern / CH

Standardising workflow nomenclature in radiology - Knud Nairz, Bern / CH

08:00

3
Standardising the mpMRI prostate reporting for one-stop clinics: a novel dedicated semi-automated workflow - Francesco Giganti, London / UK

Standardising the mpMRI prostate reporting for one-stop clinics: a novel dedicated semi-automated workflow - Francesco Giganti, London / UK

08:00

4
Integrating artificial Intelligence algorithms with DICOM structured reporting into clinical workflows - Khaled Younis, Cleveland / US

Integrating artificial Intelligence algorithms with DICOM structured reporting into clinical workflows - Khaled Younis, Cleveland / US

08:00

5
Pilot development of a natural language processing algorithm for classification of head CT reports - Ethan Wang, Houston / US

Pilot development of a natural language processing algorithm for classification of head CT reports - Ethan Wang, Houston / US

08:00

6
Structured reporting of COVID-19 chest CT with natural language processing through a semi-supervised deep learning approach - Salvatore Fanni, Pisa / IT

Structured reporting of COVID-19 chest CT with natural language processing through a semi-supervised deep learning approach - Salvatore Fanni, Pisa / IT

08:00

7
Why standardisation of preinferencing image processing methods is crucial for deep learning algorithms: compelling evidence based on the variations in outputs for different inferencing workflows - Vasantha Kumar Venugopal, New Delhi / IN

Why standardisation of preinferencing image processing methods is crucial for deep learning algorithms: compelling evidence based on the variations in outputs for different inferencing workflows - Vasantha Kumar Venugopal, New Delhi / IN

08:00

8
Using machine learning based CAD-RADS from radiological reports and non-curated electronic medical records data for adverse cardiac events prediction - Emanuele Muscogiuri, Roma / IT

Using machine learning based CAD-RADS from radiological reports and non-curated electronic medical records data for adverse cardiac events prediction - Emanuele Muscogiuri, Roma / IT

08:00