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

RPS 2205 - Integration of artificial intelligence (AI) in radiological workflow and structured reporting

  • ECR 2022
  • 7 Lectures
  • 60 Minutes
  • 7 Speakers

Lectures

1
Introduction by the moderator

Introduction by the moderator

04:00Daniel Pinto dos Santos, Cologne / DE

2
Standardising workflow nomenclature in radiology

Standardising workflow nomenclature in radiology

08:00Knud Nairz, Bern / CH

3
Standardising the mpMRI prostate reporting for one-stop clinics: a novel dedicated semi-automated workflow

Standardising the mpMRI prostate reporting for one-stop clinics: a novel dedicated semi-automated workflow

08:00Francesco Giganti, London / UK

4
Integrating artificial Intelligence algorithms with DICOM structured reporting into clinical workflows

Integrating artificial Intelligence algorithms with DICOM structured reporting into clinical workflows

08:00Khaled Younis, Cleveland / US

5
Pilot development of a natural language processing algorithm for classification of head CT reports

Pilot development of a natural language processing algorithm for classification of head CT reports

08:00Ethan Wang, Houston / US

6
Structured reporting of COVID-19 chest CT with natural language processing through a semi-supervised deep learning approach

Structured reporting of COVID-19 chest CT with natural language processing through a semi-supervised deep learning approach

08:00Salvatore Fanni, Pisa / IT

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

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

08:00Vasantha Kumar Venugopal, New Delhi / IN

8
Using machine learning based CAD-RADS from radiological reports and non-curated electronic medical records data for adverse cardiac events prediction

Using machine learning based CAD-RADS from radiological reports and non-curated electronic medical records data for adverse cardiac events prediction

08:00Emanuele Muscogiuri, Roma / IT

Moderators

  • Daniel Pinto Dos Santos

    Frankfurt / Germany

Speakers

  • Knud Nairz

    Bern / Switzerland
  • Francesco Giganti

    London / United Kingdom
  • Khaled Salem Younis

    Cleveland / United States
  • Ethan Bocheng Wang

    Houston / United States
  • Salvatore Claudio Fanni

    Pisa / Italy
  • Vasantha Kumar Venugopal

    New Delhi / India
  • Emanuele Muscogiuri

    Leuven / Belgium