INTELLIGENCE. INNOVATION. IMAGING.

THE PERFECT VISION OF AI

RECORDING AVAILABLE SOON!

AN ESR PREMIUM EVENT
BROUGHT TO YOU BY ESOR

Programme

Friday, April 5, 2019

12:30 – 14:00 Registration
14:00 – 14:15 Welcome and introduction
E. Neri, Pisa/IT
V. Vilgrain, Clichy/FR
14:15 – 16:00 Introduction to AI
Chair: V. Vilgrain, Clichy/FR
14:15 – 14:35

Artificial intelligence. The view of the computer scientist
J.M. Bishop, London/UK

Learning objectives:

  • To understand the basic concepts of AI
  • To learn the history of AI
  • To have an overview of AI in medicine
14:35 – 15:15

Machine and deep learning in radiology
M. De Bruijne, Rotterdam/NL

Learning objectives:

  • To know the technical principles of AI for radiology
  • To understand the difference between machine and deep learning
15:15 – 15:35

Imaging biomarkers and radiomics: source of big data for AI
L. Marti-Bonmati, Valencia/ES

Learning objectives:

  • To understand what imaging biomarkers are and the concept of radiomics
  • To learn how AI can be applied to the extraction and analysis of imaging biomarkers and radiomics
  • To learn how AI can improve the imaging biomarkers and radiomics development and validation
15:35 – 15:55

From raw data to beautiful images
D. Rueckert, London/UK

Learning objectives:

  • To learn how AI can be applied to image acquisition
  • To learn how AI can be applied to image reconstruction
  • To understand the value of AI in the optimisation of image acquisition protocols
15:55 – 16:00 Questions and Answers
16:00 – 16:30 Coffee break
16:30 – 18:00 Clinical applications
Chair: M. Fuchsjäger, Graz/AT
16:30 – 16:45

Neuro
P. Parizel, Antwerp/BE

Learning objectives:

  • To review the existing applications of AI in neuroimaging
  • To provide an overview of the future potential clinical applications of AI in neuroimaging
  • To foresee if and how AI will impact on the role of the neuroradiologist vs clinicians and patients
16:45 – 17:00

Chest
H.-U. Kauczor, Heidelberg/DE

Learning objectives:

  • To review the existing applications of AI in chest imaging
  • To provide an overview of the future potential clinical applications of AI in chest imaging
  • To foresee if and how AI will impact on the role of the radiologist expert in chest imaging vs clinicians and patients
17:00 – 17:15

Cardiovascular
M. Francone, Rome/IT

Learning objectives:

  • To review the existing applications of AI in cardiovascular imaging
  • To provide an overview of the future potential clinical applications of AI in cardiovascular imaging
  • To foresee if and how AI will impact on the role of the radiologist expert in cardiovascular imaging vs clinicians and patients
17:15 – 17:30

Abdomen and GI tract
D. Regge, Candiolo/IT

Learning objectives:

  • To review the existing applications of AI in abdominal and GI tract imaging
  • To provide an overview of the future potential clinical applications of AI in abdominal and GI tract imaging
  • To foresee if and how AI will impact on the role of the radiologist expert in abdominal and GI tract imaging vs clinicians and patients
17:30 – 17:45

Breast
A. Gubern-Mérida, Nijmegen/NL

Learning objectives:

  • To review the existing applications of AI in breast imaging
  • To provide an overview of the future potential clinical applications of AI in breast imaging
  • To foresee if and how AI will impact on the role of the radiologist expert in breast imaging vs clinicians and patients
17:45 – 18:00 Questions and Answers
18:00 – 18:20

Ethics and responsibilities in AI
A. Brady, Cork/IE

Learning objectives:

  • To learn the ethical controversies that AI is introducing in medical imaging
  • To provide an overview of the professional and legal implications
  • To explore/elaborate the need for a code of conduct
18:20 – 18:35

ESR Paper on Artificial Intelligence in Radiology
E. Neri, Pisa/IT

Learning objectives:

  • To describe the construction of the ESR Paper on Artificial Intelligence in Radiology
  • To provide an overview of the paper
  • To describe the strategic view of ESR on Artificial Intelligence in Radiology

Saturday, April 6, 2019

08:30 – 10:00 Impact of AI in radiology
Chair: B. Hamm, Berlin/DE
08:30 – 08:50

Integration of AI in the imaging workflow
E. Ranschaert, Tilburg/NL

Learning objectives:

  • To describe the workflow steps in which AI can be inserted: appropriateness, patient’s registration, dose monitoring, reporting, etc.
  • To foresee how AI will change the PACS workflow
  • To foresee if AI will help in big data mining/interpretation and in cost analysis
08:50 – 09:10

Impact of AI in teaching radiology
J. Sosna, Jerusalem/IL

Learning objectives:

  • To describe the potential role of AI in radiology training
  • To foresee how AI will change the radiological training
  • To provide a roadmap to integration of AI in the radiology training curriculum
09:10 – 09:30

Impact of AI in continuous education in radiology
E. Kotter, Freiburg/DE

Learning objectives:

  • To describe the potential role of AI in post-graduate education
  • To foresee how AI will change the approach to post-graduate CME of radiologists
  • To provide a roadmap to integration of AI in the post-graduate CME of radiologists
09:30 – 09:50

Impact of AI in the management of the Radiology Department: a Leadership challenge
C.D. Becker, Geneva/CH

Learning objectives:

  • To describe the potential role of AI in the management of the Radiology Department
  • To foresee how AI will change the role of the Chairman and relationship with other professionals
  • To provide a roadmap to integration of AI in management of the Radiology Department
09:50 – 10:00 Questions and Answers
10:00 – 10:30 Coffee break
10:30 – 11:30

Keynote lecture: AI and Radiology
T. Walsh, Sydney/AU

Abstract:
Geoffrey Hinton, the father of Deep Learning has made a dire prediction about the future of radiology as a profession. In this talk, Prof. Walsh will take a realistic look at what AI can and can’t do. He will discuss what parts of radiology AI will help automate as well as how to take advantage of the opportunity it presents to re-imagine the profession. He will end with a discussion of some of the ethical challenges this presents.

11:30 – 12:30

Round Table – Panel discussion: the future of radiology and radiologists
Chairs: V. Vilgrain, Clichy/FR; K. Riklund, Umeå/SE, N. Lassau, Villejuif/FR

Panelists:

  • C.D. Becker, Geneva/CH
  • B. Brkljačić, Zagreb/HR
  • L. Derchi, Genoa/IT
  • M. Fuchsjäger, Graz/AT
  • E. Neri, Pisa/IT
  • P. Parizel, Antwerp/BE
  • T. Walsh, Sydney/AU
12:30 – 13:30 Lunch
13:30 – 14:30 Professional issues
Chair: P. Mildenberger, Mainz/DE
13:30 – 13:50

Who is responsible for the diagnosis? Man or machine?
M. Zins, Paris/FR

Learning objectives:

  • To discuss how AI can be used in the diagnostic workflow: primary diagnosis, second diagnosis, concurrent diagnosis
  • To foresee how AI will impact in medico-legal responsibility of radiologists and radiographers
  • To simulate use cases of AI impact in the diagnosis
13:50 – 14:10

Who controls patient data in the era of AI? Man or machine?
P. van Ooijen, Groeningen/NL

Learning objectives:

  • To discuss the safety of data (GDPR-related)
  • To foresee the implication of AI in the data management: helpful or dangerous?
14:10 – 14:30

Making the shift to value based healthcare
J.J. Visser, Rotterdam/NL

Learning objectives:

  • To discuss if and how AI can be helpful for a value-based radiology
  • To foresee if AI can improve the quality of the profession of radiologists
  • To foresee if AI can improve the quality of the radiological diagnosis and quality of life of patients
14:30 – 17:15

AI companies’ space
Chairs: E. Neri, Pisa/IT; N. Papanikolaou, Lisbon/PT; P. van Ooijen, Groeningen/NL

Session open to SME companies in the field of AI to present themselves and to showcase their product and the advantages of AI in different scenarios of their choice.

17:15 End of event