Use Cases in AI - Reasons to do AI with friends - ESR Connect

ESR Connect Weekly - Season 2

Use Cases in AI - Reasons to do AI with friends

  • 6 Lectures
  • 270 Minutes
  • 11 Speakers
No access granted. Register to watch.

Lectures

1
The one with the six pack

The one with the six pack

58:07S. Islam

2
The one that might be renal cancer

The one that might be renal cancer

53:39W. Bai

3
The one where we find the connection

The one where we find the connection

60:43M. Diogo

4
The one with whole body MRI

The one with whole body MRI

58:02T. Barfoot

5
The one where we classify breast cancer with MRI

The one where we classify breast cancer with MRI

61:19T. Helbich

6
The one where we tackle fibrosis

The one where we tackle fibrosis

63:31G. Langs

Description

Description

We set up six exciting use cases and prove that radiologists can get involved in shaping the AI tools of the future.



Included Lectures

Episode
1: The one where we classify breast cancer with MRI
2: The one where we tackle fibrosis
3: The one with the six pack
4: The one that might be renal cancer
5: The one where we make the connection
6: The one with whole body MRI



Teaser Video



Episode 1: The one where we classify breast cancer with MRI

Learning Objectives

  1. Detection, classification & segmentation of lesions in breast tissue
  2. Data requirements in Machine Learning
  3. Data annotation & validation in Machine Learning

Use Case Description
We propose to write an algorithm that will automatically detect malignant breast lesions. In this webinar we will explain to you, why diversity is crucial in the data set and what needs to be considered when annotating and validating data sets in the lab and clinically. Our machine learning algorithm will be able to detect, classify, and segment lesions in multi-modal and multi-parametric imaging data.



Watch episode 1



Episode 2: The one where we tackle fibrosis

Learning Objectives

  1. To understand the difference between supervised and unsupervised learning
  2. To appreciate the need for large-scale data sets from different sites and scanners
  3. To understand how unsupervised algorithms can be validated

Use Case Description
Humans cannot identify complex patterns in CT data reliably. In the detection of fibrosis, relevant patterns need to be identified and we suspect that we don’t know all of them. We will write an algorithm that detects the relevant type of fibrosis and explain the difference between supervised and unsupervised learning.



Episode 3: The one with the six pack

Learning Objectives:

  1. Considerations for dataset curation
  2. Image segmentation (L3 selection and muscle mass)
  3. Training and testing
  4. Validation

Use Case Description
When assessing sarcopenia, the L3 level has to be detected manually on the whole body CT, the muscle bulk has to be segmented and corrections of these segmentations to remove adipose tissue have to be made manually. We will write an algorithm to automate detection of the L3 slice on CT and extract muscle mass for sarcopenia assessment.



Episode 4: The one that might be renal cancer

Learning Objectives

  1. To Differentiate Renal Cell Carcinoma from Oncocytoma
  2. Considerations for database development
  3. Data annotation, Training, and testing
  4. Validation
  5. Final Output

Use Case Description
We propose to write an algorithm that will automatically detect and segment renal masses on CT. The segmented mass will then be further characterised using a radiomic approach, to differentiate benign from malignant masses.



Episode 5: The one where we find the connection

Learning Objectives

  1. Despite best clinical imaging, prenatal evaluation cannot correctly predict good vs poor neurodevelopmental outcome in individual cases of isolated CCA
  2. AI can and should be used in fetal MRI
  3. Using AI we can study patterns of fetal brain connectivity and development beyond tractography, evaluating development and deviations possibly associated with disease
  4. Applying AI to fetal MRI requires specific troubleshooting

Use Case Description
Corpus callosum agenesis (CCA) is one of the most common brain malformations. Fetal MRI improves identification of parenchymal anomalies in CCA in relation to US. When isolated, CCA has a good prognosis in 70-80% of children. However, using currently available methods, we cannot correctly predict the fetuses at risk of neurodevelopmental delay.

AI is a tool that can be used in fetal MRI. Peculiarities must however be taken into consideration, pertaining to the continuous changes of the developing brain, fetal and maternal movement, to name a few. Using DTI data, models of development of the fetal connectome can be developed to quantify deviations associated with disease and establish a vocabulary we can link to outcome. For this, all available data should be used. For validation of these models, it is essential to have a close relation to clinicians and a good follow up of patients.



Episode 6: The one with whole body MRI

Learning Objectives

  1. To identify malignant lesions on whole body MRI

Use Case Description
We propose to write an algorithm that will automatically detect malignant lesions on whole body MRI. We plan to use the large prospectively collected data from both STREAMLINE studies (STC and STL) together with the consensus reference data on confirmed sites of disease to train an ML algorithm to detect the primary and metastatic lesions.



Fees

  • ESR MEMBERS €29
  • NON-MEMBERS €49

Speakers

Presenter

Wenjia Bai

London, United Kingdom

Presenter

Theo Barfoot

London, United Kingdom

Presenter

Mariana Diogo

Vienna, Austria

Presenter

Ben Glocker

London, United Kingdom

Presenter

Thomas Helbich

Vienna, Austria

Presenter

Georg Langs

Vienna, Austria

Presenter

Helmut Prosch

Vienna, Austria

Presenter

Andrea G. Rockall

London, United Kingdom

Presenter

Sayinthen Vivekanantham

London, United Kingdom

Presenter

Fahdi Kanavati

London, United Kingdom

Presenter

Shah Islam

London, United Kingdom