OCRE Final Event: Driving Innovation Through the Adoption of Commercial Cloud and EO Services

1. Virtual Aorta: Artificial
Intelligence
for Diagnostic Predictors
of Aortic Disorders
Clinical and Research Computational Tools
Andy Donald, Niamh Hynes

2. Our Vision
Industry Partnerships
Imaging Analytic Tools, Intelligent
Predictors
To Reduce Aortic Death

3. Diagnostic Errors with Lethal Outcomes
167,000
People lose their
life to Aortic
Disease
every year
50%
Patients with Aortic
Dissection are
misdiagnosed
33%
Actively treated for
the wrong condition
11%
Maternal deaths
from cardiovascular
causes are due to
Aortic Dissection
Aortic Dissection is a time-critical medical emergency with
an 80% survival rate when diagnosed and treated on
time

4. Imaging Acquisition to Analysis Mismatch
• A Radiologist working 8hours/day, has 2-3 seconds to review and report images
• 1 in 25 of all radiological diagnosis are errors
• Increasing Work pace increases error rate up to 30%
Radiology Quality Initiative 2012, Berlin 2007
Sokolovskaya et al. 2015

5. Aortic Disease: Life-long Effects
Randall R. DeMartino. Circulation: Cardiovascular Quality and Outcomes. Population-Based Assessment of the Incidence of Aortic Dissection, Intramural Hematoma, and Penetrating
Ulcer, and Its Associated Mortality From 1995 to 2015, Volume: 11, Issue: 8, DOI: (10.1161/CIRCOUTCOMES.118.004689)
• More than double the mortality rate of age matched
controls at 5-, 10- and 20-years post AD
• Two-to threefold increased risk of non-aortic CV
death, any first-time nonfatal CV event, and first-time
Heart Failure
• 80% that survive the initial acute aortic event
continue to have a substantial risk of aortic death,
any aortic event, aortic intervention, and first-time
diagnosis of aortic aneurysm
• Cardiovascular (CV) deaths have fallen over the last
two decades, but the incidence of AD has not fallen.

6. Aortic Disease is Expensive
“The median and total yearly costs to treat thoracic
aortic dissections, as well as the total yearly
costs to treat thoracic aortic aneurysms, have
increased beyond the rate of inflation”
“Factors attributed to thoracic aortic dissection costs
are likely to continue in an upward trajectory….
• Increased incidence of disease
• Operative success in historically nonoperative
patients
• Evidence‐directed endografting for type B
dissection patients historically managed
medically”
Life long Surveillance with cross-sectional imaging

7. Surveillance of the Wrong Parameter
CT Data
Dynamic MRI Data
Static 2D Aortic Diameter 5.5cm vs. Dynamic Wall Stress, Strain, Pressure and Velocity

8. Chronic Inflammatory Disease
The Team

9. Multidisciplinary and Multi-institutional Team
Prof Osama Soliman
Director CORRIB Core Lab
Consultant Cardiologist
Prof Sherif Sultan
Director Western Vascular
Institute
Consultant Vascular Surgeon
Niamh Hynes
Principal Investigator
Vascular Surgeon
Scientist
CORE TEAM
COLLABORATORS
Prof Mark Field
Lead Cardiac Aortic Surgeon
Liverpool Heart and Chest
Hospital
Prof Francesco Torella
Lead Vascular Aortic Surgeon
Liverpool Heart and Chest
Hospital
Dr Ted Vaughan
BioEngineering,
University of IGalway
Prof Santi Trimarchi
IRCCS Policlinico San Donato,
Cardiovascular Center, Milan,
Italy.
Andy Donald
Computer Science/Bioinformatics
University of Galway
Ihsan Ullah
Assistant Professor
INSIGHT Centre for data Analytics,
University of Galway

10. Clinical Partners
• 1/3 of the land mass of Ireland.
• 1 million people in catchment
area
• 7 Hospitals
• 1,961 beds
• 15.4 % older population
compared to 13.4% nationally
• Italian Based International Registry
• Anonymized clinical data and
images
• Largest UK Aortic Centres
• 1000s Complex Aortic Surgeries
annually

11. Chronic Inflammatory Disease
The Project

12. Aortic Segmentation Morphological Analysis
WP3: Manual Segmentation
Ground Truth
WP1: Data Cleaning
Set up Cloud-based Image
Repository WP2: eCRF Construction
International Online Aortic Dissection Registry
WP4: Deep Learning
WP6: External Validation
WP5: Big Data Analysis

13. Development of in silico test beds and diagnostics
Surrogate
Modelling
Big Data
Synthesis
In Silico
Test beds
Test Bed
Validation
Diagnostics
Medical
Devices

14. The Solution

15. Artificial Intelligence Improves Aortic Surveillance
• AI assistance reduced radiologist’s
reporting time for aneurysm follow-up by
63%
Rueckel J, Reidler P, Fink N, Sperl J, Geyer T, Fabritius MP, Ricke J, Ingrisch M, Sabel BO. Artificial intelligence assistance
improves reporting efficiency of thoracic aortic aneurysm CT follow-up. Eur J Radiol. 2021 Jan;134:109424. doi:
10.1016/j.ejrad.2020.109424. Epub 2020 Nov 21. PMID: 33259990.
Aortic Aneurysm
Aortic Dissection
Aortic Prediction of
Re-Intervention

16. Aortic Surveillance Within the Cloud
• Three main areas of focus for cloud services
• Storage • Compute • Transfer &
Security

17. Cloud Storage Challenges
Main data source consists of CT scans (150 3D
images each approx. 950GB each)
Multiple Data types
Computational Modelling outputs
(Structured dataset)
Data Analytics outputs
AWS Glacier
AWS S3
Data Lake
AWS S3
Data Lake

18. Cloud Compute Challenges
Computational Modelling Vs Surrogate Modelling
• The aim of this project is to model the blood flow in aorta
which has been conventionally using the finite element
computational modelling methods.
• However, these methods can be computationally
exhaustive and time consuming and may not be able to
produce predictions in real time essential for diagnostic
purposes.
• Necessary to introduce a surrogate model to emulate this
computational model for real time diagnosis.
• Surrogate models are built by:
• Sampling the output from the computational modelling
• Building and evaluating outputs
• Active learning
• Re-sampling
Computational
Modelling
Output
Surrogate
Modelling
Sampling
Model
Construction
Active
Learning
Model
Deployment

19. Cloud Security & Data Transfer Architecture
Security is extremely important as data comes from Patient CT scans. Access
to the data requires defined permissions and identities via AWS security
services.
PREVENT
Detection of any security issues via monitoring and logging services in AWS
DETECT
SECURE TRANSFER
Usage of the AWS Transfer Family in order to transfer data to AWS Storage services

20. Deliverables
• Databases: Formatted Clinical and Image Repository
• Constitute data sets for further projects (Research
Repository)
• Form the Basis for Prospective International Aortic
Registry
• AI tools for Clinicians for rapid diagnosis and stent-graft
sizing
• AI tools for Research which form the basis for in silico
testbeds
• Risk Scores Tools to predict patients at risk of aortic
events and optimise patient selection for intervention

21. The End Result

22. Thank You!