Digital twin: the journey

(1)

Institut Mines-Télécomavril@emse.fr

Digital twin: the journey towards better

healthcare -

Applications in

cardiovascular medicine

Prof Stéphane AVRIL

(2)

Institut Mines-Télécomavril@emse.fravril@emse.fr

OUTLINE

 PART I: Can computer models predict human health?

 PART II: The need of combining data driven and mechanistic models in cardiovascular mechanobiology

 PART III: From computer models to digital twins enabling precision medicine

Stéphane Avril - 2020 Dec 9 - MINDS

2

(3)

OUTLINE

 PART I: Can computer models predict human health

 PART II: The need of combining data driven and mechanistic models in cardiovascular mechanobiology

 PART III: From computer models to digital twins enabling precision medicine

3

(4)

Numerical simulation was commonplace in automotive and aeronautics industry

4 In healthcare

☓ ☓ ☓ ☓ ☓ ☓ ☓ ☓

☓ ☓ ☓ ☓ ☓ ☓ ☓ ✓

Standard test for safety and efficacy of new products is

by trial and error In any other

industrial sector

Testing is now done mostly

with computer simulation

(5)

Since 1989: a 30+ years journey

2005:

VPH

2007: STEP

2010: VPH Institute 1993: Physiome

2016: Avicenna In these 30 years the idea of an In

Silico Medicine with Digital Twins for each patient, and In Silico Trials

to test new products, moved from science fiction to an industrial

reality

Stéphane Avril - 2020 Dec 9 - MINDS

5

(6)

Computational models can predict health!!

They even enable decisions everyday in healthcare thanks to ROM and AI

2014: FDA allows marketing of HeartFlow vFFR-CT tool for optimal treatment of coronary stenosis

Gaus S, et al, JCCT 2013, 7(5):279-88.

2019: FEops HEARTguide in silico tool for planning transcatheter aortic valve implantation is CE-marked

El Faquir N, et al Int J Cardiov Img 2019

Stéphane Avril - 2020 Dec 9 - MINDS 6

2014: Sim&Cure 2017: Predisurge

(7)

My own experience on aortic aneurysms is the result of strong and historical

collaborations with clinicians

7

(8)

Aneurysms and Dissections of the aorta

== Devastating complications!

8

(9)

Planification / sizing of fenestrated stent grafts in EVAR procedures

9

(10)

Simulation of stent-graft deployment

10

(11)

Clinically validated for FEVAR Zenith®

Cook Medical

(12)

OUTLINE

 PART I: Can computer models predict human health

 PART II: The need of combining data driven and mechanistic models in cardiovascular mechanobiology

 PART III: From computer models to digital twins enabling precision medicine

12

(13)

Aneurysms and Dissections of the aorta

Challenge: decision making to avoid aortic dissections!

13

(14)

14 Mechanistic approach

ATAAs are triggered by local proteolytic injury, which induce adaptation in the ascending thoracic aorta

Guzzardi et al, JACC (2014), Condemi et al, IEEE TBME (2019)

14

Stéphane Avril - 2020 Dec 9 - MecaFib

(15)

Proteolytic injury and tissue adaptation

PROTEOLYTIC INJURY

Strain HEALING

Stress Stress

15

(16)

16 𝐅 _tot ^𝑗 = 𝐅 _elc ^𝑗 𝐅 _gr ^𝑗 𝐅 _gr ^𝑗 = 𝐅 _r ^𝑗 𝐅 _g ^𝑗

𝐅 _r ^𝑗 and 𝐅 _g ^𝑗 should be updated if the artery is not in the homeostatic state

Elastic and inelastic decomposition of deformation gradient

Mechanistic approach

Mousavi & Avril, BMMB (2017), Mousavi et al, BMMB (2019) Ghavamian et al, Front Bioeng Biotech (2020)

16

(17)

17 Mechanistic approach

Growth and remodeling of a two–layer patient–specific human ATAAs due to elastin loss

Mousavi et al, BMMB (2019)

.

Localization function around the point of TAWSS max

17

(18)

18 Growth and remodeling of a two–layer patient–

specific human ATAAs due to elastin loss

Normalized Thickness Small growth parameter

Patient-specific predictions

18 Mousavi et al, BMMB (2019)

(19)

Difficulties related to the inter-individual variability of aortic dissections

=> uncertain boundary and initial conditions

19

(20)



Humphrey et al, Science 2014

20 Difficulties related to the uncertainty of

molecular and cellular biology

(21)

Solution: combining statistical models and the mechanistic approach

21

(22)

Combining machine

learning and traditional

modeling

22

(23)

Learn boundary conditions, material properties and initial conditions from image analysis

Stéphane Avril - 2020 Dec 9 - MINDS Preoperative dynamic imaging

4D MRI

CAD Mesh

Numerical Solution

Streamlines, velocity Validation 1

TAWSS Postoperative sample

Boundary Conditions

Bulge inflation test

Validation 2

(24)

24

Stiffness [MPa.mm] Stiffness [MPa.mm]

Stéphane Avril - 2020 Dec 9 - MINDS 24

Learn boundary conditions, material properties

and initial conditions from image analysis

(25)

Institut Mines-Télécomavril@emse.fravril@emse.fr Stéphane Avril - 2020 Dec 9 - MecaFib Sample 1

Sample 2

Sample 3

Sample 4

Sample 5

Sample 6

AI model of rupture criterion…

25 AI model Stress distribution

He et al. BMMB – 2020

(Just accepted!)

(26)

Defining patient subgroups

depending on genetic factors

ሶϱ ^𝑗 (t) = ϱ ^𝑗 (t)𝑘 _σ ^𝑗 𝜎 ^𝑗 𝑡 − χ ∗ 𝜎 _h ^𝑗

χ ∗ 𝜎 _h ^𝑗 + ሶ𝜉 ^𝑗 (𝑡)

χ = 1 χ = 1.1 χ = 1.25

Tangent stiffness

after 10 years

Mousavi et al, ABME (2020, submitted)

26

(27)

OUTLINE

 PART I: Material stiffness is a predictor for aortic rupture

 PART II: The need of combining data driven and mechanistic models in cardiovascular mechanobiology

 PART III: From computer models to digital twins enabling precision medicine

27

(28)

Collaborative initiatives

https://www.youtube.com/watch?v=3-uBFx- LhWI&feature=youtu.be

28

(29)

Collaborative initiatives

29 https://meditate-project.eu/

(30)

Need to monitor the biological counterpart

Monitoring mechanical regulation and epigenetics

Predicting patient-specific pathophysiology and drug effects

DIGITAL

TWIN

30

(31)

Institut Mines-Télécomavril@emse.fravril@emse.fr Stéphane Avril - 2020 Dec 9 - MINDS

Several stiffness values

Aortic SMCs from human primary culture (AoSMC, Lonza), passages 5-7, cultured in a differenciating medium (SmBM, Lonza)

• Fluorescent microscopy + DIC : track the displacement of fluorescent microbeads

• Cell unbinding method (with trypsin) : assess the homeostatic state of single SMCs

Monitoring mechanobiology in vivo

31

(32)

Institut Mines-Télécomavril@emse.fravril@emse.fr Stéphane Avril - 2020 Dec 9 - MINDS

32

(33)

Institut Mines-Télécomavril@emse.fr Stéphane Avril - 2020 Dec 9 - MINDS

33 SUMMARY AND FUTURE WORK

 Computational models combining AI and mechanics are now used commonly in healthcare for developing medical devices and drugs

 Major challenges still need to be overcome to go beyond the virtual patient and establish digital twins of oneself

 Simple mechanistic models will always be useful!!

(34)

Institut Mines-Télécomavril@emse.fr Stéphane Avril - 2020 Dec 9 - MINDS

Wear a mask!

34

(35)

Digital twin: the journey