Professor Girish DwivediMD, MRCP (UK), PhD (UK), FASE, FESC, FRACPWesfarmers Chair in Cardiology & Consultant Cardiology (Fiona Stanley hospital)

Faculty Disclosure

•Speaker Bureau fee for Amgen, Pfizer and Astra Zeneca

•Advisory capacity for Artyra Pty Ltd and also equity interest.

•Many of the AI slides are provided by Artrya Pty Ltd and research is done in collaboration with Artrya

Tools

Tools

❑ Review the CT techniques and literature- why I think it

has enormous potential.

❑ Why AI suited for Cardiac CT interpretation

❑ Identify future perspectives

Objectives

Stable Chest Pain

• New onset stable chest pain is a common clinical problem

• USA: 4 million stress tests / year

• Australia: 233,000 Stress tests in 2014 (6m)

• EST, Myocardial Perfusion and Stress Echo

• Only 2-3% led to revascularization

• Patients with “non-cardiac” chest pain: make up 1/3 of

patients dying from CVD at 5 years

Need for improvement in:

Diagnostic Accuracy

Risk Stratification

Maurovich-Horvat, P. et al.(2014) Nat. Rev. Cardiol.

Mourovich-Horvat et al., JACC Imaging 2012

❑ Nonobstructive disease

❑ “Vulnerable” plaques.

- The majority of lesions that rupture and lead to ACS werenon-obstructive (<50%) on antecedent coronaryangiography.- 50% men and 64% women ACS or death is the first

manifestation

❑ CT Myocardial Perfusion Imaging

❑ CT FFR

Prognostic Value of Cardiac CT: High Grade Stenosis

Min JK et al. J Am Coll Cardiol 2011

0

2

4

6

8

10

12

14

16

1VD 2VD 3VD

HR 1.93 HR 2.74 HR 6.09

n=2,583, all with <50% stenosis

Followed for 3.1 years for ACM

>6-fold higher mortality for patients with 3V “mild” CADLin FY et al. J Am Coll Cardiol 2008

Prognostic Value of Cardiac CT: ’Mild’ Stenosis

Prognostic Value of Stenosis

Extensive non-obstructive CAD (SIS >4) had a similar rate of cardiovascular death or MI compared with less extensive obstructive

disease (SIS <4).

Bittencourt et al. Circ Imaging 2014

High Quantitative High Risk Plaque Features

Motoyama et al, JACC 2009

Qualitative High Risk Plaque Features: Independent Predictor of ACS

ICONIC: 25,251 patients undergoing CT, 3.4 years

Propensity Score

Age and Gender

Site

CAD Risk Factors

Angiographic CAD extent &

severity^

Patient who experienced ACS after CCTA

Case (n=234)

Patient who did not experience ACS after CCTA

Control (n=234)

When angiographic CAD extent and severity is the same, do atherosclerotic plaque characteristics matter?

Chang et al, JACC 2018

ICONIC Results: Maximal % stenosis at time of CT

<50% stenosis

65.4%21.8%

12.8%

Patient

(n=234)

50-70% stenosis >70% stenosis

75.2%20.1%

4.7%

Culprit Lesion

(n=129)

Chang et al, JACC 2018

Quantitative Plaque Analysis

Voros et al, JACC imaging 2011; Liu…dwivedi et al, JTI 2016, Gahungu et al, IJCVI 2020

Quantitative Plaque Assessment: Plaque Volume, Plaque Burden, Plaque Composition

Composition: Non-Calcified vs Calcified NCP: Necrotic Core, Fibrofatty, Fibrous

STRESSRESTSTRESSREST

MPR 7mm, MiniP 7mmWL/WW 150/200 with free user manipulation

Inacio, dwivedi…et al, RSNA 2016

Physiological Principles of CT-FFR

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% %- %. %/ %0 ( % BCH: G

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Maurovich-Horvat, P. et al.(2014) Nat. Rev. Cardiol.

• Stenosis severity grade

• Plaque burden

• Plaque volume

• Plaque composition

• Diffuseness

• Remodeling

• Distance from ostium

• Distance from bifurcation

• Ischaemia (CT-FFR)

• Myocardium at risk

• Myocardial mass

• Perfusion

• Vascular Inflammation

• Epicardial fat

Cardiac CT Metrics

Object Recognition(Automated Multi-Metric CT Analysis)

Real-time Decision Making(Changes in Care)

Machine Learning

Full Disease and Plaque Analysis across entire CT scan

Idhayhid….Dwivedi et al. SCCT, CSANZ 2020

Feasibility and Performance of Fully Automated Coronary Artery Calcium Scoring

Using Deep Machine Learning Abdul Rahman Ihdayhid1, Casey Lickfold2, Julien Flack2, Brendan Adler3, Lawrence

Dembo3,6, Jack Joyner2, Benjamin J Chow4, Girish Dwivedi4,5,6

1 Monash Cardiovascular Research Centre, Monash University and MonashHeart, Monash Health, Australia2 Artyra Pty Ltd., Perth, Australia

3 Envision Medical Imaging, Perth, Australia4 Division of Cardiology, University of Ottawa Heart Institute, University of Ottawa, Ottawa, Canada.

5 Harry Perkins Institute of Medical Research and University of Western Australia, Perth, Australia6 Fiona Stanley Hospital, Murdoch, WA, Australia

Idhayhid….Dwivedi et al. SCCT, CSANZ 2020

Method A

N=1055

Non-Contrast CTCA Siemens SOMATOM, Prospective ECG Gated, 120 kV

Manual CACS: Per-Patient + Per-VesselExperienced CTCA Readers

Development of Radiomics + ML CAC Algorithm: Artrya, University of Western Australia, Monash University

Method B

N=4807Method C

N=4807

ICC

Diagnostic Accuracy – Precision – Analysis Time

Suspected CADAge > 18 years

Bland-AltmannKappaweighted

Inclusion Criteria

PCI, Grafts, PPM, Metal Implants

Excluded

Methods

*Testing data separate from training + validation

Cases divided: Training | Validation | Testing

1. Input Non-Contrast CTCA 2. Identify Pixels ≥130 HU 3. 3D Map of Connected Voxels ≥130 HU

CAC CAC

Principles of Automated CAC

4. Extraction of Radiomic Features

Train Standard Neural Network

Hybrid Neural Network for CAC Prediction

Image Patch

5. Image Patch Around Component

Train Convolution Neural Network

LMCA

Aortic

Ground Truth Prediction

0.45 1.59 0.68 1.82

Non-V

esse

l

LMCA

LAD

LCX

RCA

0

20

40

60

80

100

Rela

tive F

req

uen

cy (

%)

‘Connected’ Voxels Labelled as Same Structure Challenging to differentiate LMCA from Aortic Calcification >99% of voxels ≥ 130 HU are not coronary

• Inefficiency in analysis time

• Risk of misclassification

Distribution of ≥ 130 HU

Automatic CAC: The Challenge

Automated CAC: Method A Input CT Method A ❌

Method B ✅

Aortic Segmentation CNN

Dice Score 94

Automated CAC: Method B Method B: Updated + Custom CNN• Method A: Image patch was analyzed with a standard AlexNET

• Analyses image patches in 2D (i.e axial plane)

• Method B

a) Custom CNN: analyze image patch in multiple dimensions

• Coronal + Sagittal Planes in addition to axial plane

b) Increased training data set from N=606 to N=2135

Custom CNN

Custom CNN AlexNET CNN

CNN trained on larger and more complex image features

Automated CAC: Method C Ground Truth Method B: Incorrect Prediction

Non-cardiaccalcium

IncorrectLCX

Cardiac ROI CNNDice Score 92

Method C: Improves Prediction

CorrectNon-CAC

Method C: Results

CA

C

0.85

0.93

0.98

A B C

0.4

0.5

0.6

0.7

0.8

0.9

1.0

Method

Kap

pa

8.7

3.8

1.2

10.4

5.1

1.9

A B C

0

2

4

6

8

10

12

Mis

cla

ssif

icati

on

(%

)

Shift Up

Shift Down

Large improvement in Kappa with low % of misclassification of risk category

0 1-10 11-100 101-400 >400 Total Diagnostic Accuracy (%) Shift Up (%) Shift Down (%)0 880 5 0 0 0 885 99.44 0.56 0.00

1-10 21 233 11 0 0 265 87.92 4.15 7.92

11-100 4 5 375 6 0 390 96.15 1.54 2.31

101-400 0 0 2 267 2 271 98.52 0.74 0.74

>400 0 0 1 4 142 147 96.60 0.00 3.40

Total 905 243 389 277 144 1958 96.88 1.23 1.89

CACML Reclassification

0 250 500 750-400

-200

0

200

400

Average

CA

C -

CA

CM

L

0 250 500 750-400

-200

0

200

400

Average

Dif

fere

nce

0 250 500 750-400

-200

0

200

400

Average

Dif

fere

nce

Mean Diff.: 2.3 ± 129.2 LOA: -250.9 to 255.6

Mean Diff.: 4.7 ± 129.2 LOA: -173.2 to 182.6

Mean Diff.: 2.4 ± 47.0 LOA: -89.7 to 94.6

Method A Method B Method C

Method C: Precision

Method C associated with narrowest limits of agreement andoverall improved precision

Non-Vessel LMCA LAD LCX RCA

0

20

40

60

80

100

Rela

tive F

req

uen

cy (

%)

Method A Method C

Distribution of ≥ 130 HU

Improved Discrimination of Non-vessel vs Coronary Calcium40% Reduction in Analysis Time

Method C: Analysis Efficiency

3.7

4.0

2.4

0 1 2 3 4 5

A

B

C

Time (min)

Analysis Time

Met

ho

d

Artery Tracking and Labeling

Artery Tracking and Labeling

AI artery analysis: Part 2

36

Tracked Arteries

Extracted walls and disease detected

Patient specific model

High-Risk

Plaque

Shear

Stress

Myocardium

At Risk

AMI

Death

Ischaemia

(FFR)

Cardiac CT

Cardiac CT

Cardiac CT

Cardiac CT

Non-Invasive Risk Assessment- CT and AI

Summary

❑CCTA now allow coronary atheroma to be visualized directly

❑Detailed information adverse plaque characteristics,

perfusion, FFR etc. possible with CCTA.

❑ However, for CCTA to achieve its potential when need AI.

❑AI is the future but considerable work remains to be done to

translate this information risk-prediction tools and improved

clinical care.

Thank You