1. Zachi Attia, MSc (EE) Co-Director of AI in Cardiology Mayo Clinic
AI In Cardiology: Finding What's Hidden In Plain Sight and Peering Into The Future
Zachi Attia, MSc (EE)
Co-Director of AI in Cardiology
Mayo Clinic

2. Disclosures
Mr. Attia and Mayo Clinic have licensed potassium sensing technology and QT measurement technology to AliveCor., the LVD detection algorithm to Eko Devices and Arrhythmia detection to Myant . Mr. Attia and Mayo may benefit from commercialization of this technology, but not from its use at Mayo Clinic.

3. Natural History of Disease
I feel great!
Sx + Signs
Metabolic, physiologic derangements
Diagnostic Testing Treatment
Develop over years
It may be too late:
First event may be:
CVA MI
SCD
Waiting for signs and symptoms before diagnosing has been done for 2,000 years. We can do better!
CP Friedman, PA MN

4. Could you have heart disease and not know it?
Asymptomatic Left Ventricular Dysfunction (ALVD) – unaware
Identification requires expensive, not readily available tests.
Once identified, effective therapies reduce hospitalizations and mortality.
ALVD: ~2% of the global population, >7 million Americans
Up to 9% of people over 60 years of age
Expensive and many American live more than 50 miles from a cardiologist
Echocardiogram
CT scan

5. ECG as Sensing Platform
Electrocardiography (ECG) is the recording of the heart electrical activity.
Maybe it can tell us more ?
Electrocardiography (ECG) is the recording of the heart electrical activity, the heart dipole is the sum of the different cells action potential.
Sum of cell action potential!!!
P Q R S T !!!!!!!!!!!!!!!!!!!!!!!11
Figures adapted from Jaakko Malmivuo & Robert Plonsey: Bioelectromagnetism

6. Is the Ejection Fraction Low? Weak heart pump?
ECG – Electrocardiogram. Painless, Inexpensive, Widely Available
A clinician cannot assess EF from an ECG
We hypothesized that a deep convolutional neural network (AI) could be trained to identify who has a low EF from an ECG
Once the network identifies a low EF subject  an echocardiogram can be arranged

7. Artificial Intelligence and Neural Networks
Artificial Neural Networks:
Defining a task and a loss function
Training from examples
Convolutional neural networks were found very effective in vision tasks
Requires a lot of data as the features are developed from the samples
Feature 2
Feature 1
What is difference between AI and ML ? (funding , hireling )
Why did we advanced ? GPUs and amounts of Data
Very Deep Convolutional Networks for Large-Scale Image Recognition - Simonyan 2014

8. Robust Digital Warehouse of Medical Information
625,326 patients with ECG - Echo paired data
461,434 ECG and Echo performed more than two weeks apart
Conv Layer
Pooling EF
Fully connected Layers + dropout
100,000s
163,892 ECG-Echo pairs within 2-week interval, from 100,029 patients suitable for analysis
63,863 ECG-Echo pairs from patients with more than one pair used for follow up analysis
2,200  ECG-Echo pairs used to develop proof of concept
First ECG-Echo pair of 97,829 patient was selected for analysis
Figure 1
35,970 ECG-Echo pairs used to train the network
8,989 ECG-Echo pairs used to validate the network
52,870 ECG-Echo pairs used to test the network
Robust Digital Warehouse of Medical Information

9. AUC of EF network = 0.93 (perfect test = 1.0)
Compares favorably with other medical tests:
Mamography for breast cancer = 0.85
Cervical cytology (Pap smear) = 0.70
PSA (prostate cancer) = 0.92
Figure 2

10. Long Term Outcomes of Patients with Normal EF during screening
“False positive” or delayed true positive
All patients had EF >=50% at baseline

11. Could the network perform better by feeding it age and sex?
No.

12. Age and Sex from ECG “Real” Age vs ECG Estimated Age
ROC -Reported Sex vs ECG Estimated Sex
Can The model really not be affected from Age and Sex ?
Blue Red
For Sex- in par with Visual estimation
Might be Heart Age

13. Progression of ECG Age in a Patient with Multiple ECGs
Aging Factor > 1
Real Age=Ecg Age
Linear Regression of
Estimated Age
Real Age

14. Progression of ECG Age in a Patient with Multiple ECGs
Aging Factor < 1
Real Age=Ecg Age
Linear Regression of
Estimated Age
Real Age

15. Progression of ECG Age in a Patient with Multiple ECGs
Heart Transplant
“ECG Age “
Aging Factor < 0 ?
Real Age=Ecg Age
Linear Regression of
Estimated Age
Real Age

16. Can We Do It with a Single Lead ?
Lead I vs. 12 lead
AUC = 0.9

17. What about Screening for Atrial Fibrillation to Prevent Stroke?
The presence of atrial fibrillation is critical for determining optimal therapy
Paroxysmal AF is often asymptomatic and under detected – important for CS patients
The longer we monitor, the higher the yield

18. Continuous Monitoring vs Mobile Form Factors
Will short episodes, detected with continuous or near continuous monitoring have the same prognostic significance?

19. What about Screening for Atrial Fibrillation to Prevent Stroke?

20. Scanning ECGs in NSR to Determine if AF was Present in the Past!
Conv Layer
Pooling
Hx of AF
Fully connected Layers + dropout
1,000,000
Coded
for HX AF
Attia et al AHA 2018

21. Finding AF – During NSR! Area Under the Curve : 0.864
Sensitivity : 77.3 %
Specificity : 80.0 %
AHA 18 - Attia et al
CP Friedman, PA MN

22. Conclusions
The addition of AI to large digital data sets permits detection of “occult” disease (screening)
It permits “prediction” of future disease (detection well before signs/symptoms)
With Smartphone enable AI we can make these tools massively scalable
Confidential

23. Thank you Questions ?
Confidential