Artifical intelligence in cardiovascular medicine

Artificial
intelligence in
Cardiovascular
medicine
Dr Shilanjan Roy
Consultant Cardiologist, Charnock Hospital
Asst Prof Cardiology, K.P.C Medical College Kolkata

A.I is the future…
• As a branch of computer science, artificial intelligence (AI) is a new
technical science, simulating and extending human intelligence to
handle complex issues.
• AI mimics the human brain to process data, which could identify,
process, integrate, and analyze massive amounts of healthcare data
(medical records, ultrasounds, medications, and experimental
results)
• Artificial intelligence and machine learning are poised to influence
nearly every aspect of the human condition, and cardiology is not
an exception to this trend.

WAZE
Prediction of Traffic
Patterns
UBER
Virtual Transportation App
Prediction of Trip’s
Performance
TESLA - Auto-Pilot
Driverless Automotives
Surrounding Imaging-Object
Analytics
AI and Deep Machine Learning are Already Impacting Life

Defining the AI nomenclatures…
• AI depends on machine learning, which could capture subtle
connections from a series of data rather than manually encoding.
• Accordingly, these subtle findings might revolutionize the
progression of human diseases in prediction, diagnosis, prognosis
and recovery .
• The subdisciplines of AI include cognitive computing, deep
learning, and machine learning (ML)

AI enhances the effectiveness of auxiliary
tools
• AI-learned pattern that can effectively calculate EF.
• AI created the possibility of monitoring regional wall motion
abnormality by screening echocardiograms.
• Matching data set of patients with and without a myocardial
infarction and trained a deep convolutional neural network (DCNN)
to predict the presence of wall motion abnormalities, achieving an
AUC of 0.99 similar to cardiologist and sonographer readers

A.I Enhancing human capabilities..
• AI-based algorithm enhance CCTA performance by allowing for
accurate and rapid assessment of stenosis, atherosclerosis, and
vessel morphology compared with the consensus of expert readers
at level 3.
• Knott et al. used AI algorithms to quantify myocardial blood flow
(MBP) and myocardial perfusion reserve (MPR) by CMR and
evaluate the algorithms in a cohort of 1049 patients with high
degree of accuracy.

Congenital heart disease
In clinical practice, due to a lack of specialized sonographers or
missing critical image frames to help the diagnosis of CHD, the
detection of CHD during pregnancy is often very low.
Trained AI models can detect abnormal image frames that are
difcult for the clinician to discern, improving the diagnosis of CHD

A.I in detecting/screening CHD
• Arnaout et al. trained a neural network to distinguish normal hearts
and CHD using nearly 100,000 images from echocardiographic and
screening ultrasound from 18 to 24 weeks.
• In the internal test set, the model distinguished normal from
abnormal hearts with an AUC of 0.99 and achieved a negative
predictive value of 100%.
• Importantly, the model performed robustly on outside-hospital and
lower-quality images, suggesting that DL-based screening ultrasound
improves the fetal detection of CHD

AI‑aided CVD stratification and typing
• Prediction of CRT Responders:
• Cikes et al.trained an unsupervised ML algorithm to categorize
subjects by similarities in clinical parameters, left ventricular
volume, and deformation traces at baseline into four exclusive
groups.
• Four phenogroups were identifed and two phenogroups were
associated with a substantially better treatment effect of CRT.

CVD risk stratification
• AI-based clustering approach was able to distinguish prognostic response
from β-blockers both in sinus rhythm patients as well as patients with
concomitant AF.
• Proietti et al.performed a hierarchical cluster analysis derived from EORP-
AF.
• Over a mean followup of 22.5 months, Cluster 3 had the highest rate of
cardiovascular events, all-cause death, and the composite outcome
(combining the previous two) compared to Cluster 1 and Cluster 2,
suggesting that cluster analysis might be a choice for providing information
of AF patients’ clinical phenotypes and prognostic events

AI And Deep Machine Learning in Cardiology

Automation Via Deep Learning Image Analysis
2
3
Deep learning on over 8000 annotated CTs enables accurate, precise
performance on automated lumen analysis when evaluated vs. OCT.

Computer Vision
• Objects and feature recognition in digital images, including digital video
frames.
• Applications: Acquisition/interpretation of cardiac images, including
computer-aided diagnosis and image-guided procedures/surgery

Computer Vision for Coronary Angiography

The Digitized Cardiovascular Physician Visit

Translation of artificial intelligence
to future clinical practice
• Ethical dilemmas concerning its real-life implementation are still
unaddressed.
• AI systems can be flawed and their generalizability to new
populations and settings, may produce bad outcomes and lead to
poor decision-making.
• Education of scientists, physicians but also of the public regarding
AI and the logic behind its applications is vital. This can lead to
better understanding and improved engagement in
commercialisation of AI applications

Embracing A.I is the way forward for CV
physicians
• Important aspect is the achievement of robust regulation and
quality control of AI systems.
• AI will be a part of every cardiologist’s daily routine to provide the
opportunity for effective phenotyping of patients and design of
predictive models for different diseases.
• Future cardiologists will be able to tell an asymptomatic patient,
whether they will develop a lethal arrythmia or an MI and what
needs to be done to avoid this.
• Cardiologists should educate themselves in the development of AI
and take part in AI innovations and utilise them in their practice.

Artifical intelligence in cardiovascular medicine

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