Impact of Artificial Intelligence in Cardiovascular Disease
1
Mir Khan , 2Saleem Ahmed, 3Pardeep Kumar, 2Dost Muhammad Saqib Bhatti
1
National Institute of Cardiovascular Diseases, Karachi, Pakistan
2
Dawood University of Engineering & Technology, Karachi, Pakistan
3
Quaid e Awam University of Engineering Sciences & Technology, Karachi, Pakistan
The field of medicine has made tremendous progress, however, cardiovascular diseases (CVD) is
still major reason of death cause in the present world. Still major studies are required to improve
CVD death ratio. One of the areas which can be exploited in order to reduce CVD is by working
on medical imaging. The Artificial Intelligence (AI) strategies such as machine and deep
learning with advance clinical data can be benefited to make the unavoidable healthcare benefit
through which elderly and constant infection patients can get restorative care at their domestic,
lessening hospitalizations and making strides in quality of life.
The CVD may be a key chance to wellbeing and the critical source of death worldwide. The
event of CVD created 17.6 million deaths in 2016, an increment of 14.5% from 2006 to 2016 [1].
The mortality and disease rates of CVD are growing yearly, specifically in emerging countries
[2]. Reports have shown that up to around 80% of CVD-related deaths occur in developing
countries. Other than that, these deaths occur at a very young age in developed countries [3].
Furthermore, current pandemic COVID-19 has also higher impact on CVD patients.
CVD has put an overwhelming burden on patients and civilization as an entire. Hence, it is
necessary to develop techniques for moving forward with determination to cure the CVD in
coming years for better future. The AI can be one of the sources and can help to find the
solutions for CVD related diseases.
Artificial Intelligence (AI):
AI is one of the field of computer science which is intelligence shown by machine rather than
humans. The AI machines can understand human speech, playing games, autonomous cars and
many others. Similarly, AI can play important role in healthcare. Machine Learning (ML) can be
employed to develop various algorithms by using medical and healthcare data. The basic role of
AI in healthcare is to analyze relationship between patients’ outcome and treatment methods.
Another use of AI in healthcare can be used for diagnoses and to prevent the disease by applying
suitable machine learning and deep learning algorithms. Besides this, AI can be used in
pharmaceutical field which can have further impact on healthcare issues like CVD [4].
The rapid progress of AI methods, mostly in the subdomains of machine learning and deep
learning has rapidly concerned the courtesy of clinicians to generate new unified, consistent, and
effective approaches in order to solve healthcare issues.
AI methods can also support physicians to improve medical conclusions empowering early
discovery of subclinical organ failure, with clinically pertinent.
Machine Learning (ML):
ML helps the system to make better predictions and help making better decision by involving
proper data that engages calculations to induce it and learn from it. In addition, the noteworthy of
machine learning is to find the hidden patterns from information and create an outline based on
that information. Along these lines, the machine can utilize this to provide future patterns [5].
Machine learning could be a prevalent sub discipline of AI, represents different methods for
tackling complicated issues with huge information by distinguishing interaction designs among
factors. As compared to conventional measurement approaches; machine learning is centered on
building robotized clinical choice frameworks (such as read-mission and mortality score
frameworks) that help doctors make more exact forecasts, instead of basic assessed score
frameworks.
Machine learning can be categorized into following three categories as shown in figure-1.
Three categories of Machine Learning:
1. Supervised Learning: It is important in supervised learning to train the algorithm. There
are different algorithm in supervised learning like regression and convolutional neural
networks (CNN). The Supervised learning can utilize in healthcare systems to predict or
analyze various medical parameters based on the medical/patient records.
2. Un Supervised learning: In this method the data set is without labels, therefore that
suggests that the machine must discover the label itself. Unsupervised learning can play
important role in CVD disease treatment and can support solving issues such as CVD
forecast, cardiovascular image analysis, conclusion and treatment [5].
3. Reinforcement Learning: In reinforcement learning, there is no necessity to complete
the offered objectives. In supervised and unsupervised learning, humans pose an
objective and it should be accomplish by applying one of the mentioned method. Another
approach of AI is based on response mechanism, frequently defined as a ‘reward’. The
objective of this category of machine learning is not to reach the offered goal, but to
exploit the reward for the model throughout the learning process. It has been deployed to
enhance the process of patients who are on mechanical ventilation in intensive care units
(ICU).
Fig. 1. Supervised, unsupervised, and reinforcement learning.
The Application of AI in CVD:
AI advances have been useful in cardiovascular medicine counting precision medicine, clinical
expectation, cardiac imaging examination and intelligent robots and cardiovascular medicine.
Precision Medicine:
Artificial intelligence can be essentially utilitarian for far off subsequent meet-ups, moment
sicknesses guiding, ideal alerts of signs and drug prompts. Simultaneously, from the impression
of clinicians, Artificial intelligence can assist assemble with voicing data, connect electronic
clinical records frameworks and lessening the remaining task at workload of clinicians [6].
Within the future, intellectual computers (gadgets are taughtby machine or deep learning
calculations and can unravel issues deprived of human help) will offer assistance to clinicians in
order to make precise choices and forecast patient results. With the assistance of AI, it is most
likely to execute a precise therapeutic that modifies healthcare for each individual. AI may not
replace clinicians but clinicians can utilize AI innovations to innovate in cardiovascular disease
cure and drug development.
Clinical Prediction:
By using Machine learning and huge information analytics, AI can offer assistance to clinicians
to form forecasts that are more precise for patients. Research from Dawes TJW recommends that
AI can foresee conceivable times of passing for heart illness patients [7]. In their research, AI
program recorded the cardiac magnetic resonance imaging (MRI) and blood lab tests of 256
heart illness patients. The program measured the development of 30,000 meeting point that are
checked on the heart structures in each pulse. By merging this information with the patients'
eight years’ health records, AI seem foresee the irregular conditions that will lead to persistent
passing. Moreover, their program was able to foresee the survival rates of patients for another
five year, and another year of survival for patients having chances of 80%. Moreover, Motwani
M and his colleagues set up a prophetic prediction using deep learning, in order to increase five
year life time, for 10,030 suspected coronary heart infection (CHD) patients. This study shows
that the evaluation based on AI has higher accuracy compared to conventional medical judgment
and coronary computed tomographic angiography [8].
Cardiac Imaging analysis:
With the invent of machine learning, cardiac imaging examination has shown incredible
improvement. The machine learning can offer assistance to analyze electrocardiogram (ECG),
coronary angiography and echocardiography. In later decades, the cardiac medication has most
focus on the counting CHD and acute coronary syndrome (ACS). The Machine learning
algorithms can recognize coronary atherosclerotic more precisely than clinicians. In addition, AI
can too be utilized to analyze echocardiographic pictures.
The College of California, San Francisco, made convolutional neural systems through utilizing
the echocardiographies of 267 randomized patients (age range: from 20 to 96 years) between
2000 and 2017 from the college therapeutic center. The 223,000 pictures were isolated into
fifteen groups. Moreover, this grouping calculation has outflanked the human cardiovascular
doctors within the classification competition of cardiac ultrasound images. The deep learning
will make imaging analysis more accurate at ease and early prediction before it becomes severe
[9].
Future Prospect:
AI has made a lot of progress in cardiovascular medicine. The incorporation of AI and
cardiovascular medicine involves qualified services, advance technologies. The AI ventures are
most likely to be conducted by huge innovation enterprises such as Google, Apple and
Microsoft. Which have contributed intensely in AI to progress the efficiency of cardiovascular
medicine. Stanford and Apple propelled a venture entitled “Apple Heart Study” with the
assistance of ML. In addition, the advancement of sensor innovation has encouraged the use of
AI in cardiovascular medication.The latest Apple watch series 4 has a new transducer that
measures ECG. The US Food and Drug Administration (FDA) have approved this new feature.
In early 2018, researchers from Verily (Google Life Sciences-Alphabet Inc.'s inquire about
organization) utilized machine learning to evaluate the hazard of sympathetic enduring from
cardiovascular infection. They effectively performed study to analyze the patient's eye. At that
point they also gathered different sorts of information, counting the patient's blood, age, weight
and smoking status. Subsequently, this permitted the researchers to foresee the patient's chance
of cardiovascular infection. To prepare the calculation, they utilized machine learning to analyze
the therapeutic information of about 30 million patients. As aoutcome, the precision of the
calculation in recognizing patients with cardiovascular illness was as tall as 70%, which is near
to the conventional cardiovascular treatment [10].
Besides, Microsoft as of late reported that they will help out Apollo Medical clinic in India
recorded as a hard copy calculation to support clinicians in anticipating the danger causes for
CVD. On 9thJanuary, 2017, the FDA offered freedom for the utilization of a cardiovascular Xray examination programming called Cardio DL (from Arterys), that utilizes profound learning
for clinical picture investigation and gives mechanized ventricular division to conventional heart
X-ray checks. By utilizing distributed computing, Cardio DL can naturally finish picture
preparing in under ten seconds, and can draw the framework of the ventricular epicardium and
subcardium, to precisely assess the capacity of the ventricle.11 Siemens has constructed a huge
information base of in excess of 250 million connected pictures, reports, careful information and
different materials for preparing its simulated intelligence computation programs. A team of
cardiologists at the University Hospital of Heidelberg conducted a six-year trial. A group of
cardiologists at the University Hospital of Heidelberg led a six-year preliminary. They utilized
information from patients with cardiovascular breakdown to produce 100 carefully mimicked
hearts and utilized artificial intelligence to foresee the forecast of these patients, and afterward
contrasted the anticipated outcomes and the genuine circumstance of the patients. Clinicians can
even utilize 3D production innovation to make models of the heart, to build up a more suitable
cure.
All these information recommend that a significant insurgency in the medical use of artificial
intelligence in cardiovascular medication (closely resembling a Cambrian blast) may happen
shortly, and this application is just the start of the general utilization of computer-based
intelligence
PUAI and Novel Medical mode:
Phenomenon of PUAI:
The problems clinical industry has experienced, the greater part of the current ramifications of
computer-based intelligence in cardiovascular medication can be depicted as 'PUAI'. In clinical
practice, the principle issue looked by specialists consistently ought to be understood first, for
example, the right analysis and powerful treatment for understanding.
The application and abilities of computer based intelligence depend for huge scope and develop
clinical data for AI. At present, it very well may be applied in certain particular conditions, for
example, trauma center and chest pain center (CPC).
With the improvement of artificial intelligence innovation, we actually cannot ensure that the
innovation is solid. A few specialists stress that a few clinicians will depend altogether on
computer based intelligence to manage patients. Whereas, specialists are the pillar, and artificial
intelligence can assist specialists with improving the adequacy of their treatment [12]. Verghese,
et al. called attention to that clinicians can utilize computer based intelligence to more readily
serve patients [13]. Studies have proposed that the blend of clinicians and artificial intelligence
abilities will furnish patients with better symptomatic outcomes than experience alone.1To this
end, in light of the fact that our group has developed another territorial agreeable salvage model
to upgrade the conclusion and therapy framework for CPCs [14].We have planned another
clinical model that may enable youthful clinicians to decrease the pace of misdiagnosis [15].
Novel medical model:
Right now, clinicians give patients direct treatment choices dependent on their personal finding.
Nonetheless, because of the clinician's understanding, stress, or exhaust, different causes may
make an off-base finding, and even lead to disastrous results. In the new model as shown in
figure 2, the clinician can pass the finding plan through artificial intelligence, and if the
guidelines are right, the simulated intelligence will accomplish. In the event that the guidelines
are scrambled, disregard the directions given by the artificial intelligence dependent on AI and
approach the senior clinician for help. It is trusted that new model will decrease the rate of
clinical misbehavior brought about by clinician blunders.
Fig. 2. The clinician can pass the finding plan through artificial intelligence.
Traditional mode
Novel medical mode plus PUAI:
We can join the idea of PUAI into novel clinical model. We can locate a simpler method to
fabricate an artificial intelligence model. Presently, the restricted utilization of computer-based
intelligence for clinical conclusion includes the differential finding of CVD. There are a few
kinds of CVD, each with various analytic strategies; along these lines, complex calculations and
models, for example, profound learning and fortification learning, are needed to expand the
trouble of composing calculations and increment the size of preparing informational indexes and
computer based intelligence models. Be that as it may, by fusing PUAI into another clinical
model, it is just important to plan a little and basic model for the analysis of basic infections, for
example, ACS and aortic dismemberment by utilizing less difficult calculations. There is no
compelling reason to stress over the restrictions of flow artificial intelligence obstruction
between these ailments. For instance, these kinds of thoughts can be applied to build up a notice
framework in the current clinical framework, as shown in Figure 3.
Fig. 3. current clinical framework
The patient arrivesin the emergency clinic after that the patient's data is all the while gone into
the information base when the clinician gathers the history. In view of the clinician's finding, the
simulated intelligence will agreeing the symptomatic models for the sickness (Standard A) from
the information base, contrasted with the patient's genuine condition (Standard B). In the event
that the examination results coordinate, the admonition framework will not ready. In case of
clashing outcomes, the simulated intelligence will create an alarm, making the clinician aware of
cautiously look at his/her finding.
This new medical admonition framework is appropriate for ICUs, as well as the coronary
consideration unit and CPC, which is particularly valuable during night shifts. Since, in these
particular regions, the presentation of night shifts specialists directly affects persistent safety.16
Studies from Maltese, et al. have demonstrated that the dynamic capacity of ICU specialists has
dropped altogether [17]. Presently, this new clinical early admonition framework shows
incredible potential for evading misdiagnosis brought about around evening time move
clinicians' psychological decrease. In addition, the notice framework is anything but difficult to
apply. Our group has planned another provincial helpful salvage model to upgrade the analysis
and treatment framework for CPCs. It gives ideal and powerful PCI to patients with ST-fragment
height myocardial localized necrosis, particularly in creating nations, for example, China [5].
The central issue of this model is to lessen the time from indication beginning to reperfusion and
cardiovascular mortality. Later on, we will likely apply this novel clinical model to existing
frameworks. By utilizing an AI based calculation, artificial intelligence can cautiously inspect
the clinician's determination and curestrategy. The objective is to save more lives. We will keep
on refining this novel clinical model and check its functional application esteem in medical
practice.
At present, CVD stays a significant medical issue influencing the whole world, particularly in
low-and center salary nations. It will keep on being the primary driver of mortality in the
following twenty years.The utilization of simulated intelligence, particularly AI, has
demonstrated incredible potential in overseeing and treating this irksome illness [3,18]. In
addition, we solidly accept that later on, computer based intelligence will be a collaborator to
clinicians, not a foe, since computer based intelligence was initially intended to mirror human
reasoning cycles as opposed to advancement. Hence, the clinician ought to comprehend the
importance of simulated intelligence and be acquainted with its use esteem. The capacity of
computer-based intelligence will increment as clinicians developfurther mindful of the infection.
The improvement of clinical abilities, inside, and out clinical exploration is the reason for the
advancement of artificial intelligence. Clinicians must not disregard to persistently figure out
how to recover their capacity to help patients, and not to depend a lot on machines and simulated
intelligence.
Representative Calculations of AI:
Machine learning and deep learning consist of a multitude of algorithms. Table 1 summarizes
brief descriptions of basic machine learning algorithms used in different tasks. Currently,
ensemble learning and deep learning can be described as the mainstay of algorithms of AI.
Ensemble learning is a machine learning method that combines multiple “weak” learners
(algorithms) such as decision tree and logistic regression (Table 1) to obtain a good prediction.
Boosting, bagging, and stacking are the 3 main methods of ensemble learning [19].
Table 1 summarizes brief descriptions of basic machine learning algorithms used in different
tasks
Algorithm
Description
Use
Logistic regression
An algorithm that estimates
probability of dichotomized
outcome from multiple
covariates using logistic
function.
A flow chart–like algorithm
Classification
Decision tree
Classification/regression
(simple) Neural network
K nearest neighbor
Support vector machine
K means
Hierarchical clustering
Principal component analysis
that divides data into
branches by considering
information gain. The final
branches represent output of
the algorithm (class or value).
An algorithm inspired by
human brain architecture.
Layers consisting of nodes
are connected to one another
with edges weighted as per
training results.
A simple algorithm that
classifies observations by
comparing k examples that
exist in the nearest locations
(=examples with the most
similar features).
Support vector machine
draws a boundary line that
maximizes margins from each
class. New observations are
classified using this line
A clustering method that
makes k clusters in which
each observation belongs to
the cluster that has its mean
in the nearest locations from
the observation.
A type of cluster analysis that
builds a dendrogram with a
hierarchy of clusters. Pairs of
clusters are merged to form
clusters as they move up the
hierarchy (agglomerative
approach).
An algorithm that converts
high dimensional data into
lower dimensional data with
keeping important
information as much as
possible by orthogonal
transformation
Classification/regression
Classification/regression
Classification/regression
Clustering
Clustering
Dimensionality reduction
Overview of Pipeline for Image-Based Machine Learning Diagnosis:
The in general pipeline to construct ML instruments for image-based cardiac determination is
schematically portrayed within the taking after segment, as well as in Figure 4. In brief, it
requires (1) input imaging datasets from which appropriate imaging indicators can be extricated,
(2) precise yield determination names, and (3) a suitable ML procedure that's ordinarily chosen
and optimized depending on the application to foresee the cardiac determination (yield) based on
the imaging indicators (input). Extra non-imaging indicators (e.g., electrocardiogram
information, hereditary information, sex, or age) are regularly coordinates into the ML
demonstrate and regularly move forward demonstrate execution.
In this segment, we'll to begin with talk about the input and yield factors in more detail, some
time recently presenting common utilized ML procedures and their applications.
Fig. 4. Pipeline for building image-based machine learning models.
References
1. Thomas H, Diamond J, Vieco A, Chaudhuri S, Shinnar E, Cromer S, Perel P, Mensah GA, Narula
J, Johnson CO, Roth GA, Moran AE Glob Heart. “Global Atlas of Cardiovascular Disease 20002016: The Path to Prevention and Control.” 13(3):143-163. 2018
4
2. Global, regional, and national age-sex specific mortality for 264 causes of death, 1980-2016: a
systematic analysis for the Global Burden of Disease Study 2016. GBD 2016 Causes of Death
Collaborators. Lancet. 390(10100):1151-1210. 2017
3. Gersh BJ, Sliwa K, Mayosi BM, Yusuf S “Novel therapeutic concepts: the epidemic of
cardiovascular disease in the developing world: global implications”.
Eur Heart J. 31(6):642-8. 2010
4. Gulshan V, Peng L, Coram M, Stumpe MC, Wu D, Narayanaswamy A, Venugopalan S, Widner
K, Madams T, Cuadros J, Kim R, Raman R, Nelson PC, Mega JL, Webster DR “Development
and Validation of a Deep Learning Algorithm for Detection of Diabetic Retinopathy in Retinal
Fundus Photographs.” JAMA. 316(22):2402-2410. 2016
5. Krittanawong C, Zhang H, Wang Z, et al. “Artificial intelligence in precision cardiovascular
medicine”. J Am CollCardiol. ;69:2657–2664. 2017
6. Johnson KW, Torres Soto J, Glicksberg BS, et al. “Artificial intelligence in cardiology”. J Am
CollCardiol. 71:2668–2679. 2018
7. Dawes TJW, de Marvao A, Shi W, et al. “Machine learning of three-dimensional right ventricular
motion enables outcome prediction in pulmonary hypertension: a cardiac MR imaging
study”. Radiology. 283:381–390. 2017
8. Motwani M, Dey D, Berman DS,. “Machine learning for prediction of all-cause mortality in
patients with suspected coronary artery disease: a 5-year multicentre prospective registry
analysis”. Eur Heart J. 38:500–507. 2017
9. Madani A, Arnaout R, Mofrad M,. “Fast and accurate view classification of echocardiograms
using deep learning.” NPJ Digit Med. 2018
10. Poplin R, Varadarajan AV, Blumer K, . “Predicting cardiovascular risk factors from retinal
fundus photographs using deep learning”. Nat Biomed Eng. 2:158–164. 2018.
11. Arterys cardio DL cloud MRI analytics software receives FDA clearance. In: editor book arterys
cardio DL cloud MRI analytics software receives FDA clearance, 2017. Diagnostic and
Interventional Cardiology. 2017
12. Russell S, Bohannon J. “Artificial intelligence. Fears of an AI pioneer.” Science. 349:252–
252. 2017
13. Verghese A, Shah NH, Harrington RA. “What this computer needs is a physician: humanism and
artificial intelligence.” JAMA. 319:19–20. 2017.
14. Chen JH, Asch SM. “Machine learning and prediction in medicine-beyond the peak of inflated
expectations”. N Engl J Med. 376:2507–2509. 2017
15. Yan J, Wang Z, Xu LJ, . “Effects of new regional cooperative rescue model on patients with ST-
elevation myocardial infarction.” Int J Cardiol. 177:494–496. 2017
16. Reinke L, Ö zbay Y, Dieperink W,. “The effect of chronotype on sleepiness, fatigue, and
psychomotor vigilance of ICU nurses during the night shift.” Intensive Care Med. 41:657–
666. 2017
17. Maltese F, Adda M, Bablon A, “Night shift decreases cognitive performance of ICU
physicians.” Intensive Care Med. 42:393–400. 2016.
18. Hu J, Cui X, Gong Y,. P”ortable microfluidic and smartphone-based devices for monitoring of
cardiovascular diseases at the point of care.” Biotechnol Adv. 34:305–320. 2016
19. Wang G, Hao J, Ma J, Jiang H. “A comparative assessment of ensemble learning for credit
scoring.” Expert Syst Appl. 38:223–230. 2011