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\begin{document}

\title{Advances in Technology and Remote Cardiac Monitoring: Living the future
of cardiovascular technology}



\author[1]{Alaa Omar}%
\author[2]{Omar Lattouf}%
\affil[1]{Mount Sinai Morningside Hospital}%
\affil[2]{Emory University School of Medicine}%


\vspace{-1em}



  
  \date{May 17, 2024}


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\selectlanguage{english}
\begin{abstract}
Hospital administrations and providers are more than ever in need for
new technologies and innovative methods with clinical benefit at lower
costs. Surgeons and clinicians depend on conventional risk
stratification scores developed to allow physicians to establish the
risk of perioperative mortality. However, the current practiced models
of preventive cardiology largely depend on patient motivation and
awareness to be able to apply such risk scores appropriately. It was not
until the appearance of miniaturized pocket-sized, user-friendly digital
technologies that the awareness started to grow, highlighting the
importance of role of technology and artificial intelligence (AI) in
modern day medicine.%
\end{abstract}%



\sloppy


\textbf{Advances in Technology and Remote Cardiac Monitoring: Living the
future of cardiovascular technology}

Alaa Mabrouk Salem Omar, MD, MSc, PhD (1,3), Omar Lattouf, MD, PhD
(2,3,4)

1) Department of Cardiovascular Medicine, Mount Saini Morningside, New
York, NY, USA

2) Department of Cardiovascular Surgery, Mount Sinai Morningside, New
York, NY, USA

2) Icahn School of Medicine at Mount Sinai, New York, NY, USA

3) Emory University School of Medicine, Atlanta, GA, USA

\textbf{Author of correspondence}

Omar Lattouf, MD, PhD

Professor of Cardiovascular Surgery

Department of Cardiovascular Surgery

Mount Sinai Morningside Hospital

111 Amsterdam Street, New York, New York 10025

Phone: 404-550-2076

Email: omar.lattouf@mountsinai.org

\textbf{Abstract}

Hospital administrations and providers are more than ever in need for
new technologies and innovative methods with clinical benefit at lower
costs. Surgeons and clinicians depend on conventional risk
stratification scores developed to allow physicians to establish the
risk of perioperative mortality. However, the current practiced models
of preventive cardiology largely depend on patient motivation and
awareness to be able to apply such risk scores appropriately. It was not
until the appearance of miniaturized pocket-sized, user-friendly digital
technologies that the awareness started to grow, highlighting the
importance of role of technology and artificial intelligence (AI) in
modern day medicine.

\textbf{Key Words:} Artificial Intelligence, cardiac surgery, outcomes,
remote cardiac monitoring

The 21\textsuperscript{st} century has witnessed outstanding advances in
medical diagnostics and interventional methods resulting in an
exponential increase in medical data. Meanwhile, cardiovascular diseases
remain a leading cause of death in the United States, claiming the lives
of 17.9 million people yearly according to the World Health Organization
(1) with annual costs of care that are projected to increase from \$21
to \$53 billion by the year 2030 (1). As such, hospital administrations
and providers are more than ever in need for new technologies and
innovative methods that can show clinical benefit and improve outcomes
at lower costs. There are inherit challenges facing these technologies,
however, as it is hard to expect when symptoms related to heart
conditions will develop and who will develop complications of cardiac
procedures and surgeries. Surgeons and clinicians depend on well-studied
conventional risk stratification scores, such as EuroSCORE II, STS risk
scores, which were developed to allow physicians to establish the risk
of perioperative mortality (2). Recent studies, however, suggest that
such scores, despite the improvements, tend to overestimate risk in the
general population and underestimate it in high-risk populations;
perhaps because these scores are built in a one-size-fit-all fashion
ignoring the natural variability and heterogeneity (3). In addition, the
current practiced models of preventive cardiology largely depend on
patient motivation and awareness to be able to apply such risk scores
appropriately. It was not until the appearance of miniaturized
pocket-sized, user-friendly digital technologies that the awareness
started to grow, highlighting the importance of role of technology and
artificial intelligence (AI) in modern day medicine(4).

The applications of AI in our everyday life nowadays are various. From
wearable devices to leadless pacemakers, holograms, robotic cardiac
procedures, and transcutaneous techniques, the cardiovascular field is
flooded with AI applications that has changed the language of clinical
medicine quite considerably(4). Wireless devices, specifically, have
invaded the medical field and are now integrated in the care of cardiac
patients with the ability to remotely monitor various aspects of
clinical and vital parameters. The widespread availability and use of
cellular and wireless devices has grasped the attention of both the
consumers and healthcare professionals. Millions of people nowadays use
wearable devices such as~Apple Watch to track their health, do simple
diagnostic tests such as electrocardiographs and even alert emergency
medical systems (EMS) in case of syncopal episodes or malignant
ventricular rhythms (5). This consumer-level early warning network will
likely play a big role in the coming years to automatically triage
patients and help know when they should seek professional healthcare
help.

Along these lines, At\selectlanguage{polish}ı\selectlanguage{english}lgan, et al., report in this issue of the
successful use of a sophisticated method of remote monitoring of
patients after having cardiac surgery. The device used incorporates
measurements of clinical data such as blood pressure, heart rate, oxygen
saturation, body temperature, blood glucose, as well as static and
ambulatory electrocardiography {[}ECG{]} recording. Moreover, the system
used, incorporated medication reminder, suggested daily life activities,
diet and nutrition plans, and a video conference and communication
platform. The use of this miniature method resulted in early detection
of life-threatening events post-operatively, eliminated the need for
multiple device monitoring after discharge, resulting in increasing
patient's compliance, and was a successful aid towards appropriate
hospitalization as well as avoidance of unnecessary hospitalizations.

The findings of the current study confirm that we are at the verge of a
potential medical technological revolution. In a time of social
distancing and no contact services, that the recent COVID-19 pandemic
has cast over us, such a revolution appears to be more needed than ever.
It is not a science fiction subject or an academic fantasy anymore. It
is real and pivotal in our current practices to improve healthcare.

The technological applications discussed in the current study cannot be
more relevant during this time of uncertainty. As such, one can now
envision the future of cardiovascular medicine and surgery in a reduced
contact environment from both a patient side and the healthcare provider
side. This futuristic aspect of the relevance of this study are two
folds; both have been developing with variable speeds, and presented
herein in the forms of questions. The first is: how can we use the data
retrieved from technological advances? The second is: , will the use the
data itself, as novel diagnostic approach, uncover disease processes
previously unseen?

The first aspect of this development is well represented in the study at
hand in which impressive hardware and software technologies are
developed with reported abilities to remotely and quasi-automatically
collect, interpret and detect clinical abnormalities! These devices are
programmed to operate semi automatically to gather and forward data to a
monitoring center controlled by trained personnel.

The tele-health monitoring system, we are informed would automatically
alert the responsible physicians, or his/her designee on worrisome
developments.

As one discusses tele-health applications, one must bring Artificial
Intelligence (AI) into focus, where AI applications are starting to
exist and replace some tasks performed by healthcare specialists and
technicians in both inpatient and outpatient settings. At this stage, AI
applications using machine learning and deep learning algorithms promise
exploration of new disease subclasses previously unseen to the human
eyes. Efforts of operationalizing big data analytics towards disease
phenotyping and better understanding the pathological and
epidemiological heterogeneity are already underway(3, 6-10). This
promises not only ability to collect data in an automated and remote
fashion, as suggested in the current study, but also to automate and
innovate diagnostics as well. However, several challenges do exist and
must be addressed and resolved before AI-based diagnostic algorithms can
be broadly applied in clinical practice.~First, the development of
reliable AI algorithm requires massive amounts of pre-labeled data for
training computers in the quest for achieving human-level classification
performance. The type and amount of data suitable for training such
algorithms exist but frequently face the obstacle of healthcare privacy
laws and medical data regulations making medical data less available
compared to other fields of computer science. The development of a
homogenous nationwide database using calibrated algorithms can be useful
in that regard. Internet of medical things (IoMT) is a much-awaited
promise that would take such solutions to an international level.

Furthermore, the rapid development of software and hardware biosensors,
wearable monitors and implantable devices has not been accompanied by
the same rate of development of the software that will be needed to
manage the enormous amount of data generated from such devices. The
ability to process massive data resulting from variabile of vendors,
operators, software versions, and acquisition techniques can confound
data processing. This will require tremendous bioinformatics
capabilities to eventually understand and analyze such expansive amount
of data.(11) (12)

In conclusion, the cardiac patient of the future, wired with a network
of biosensors, wearable monitors and implantable miniature device,
investigated and operated upon with robotic imaging arms and analyzed by
computers will be extremely different from our current day-to-day
patients. These biosensors will gather and transmit tremendous amount of
personalized data and clinical responses to daily stimuli to personal
smartphones powered to processes gathered data and direct the patient
towards the next appropriate action or even do it independently. The
future has already arrived, are we ready? (13-15)

\textbf{References:}

1. Levy D, Kenchaiah S, Larson MG, Benjamin EJ, Kupka MJ, Ho KK, et al.
Long-term trends in the incidence of and survival with heart failure. N
Engl J Med. 2002;347(18):1397-402.

2. Sullivan PG, Wallach JD, Ioannidis JP. Meta-Analysis Comparing
Established Risk Prediction Models (EuroSCORE II, STS Score, and ACEF
Score) for Perioperative Mortality During Cardiac Surgery. Am J Cardiol.
2016;118(10):1574-82.

3. Omar AMS, Krittanawong C, Narula S, Narula J, Argulian E.
Echocardiographic Data in Artificial Intelligence Research: Primer on
Concepts of Big Data and Latent States. JACC Cardiovasc Imaging.
2020;13(1 Pt 1):170-2.

4. Bhattad PB, Jain V. Artificial Intelligence in Modern Medicine - The
Evolving Necessity of the Present and Role in Transforming the Future of
Medical Care. Cureus. 2020;12(5):e8041.

5. Isakadze N, Martin SS. How useful is the smartwatch ECG? Trends
Cardiovasc Med. 2020;30(7):442-8.

6. Lancaster MC, Salem Omar AM, Narula S, Kulkarni H, Narula J, Sengupta
PP. Phenotypic Clustering of Left Ventricular Diastolic Function
Parameters: Patterns and Prognostic Relevance. JACC Cardiovasc Imaging.
2018.

7. Narula S, Shameer K, Salem Omar AM, Dudley JT, Sengupta PP. Reply:
Deep Learning With Unsupervised Feature in Echocardiographic Imaging. J
Am Coll Cardiol. 2017;69(16):2101-2.

8. Narula S, Shameer K, Salem Omar AM, Dudley JT, Sengupta PP.
Machine-Learning Algorithms to Automate Morphological and Functional
Assessments in 2D Echocardiography. J Am Coll Cardiol.
2016;68(21):2287-95.

9. Omar AMS, Narula S, Abdel Rahman MA, Pedrizzetti G, Raslan H, Rifaie
O, et al. Precision Phenotyping in Heart Failure and Pattern Clustering
of Ultrasound Data for the Assessment of Diastolic Dysfunction. JACC
Cardiovasc Imaging. 2017;10(11):1291-303.

10. Salem Omar AM, Shameer K, Narula S, Abdel Rahman MA, Rifaie O,
Narula J, et al. Artificial Intelligence-Based Assessment of Left
Ventricular Filling Pressures From 2-Dimensional Cardiac Ultrasound
Images. JACC Cardiovasc Imaging. 2018;11(3):509-10.

11. Blier L, Wolinski P, Ollivier Y. Learning with Random Learning
Rates. arXiv preprint arXiv:181001322. 2018.

12. Lin M, Chen Q, Yan S. Network in network. arXiv preprint
arXiv:13124400. 2013.

13. Krittanawong C, Zhang H, Wang Z, Aydar M, Kitai T. Artificial
intelligence in precision cardiovascular medicine. Journal of the
American College of Cardiology. 2017;69(21):2657-64.

14. Krittanawong C, Tunhasiriwet A, Zhang H, Wang Z, Aydar M, Kitai T.
Deep learning with unsupervised feature in echocardiographic imaging.
Journal of the American College of Cardiology. 2017;69(16):2100-1.

15. Picard MH, Adams D, Bierig SM, Dent JM, Douglas PS, Gillam LD, et
al. American Society of Echocardiography recommendations for quality
echocardiography laboratory operations. Journal of the American Society
of Echocardiography. 2011;24(1):1-10.

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