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\begin{document}

\title{Artificial Intelligence and Atrial Fibrillation}



\author[1]{Ojasav Sehrawat}%
\author[1]{Anthony Kashou}%
\author[1]{Peter Noseworthy}%
\affil[1]{Mayo Clinic Department of Cardiovascular Medicine}%


\vspace{-1em}



  
  \date{June 06, 2024}


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\selectlanguage{english}
\begin{abstract}
In the context of atrial fibrillation (AF), traditional clinical
practices have thus far fallen short in several domains such as
identifying patients at risk of incident AF or patients with concomitant
undetected paroxysmal AF. Novel approaches leveraging artificial
intelligence have the potential to provide new tools to deal with some
of these old problems. In this review we focus on the roles of
artificial intelligence-enabled ECG pertaining to AF, potential roles of
deep learning (DL) models in the context of current knowledge gaps, as
well as limitations of these models. One key area where DL models can
translate to better patient outcomes is through automated ECG
interpretation. Further, we overview some of the challenges facing AF
screening and the harms and benefits of screening. In this context, a
unique model was developed to detect underlying hidden AF from sinus
rhythm and is discussed in detail with its potential uses. Knowledge
gaps also remain regarding the best ways to monitor patients with ESUS
and who would benefit most from oral anticoagulation. The AI-enabled AF
model is one potential way to tackle this complex problem as it could be
used to identify a subset of high-risk ESUS patients likely to benefit
from empirical oral anticoagulation. Role of DL models assessing AF
burden from long duration ECG data is also discussed as a way of guiding
management. There is a trend towards the use of consumer-grade
wristbands and watches to detect AF from photoplethysmography data.
However, ECG currently remains the gold standard to detect arrythmias
including AF. Lastly, role of adequate external validation of the models
and clinical trials to study true performance is discussed.%
\end{abstract}%



\sloppy


\textbf{Introduction}

The ECG has an incredible untapped diagnostic and prognostic potential.
This easy to use, omnipresent technology has been largely unchanged for
a century but still remains critical to the everyday clinical workflow.
It is non-invasive, provides rapid actionable insights, easy to perform
and readily available at a low cost compared to advanced cardiac imaging
modalities.\textsuperscript{1} These attributes make it a go-to baseline
evaluation test for many patients across the spectrum of healthcare
settings.\textsuperscript{1,2}

Currently, the ECG is used primarily as a diagnostic tool rather than a
broad screening tool for conditions other than rhythm
disorders.\textsuperscript{3-5} This is largely because standard ECG
interpretation using analogue or feature-based approaches lack the
negative predictive value to exclude cardiac disease (such as myocardial
or valvular heart disease) in ECGs that `appear normal'. Furthermore,
the accuracy of results depends considerably on the interpreter's
competency level. However, this could soon change as technological
advances are breathing new life into this century old modality.

This potential change has come forth due to recent computational
advancements which have allowed a significant improvement in the machine
learning algorithms alongside a vast availability of well-annotated
digitalized ECG data.\textsuperscript{6} Particularly important has been
the application of a branch of machine learning known as deep learning
(DL) to the ECG\textsuperscript{7} which has allowed investigators to
derive new insights from these voltage-time matrices. It is also now
evident that largely undefined markers of health exist that, while
unapparent to an expert cardiologist, can be recognized by deep neural
network (DNN) models.\textsuperscript{8} Information at the
point-of-care can also be leveraged to facilitate `cardiologist-level'
ECG interpretation,\textsuperscript{6,9} and even go beyond human
capability to determine age and sex,\textsuperscript{10} left
ventricular dysfunction,\textsuperscript{11} predicting hypertrophic
cardiomyopathy\textsuperscript{12} and atrial fibrillation (AF) from
sinus rhythm\textsuperscript{11} amongst others.

In the context of AF, traditional clinical practices have thus far
fallen short in several domains including identifying patients at risk
of incident AF or those with concomitant undetected paroxysmal AF.
Innovative approaches leveraging AI have the potential to provide
solutions to solve some of these old problems. In this review we focus
only on the roles of AI-enabled ECG (AI-ECG) as it relates to AF, the
potential role of DL models in the context of current knowledge gaps, as
well as the current limitations of these models.

\textbf{Artificial Neural Networks}

Artificial neural networks (ANNs) are computational predictive
analytical systems inspired by the human nervous system. These consist
of a high number of computational nodes (called neurons) that spread
across various layers. In simplest terms, it is comprised of three basic
layers with distinct functions.

\begin{enumerate}
\tightlist
\item
  An input layer, in which data (usually multidimensional vector) is
  ingested and distributed to the hidden layer.
\item
  A hidden layer, which makes decisions to assign random weights within
  each node to determine if it detriments or improves the output
  (referred to as learning). When multiple hidden layers are stacked
  together to perform a complex pattern recognition task, it is referred
  to as DL. There are two fundamental learning approaches, supervised
  and unsupervised learning. Supervised learning is most often used for
  image-based pattern recognition tasks. In this form, for every
  training set (e.g., a single digital ECG), input vectors are
  associated to one or more pre-assigned labels \textsuperscript{13}. In
  contrast unsupervised learning does not have pre-selected labels, but
  rather seeks to find clusters of salient features from the data
  itself.
\item
  An output layer where, in the case of supervised learning, a system
  output is compared to the preset label output and backpropagated to
  the previous layers with the goal of reducing classification error as
  much as possible by tuning the weights.
\end{enumerate}

Convolutional neural networks (CNN) are similar to traditional ANN, but
are particularly well suited for image recognition tasks, and thus
reduce the parameters required to set up the model. The capability of
these models to analyze subtle details from abstract data is remarkable
and far superior to humans. More details on these models are described
elsewhere.\textsuperscript{14}

\textbf{The AF Epidemic}

AF has emerged as a major public health problem and global
epidemic.\textsuperscript{15,16}~Not only is it the most common
arrythmia but it is also responsible for more morbidity than any other
rhythm disorder.\textsuperscript{17,18} Data from the Framingham~Heart
Study revealed~a lifetime risk of approximately 1 in 4~for men and
women~of age 40 years or older.\textsuperscript{19}~This signifies a
substantial burden on healthcare costs with approximately 350,000
hospitalizations, 5 million office visits, 276,000 emergency visits and
234,000 outpatient visits attributable to AF annually within the United
States translating to an estimated cost of treatment at 6.65 billion
dollars.\textsuperscript{18} The prevalence is further estimated to
increase 2.5 to 3-fold which projects to 5.6 million affected Americans
2050.\textsuperscript{20} Additionally, global AF burden is not well
known and is most likely underestimated in many regions outside of North
America and Europe.\textsuperscript{21}

AF is vastly heterogenous with a myriad of causes, presentations and
complications. There is a need to improve contemporary practices as the
prevention and treatment outcomes in AF are
sub-optimal.\textsuperscript{22} AF is an independent predictor of
all-cause mortality, hemorrhage, left ventricular dysfunction and
thromboembolism.\textsuperscript{23,24} A global collaborative effort to
deal with the growing concern of AF is underway especially in the past 2
decades in areas of basic sciences, epidemiology, genetics along with
clinical studies and now with the use of AI.

\textbf{Current Computerized ECG Interpretation Models}

Computerized algorithms designed to interpret 12-lead standard (12 SL)
ECGs have been commercially available since early
1980s.\textsuperscript{25} Currently, computer generated ECG
interpretations are widely used to provide `first pass' interpretations
which are then over-read by trained technicians and physicians. Most of
these models use traditional statistical algorithms and require
intensive feature extraction and engineering to compute. Misdiagnoses
are not uncommon with this approach especially if the reliance on
computer generated interpretation is high.\textsuperscript{13,26} For
instance, in a retrospective review computer interpreted diagnosis of AF
was incorrect in 35\% of the 1085
patients.\textsuperscript{27}Furthermore, the physician ordering the ECG
failed to correct the interpretation in 10\% of these patients. This has
significant downstream effects e.g., inappropriate management and
unnecessary testing.\textsuperscript{27} In one recent study, about 10\%
misinterpretation rates were reported for AF- 47\% of which were not
corrected by overreading primary care
physicians.\textsuperscript{28}Analysis from the SAFE (screening for
atrial fibrillation in the elderly) trial, assessing diagnostic accuracy
of general practitioners (from 49 practices) and traditional computer
interpretations to diagnose AF revealed 20\% missed AF cases and 8\%
false positives compared to reference standard
cardiologists.\textsuperscript{29} These studies highlight the
limitations of current computer algorithms used to interpret ECGs and
the devastating consequences on patient outcomes when there is an
overreliance on them.

\textbf{AI-Enabled ECG Interpretation Model:}

There is much room for improvement in the current automated ECG
interpretation.\textsuperscript{30,31} This change could likely
translate to better outcomes as incorrect labels are associated with
incorrect physician overreads.\textsuperscript{27,32,33} End-to-end DNN
models to interpret ECG have recently shown great promise to replace the
`feature-based' computer algorithms currently used.

In one study, a DNN model was trained on a dataset of 91,232 ECGs to
detect 12 rhythm classes from a single lead, patch based ambulatory
monitor.\textsuperscript{6} Results showed an AUC\textgreater{}0.91 and
a superior model performance compared to separate annotations made by 6
cardiologists. AUC of the model for AF was as high as 0.96 but the small
testing dataset of only 328 ECGs limits the reliability of results for
any individual class \textsuperscript{34}. For external validation, the
model was trained and tested using 2017 Physionet Challenge data with a
relatively larger testing dataset (performance for AF diagnosis- F1
score 0.84). However, the testing dataset was not randomly selected, and
more rare diagnoses were purposefully included which makes the results
less generalizable.\textsuperscript{6}

In another proof-of-concept study, a DNN model was trained using over 2
million ECGs to detect 6 abnormalities from 12 SL
ECG.\textsuperscript{7} However, individual diagnostic accuracy for the
6 abnormalities selected is hard to comment on due to a smaller testing
dataset used to assess several distinct diagnoses. For instance, testing
dataset included only 13 AF cases.\textsuperscript{7}

PhysioNet/computing in cardiology challenge
2017,\textsuperscript{35}allowed an opportunity for external validation
of algorithms from 75 teams which were trained using a common training
dataset of 8,528 single lead ECGs. These then competed head-to-head on a
hidden dataset to diagnose AF amongst the 4 labeled outputs (normal, AF,
other, noisy). Winning algorithms (F1 score 0.83) varied from hand
featured models using random forest, extreme gradient boosting to CNN
and recurrent neural networks. However, the authors concluded that
training set was not sufficient to allow an advantage for more complex
algorithms that require enormous data for parameter and hyperparameter
tuning. Furthermore, although training set was relatively larger (3,658
ECGs with 311 AF cases), only 27.3\% of it was used to rank the
algorithms.\textsuperscript{35}

A preliminary study demonstrated that triaging patients in emergency
care setting from interpretation of 12 SL ECG using a
CNN.\textsuperscript{36} The results were also compared to their
conventional ECG algorithm currently in use. Output was mapped to 16
pre-specified groups to provide actionable information. Results showed
slightly better performance than the traditional interpretation but was
statistically significant. One of the limitations was again a small
testing dataset which included few emergency readings (total
60).\textsuperscript{36}

Our group has developed a DNN model to make a comprehensive 12-lead ECG
interpretation using 2.5 million standard 12 SL-ECGs from 720,000
patients.\textsuperscript{9,37} A `transformer model' was also
incorporated to translate the output into 66 discrete readable ECG
diagnostic codes and make a multilabel prediction comparable to current
computer automated programs. We previously showed the performance of
this model according to all individual 66 codes included using a testing
dataset of 499,917 ECGs.\textsuperscript{37} The overall performance was
an AUC of [?]0.98 for 62 of the 66 reported codes. Recently, the
performance was evaluated head-to-head against the traditional ECG
interpretation software currently in use at our institute and the final
cardiologist over-read diagnosis.\textsuperscript{9} Results showed an
average ideal or acceptable diagnosis of 91.8\% (AI-enabled
interpretation) vs 86.6\% (computer generated interpretation) vs 94\%
(final clinical diagnosis). In some ways these studies show the
potential of DNN models to provide a level of ECG interpretation
previously confined to the realms of field experts.

\textbf{Challenges in Screening}

There are many uncertainties regarding our current approaches to AF
screening. Remaining questions include which subgroup of patients to
screen, the best modality to use for screening and the subset of
subclinical AF (SCAF) cases likely to benefit from oral anticoagulants
(OAC). Two recently published large screening studies have produced
somewhat contradictory results but provide important new insights about
such questions.

In the `clinical outcomes in systematic screening for atrial
fibrillation' (STROKESTOP)\textsuperscript{38} trial, population aged 75
to 76 years without an earlier diagnosis of AF were screened using
intermittent ECG recordings over 2 weeks yielded 3\% detection of new AF
cases with 90\% patients eventually put on OAC.\textsuperscript{39} AF
detection rate was only 0.5\% from the first ECG used to
screen.\textsuperscript{39} This highlights the low yield of using a
single ECG for screening even in elderly population because of a
paroxysmal nature of the arrhythmia. With further continuation of this
study (median follow up 6.9 years), screening resulted in a slight
benefit in outcomes (i.e., stroke, emboli and bleeding) compared to
standard of care.\textsuperscript{38} These results suggest that
screening is safe and beneficial in elderly
population.\textsuperscript{38}

In contrast, the `implantable loop recorder detection of atrial
fibrillation to prevent stroke' (LOOP)\textsuperscript{40} trial
screened patients aged 70-90 years, with at least one additional stroke
risk factor.\textsuperscript{40} In the intervention group, implantable
loop recorders were used for prolonged monitoring to detect AF and
started on OAC if AF lasted more than 6 minutes. Results showed 3 times
increase in AF detection and OAC initiation however, it did not result
in better outcomes (stroke and emboli prevention) compared to the
control group. Different results from this study have been attributed to
detection of very short episodes of AF with loop recorder which might
not benefit from anticoagulation.\textsuperscript{40}

The best approach to population wide screening also remains unresolved.
Systematic screening using (pulse palpation alongside an ECG) has not
been shown to be superior to opportunistic screening (pulse palpation
followed by ECG if the former is positive) in various clinical
trials.\textsuperscript{29,41,42}

\textbf{Benefits and Harms of Screening}

There is 30\% first year mortality rate amongst AF patients who have
stroke and another 30\% are permanently
disabled.\textsuperscript{43}Therefore, one would expect that there is
merit in detecting AF cases early in the course at the stage of
paroxysmal and subclinical stage before considerable remodeling has
occurred to allow for a higher likelihood of spontaneous conversion,
early OAC to prevent strokes, and overall better outcomes for
patients.\textsuperscript{44} Meta-analyses data consistently show lower
risk of primary composite outcomes of stroke and emboli in AF patients
treated with warfarin and direct acting OAC.\textsuperscript{45-47}

Implementation of a risk modification plan (such as weight reduction,
decreasing alcohol intake, treatment of obstructive sleep apnea) in
patients identified to be at a future high risk of developing AF could
also alter the overall outcomes.\textsuperscript{48} Especially as
considerable research efforts to identify risk factors of AF are
transpiring, an effective screening plan could eventually become
crucial.

Lastly, another important consideration is harm from screening which is
not extensively studied.\textsuperscript{49} Potential harms include
misinterpretation of ECGs, with false positive results leading to
unnecessary testing and treatment with OAC. Additionally, with
increasing screening modalities being employed, anticoagulation rates
are bound to increase and thus, there is a need to be mindful of
increased bleeding risk.\textsuperscript{49}

Ideally, the screening modality needs to be cheap, widely available,
non-invasive and non-cumbersome for the patient. Stratifying patients
who are at higher risk is likely beneficial to increase diagnostic yield
of any modality, to be cost effective and reduce unnecessary testing and
distress to patients. Thus, having a tool which can make prediction of
incident AF using only a 12 SL ECG has potential to affect patient
outcomes.

\textbf{AI-ECG AF Model}

To tackle the complex problem of screening for AF, especially pertaining
to paroxysmal and SCAF, our group developed a unique model to predict
the likelihood of a person having underlying hidden AF from a sinus
rhythm `apparently normal' ECG without any additional
information.\textsuperscript{11} The rationale for this study was that
mechanical remodeling in the form of myocyte hypertrophy, fibrosis and
chamber enlargement might lead to ECG changes yet undefined and too
subtle to be studied effectively by human potential. For instance,
evidence of interatrial block (Baye\selectlanguage{english}s syndrome) which is seldom reported
on ECGs correlates to both risk of incident AF and
stroke.\textsuperscript{50,51}

Over 20 years of ECG data from 180,922 patients and 649,931 normal sinus
rhythm ECGs were analyzed. Patients were randomly assigned into 3
groups- 70\% for training dataset, 10\% for internal validation
(optimization and selecting hyperparameters) and 20\% testing set
(previously unexposed ECGs). Within each dataset there were 2 groups-
patients with at least one ECG confirmed AF/atrial flutter diagnosis and
patients with no AF rhythms recorded. A 31-day window period preceding
the AF recorded ECG was taken in the disease group and all sinus rhythm
ECGs were included from the control group. This short 31-day period
prior to AF diagnosis was taken to include ECGs with the maximum
potential markers associated with AF and left atrial remodeling.

To optimize performance, eight independent leads (leads I, II, and
V1--6) were selected because any linear function of the leads could be
learned by the models (8\selectlanguage{ngerman}×5000 matrix). The model was then tested on a
dataset of 130,802 sinus rhythm ECGs (3051 verified AF cases) (Table 1).
Model performed well with an AUC of 0·87 when a single ECG was used with
no additional information and an AUC of 0.90 when multiple ECGs were
used.

\textbf{Potential Applications of AI-ECG AF Model}

AI-ECG model thresholds can be adjusted to be more specific for patients
with low pretest probability such as healthy population. This could help
make a more cost-effective strategy to rule in patients for further
testing (Figure 1).

To rule out patients with AF, a higher sensitivity is needed such as in
patients with cryptogenic stroke. Patients with a higher likelihood of
AF could then undergo more extensive monitoring (eg., beyond the
recommended 30-day monitoring period) (Figure 1).\textsuperscript{52}

As part of a risk score to predict 5- or 10-year probability of incident
AF, which may have utility in prevention trials and screening
programs.\textsuperscript{53} Previously this has been attempted with
various risk scores, such as the Framingham AF and CHARGE-AF risk
scores.\textsuperscript{54,55} Although not developed for this purpose,
the usefulness of the model as an independent predictor of future AF has
been externally validated in a population-based Mayo Clinic study of
Ageing.\textsuperscript{56} In the study, both CHARGE-AF score and
AI-ECG AF model had similar performance (C statistic of 0.69 for both).
Combining the two resulted in a slight increase in overall performance.
Participants with an AI-ECG AF model output of \textgreater{}0.5 had a
cumulative incidence of 21.5\% at 2 years and 52.2\% at 10
years.\textsuperscript{56}

In another recent study, a methodology similar to AI-ECG AF model was
used to create an ECG-based algorithm for AF prediction. This model was
trained using sinus rhythm ECGs at baseline and a window of interest for
ECG was at least 1-3 years before AF diagnosis (compared to within a
30-day window in the Mayo Clinic model). They evaluated the performance
of this model using UK Biobank data and showed an overall comparable
performance to the CHARGE-AF risk score. There was a modest improvement
in model performance when the AI-ECG was added to CHARGE-AF. These
studies demonstrate that the ECG holds value in predicting AF (not just
detecting concomitant AF) and that the clinical features captured by
CHARGE-AF likely explain a lot of the predictive poster of the AI-ECG
model.\textsuperscript{57}

As a tool to guide management, for instance, identification of a
high-risk subset of patients with ESUS may help determine who may
benefit from empirical anticoagulation. This utility is currently
planned as a follow up study to the BEAGLE trial.\textsuperscript{34}

It may have utility to guide management in difficult cases when AF is
highly suspected but has eluded diagnosis (case
report).\textsuperscript{58}

\textbf{Cryptogenic Stroke}

Cryptogenic strokes are defined as symptomatic cerebral infarcts for
which no probable cause is identified after thorough standard
evaluation.\textsuperscript{59} About one-third of transient ischemic
attacks \textsuperscript{60} and ischemic strokes are of undetermined
etiology (cryptogenic).\textsuperscript{60} These numbers have decreased
over time from as high as 40\% in 1970's\textsuperscript{61} to as low
as 10-15\% today in advanced centers with extensive testing
modalities.\textsuperscript{29,62} This highlights the importance of
better ways to investigate patients in order to initiate an appropriate
and timely secondary prevention strategy.

AF prevalence as high as 24.6\% has been noted in patients presenting
with first time ischemic strokes and was especially high amongst elderly
females.\textsuperscript{63,64} In CRYSTAL AF (Cryptogenic Stroke and
Underlying AF) trial, AF detection was compared between insertable
cardiac monitors \textsuperscript{65} and conventional follow-up in
patients with cryptogenic stroke or TIA.\textsuperscript{65} Results
showed detection rate of AF at 8.9\% vs 1.4\% at 6 months and 12.4\% vs
2\% at 12 months for ICM vs conventional follow-up respectively. This
proves that many cases go undetected after the first thromboembolic
event and foreshadow a recurrence which could have been prevented. In
the same trial, mean time in AF was only 4.3 minutes a day and about
74\% patients were asymptomatic, highlighting the increasingly difficult
diagnosis of paroxysmal AF in these patients.\textsuperscript{65} Since
prolonged monitoring is inconvenient and expensive, the authors called
for further studies to determine which risk factors could better
delineate which patients would derive the most clinical benefit from
extensive monitoring.

Current recommendation for patients with cryptogenic stroke is that a
prolonged rhythm monitoring of about 30 days within 6 months of index
event is reasonable.\textsuperscript{66} Furthermore, even though a
single 1-hour episode of AF during 2 years of monitoring doubles the
risk of stroke, the treatment benefit of anticoagulants vs antiplatelet
agents is not clearly defined amongst low burden paroxysmal AF patients.

A large subgroup of cryptogenic strokes (80-90\%), in which the cause is
almost always embolic (superficial or deep large infarcts) is termed
embolic stroke of undetermined significance
(ESUS).\textsuperscript{59}Low burden paroxysmal AF forms an important
underlying cause for these patients. However, empiric anticoagulation is
not recommended as bleeding risk seems to outweigh the
benefits.\textsuperscript{67,68}

Therefore, knowledge gaps remain regarding the best way to monitor
patients with ESUS and who would benefit most from oral anticoagulation.
The AI-ECG AF model is one potential way to tackle this complex problem
as it could be used to identify a subset of high-risk ESUS patients most
likely to benefit from empirical oral anticoagulation. In a
retrospective study of stroke patients, we found a strong association of
probability output \textgreater{}0.2 as noted by the AI-ECG AF model and
detection of AF by ambulatory cardiac monitoring OR of 5.47 (95\% CI
1.51-22.51; P = 0.01).\textsuperscript{52} However, we were limited by
the detection rate of AF amongst the ESUS patients due to a shorter
average monitoring time. A clinical trial `batch enrollment for
AI-guided intervention to lower neurologic events in unrecognized AF'
(BEAGLE) is currently underway to assess the use of this
model.\textsuperscript{34} Participants are selected using natural
language processing tools if they are at high risk for incident AF as
identified using the algorithm and are eligible to receive
anticoagulation if AF is detected. Enrolled patients are then mailed a
device to continuously monitor for up to 30 days. This is a completely
off-site trial and will allow vetting and validation of the model in a
real-world scenario.\textsuperscript{34}

\textbf{AF Burden}

Currently, AF is classified as paroxysmal, persistent or permanent, but
treatment generally relies on a binary classification (i.e., presence or
absence of AF to determine the decision for
anticoagulation).\textsuperscript{69} A quantitative approach to define
the burden of AF has been suggested. This factors in the number of
episodes or the proportion of time a patient spends in AF during a
particular monitoring period.\textsuperscript{70} AF burden has been
noted to have a predictive value for AF-related strokes and thus, could
guide management.\textsuperscript{70,71} However, even with wearable and
implantable technologies allowing longer monitoring durations, burden of
AF is difficult to determine. Also, the minimum AF duration required for
anticoagulation is not well defined.\textsuperscript{72}

Another scenario in which AF burden becomes important is SCAF. Higher AF
burden along with the traditional risk factors
(CHA\textsubscript{2}DS\textsubscript{2}-VASc score) are more likely to
benefit from oral anticoagulants than low to medium burden of AF and
without risk factors.\textsuperscript{73} A general rule of thumb for
anticoagulation, regardless of indication, is the need to maintain
efficacy while minimizing the bleeding risk.\textsuperscript{74}However,
the exact cut-offs are not well defined and leads to considerable
variation in management depending on the provider.\textsuperscript{75}
With advancements in wearable and intracardiac monitoring techniques as
well as their wider availability,\textsuperscript{76} detection of SCAF
is also increasing and the knowledge gaps need to be addressed more
clearly. Two highly anticipated trials Apixaban for the Reduction of
Thrombo-Embolism in Patients With Device-Detected Sub-Clinical Atrial
Fibrillation\textsuperscript{\textbf{77}} and Non-vitamin K Antagonist
Oral Anticoagulants in Patients With Atrial High Rate
Episodes\textsuperscript{78} will likely help inform management in these
patients.

A preliminary study using DNN model has been able to assess AF burden
from 24-hour Holter monitor data.\textsuperscript{79} Although this
proves feasibility of developing the prototype, translation of such
models into clinical use would require validation and clearer guidelines
as to the importance of AF burden and duration for guiding
anticoagulation therapy.

\textbf{Deep Learning Approach to AF Detection Using
Photoplethysmography Data}

Photoplethysmography (PPG) data is a pulse pressure waveform detected
using a light source and a photodetector.\textsuperscript{80} This
technology has been successfully used previously to detect oxygen
saturation, heart rate and respiratory rate. On PPG data, AF is
manifested as variable pulse-to-pulse intervals and
morphologies.\textsuperscript{80} Several studies have tested DL
algorithms on PPG data to detect AF, although many of these included
very few patients.\textsuperscript{81,82}

There is also a trend towards the use of consumer-grade wristbands and
watches to detect AF. In Huawei heart study, of the 187,912 individuals
who used smart devices for PPG analysis, only 0.23\% received `suspected
AF' notification.\textsuperscript{83} Out of those undergoing an
effective follow up, 87\% were confirmed as having AF. These results
show an impressive positive predictive value of
91.6\%.\textsuperscript{83}

In Apple Heart Study, Apple smartwatches were used to detect AF using a
proprietary PPG based algorithm.\textsuperscript{84} Of the 419,297
patients enrolled over an 8-month period, only 2161 (0.52\%) received a
notification for irregular pulse. After a telemedicine visit, ECG
patches were mailed to the patients to be worn for up to 7 days. Among
450 patients who returned patches with analyzable data, only 34\% were
diagnosed with AF, even though the algorithm was designed to maximize
specificity. Furthermore, sensitivity and thus usefulness as a screening
tool could not be assessed from this study.\textsuperscript{84}

The Fitbit Heart Study- another large prospective study applying a
similar methodology is currently underway.\textsuperscript{85} These
studies highlight the barriers to mass screening for AF in healthy
populations particularly given the low benefit to cost ratio.

Some key limitations of using PPG data include high false positive rate
due to detection of other arrythmias mimicking AF, low signal to noise
ratio and motion artifacts.\textsuperscript{80} One study showed that
almost 40\% of the signal was unusable.\textsuperscript{86} Due to these
limitations, the 12-SL ECG remains the gold standard to detect
arrythmias including AF.

\textbf{Scalability}

In primary care settings and emergency units, there is often a lack of
trained specialists to interpret complex ECGs and make rapid
diagnosis.\textsuperscript{87} Even the combined accuracy of
practitioners in these settings with current computer interpretations
for AF diagnosis remains insufficient.\textsuperscript{29} This need is
even greater in less developed countries which contribute about 75\% to
the overall cardiovascular mortality.\textsuperscript{7} As discussed
previously, automated AI-ECG interpretation could enable expert level
diagnoses and streamline clinical workflow in these settings.

A network once trained can be fine-tuned to widespread applications and
smart devices. For instance, we have shown the application of an
algorithm trained using 12 lead ECG to detect serum potassium levels and
applied it to a single lead ECG recorded using a
smartphone.\textsuperscript{88}

To bring these tools to the point-of-care we have incorporated AI-ECG
tools into the electronic health record as an `AI-ECG
Dashboard'.\textsuperscript{8} With the click of a button, clinicians
can have all the ECGs available for that patient analyzed with AI-ECG
probability outputs for various diagnoses. A major advantage of
incorporating AI-ECG algorithms into practice is their ability to keep
learning indefinitely as more information is added. This essentially
creates a self-improving healthcare system.\textsuperscript{36}

The application of DNN models to long duration signals like 24-hour
Holter monitor data to detect paroxysmal AF has shown good preliminary
results.\textsuperscript{79} This has broad applications for development
of a new generation of real-time analytical tools to detect AF from the
growing number of ambulatory monitoring devices.

\textbf{Limitations}

After the development of an AI-ECG model, rigorous external validation
in diverse populations and clinical trials to demonstrate superior
clinical outcomes to the standard of care is needed. To date, most
models have not undergone rigorous evaluation and thus their results
remain confined to their own datasets. Furthermore, due to the several
knowledge gaps surrounding AF, it becomes challenging to prove immediate
benefits in clinical outcomes.

A limitation to widespread applicability of these models is the need to
be tailored to the target application. Although, cross domain utility of
a pre-trained model to perform the same function has shown promising
results. For instance, in a study a DNN model was trained to detect
paroxysmal AF from 24-hour Holter ECG and then fine tuned to a
completely different input data of smartwatch PPG signal with superior
results (AUC of 0.97).\textsuperscript{79} However, as noted by authors,
this study was limited by a lack of gold standard labels to study true
performance.\textsuperscript{79}

Several other limitations surrounding AI such as data security,
perpetuating bias, physician acceptance and regulatory considerations
are also important to be addressed and have been discussed elsewhere in
more detail.\textsuperscript{8,89}

\textbf{Conclusion}

Algorithms using AI to interpret ECGs in various new ways have been
developed. While still much work needs to be done, these technologies
have shown enormous potential in a short span of time. With further
advancements and continuous research, these novel ways of interpretation
may well become part of everyday clinical workflow. However, rigorous
testing is key with development of new models. Testing datasets need to
be large to show accurate results, especially when several different
outputs are being considered. Finally, the models need to undergo
appropriate external validation and clinical trials to note
generalizable benefits in diverse populations when compared to the
standard of care. This may also help alleviate some of the current
hesitance in clinicians from acceptance of AI tools.

\textbf{Figures/Tables}

\textbf{Table 1}

Summary (non-exhaustive) list of various DL models, with performance
discussed in context of AF for this article.

\textbf{(} AF: Atrial Fibrillation; AUC: Area under the receiver
operating characteristic curve; DNN: Deep neural network; F1: F1 score;
S12L-ECG: 12 lead standard ECG; SN: Sensitivity; SP: Specificity)

\textbf{Figure 1}

Overview of performance, validation, potential uses and ongoing work
with AI-ECG AF model from Mayo Clinic.

(AF: Atrial Fibrillation; AI: Artificial Intelligence; AUC: Area under
the receiver operating characteristic curve; ESUS: Embolic stroke of
undetermined significance; RCT: Randomized controlled trial)

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