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\begin{document}

\title{Will the blooming of artificial intelligence modify our approach to
atrial fibrillation cure?}



\author[1]{LUIGI SCIARRA}%
\author[2]{Antonio Scarà}%
\affil[1]{University of L'Aquila Faculty of Medicine and Surgery}%
\affil[2]{San Carlo di Nancy Hospital - GVM care and research}%


\vspace{-1em}



  
  \date{May 26, 2024}


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\selectlanguage{english}
\begin{abstract}
Catheter ablation represents nowadays a cornerstone for the treatment of
atrial fibrillation. However, the benefit-risk ratio of this procedure
still needs to be significantly improved. Many strategies have been
proposed for this goal. Increased experience of operators and
technological advances appear to be crucial points. Very important in
this scenario is improving patients selection for ablation, as well. The
blooming of artificial intelligence and machine learning represents a
solid promise in many fields of medicine. Even in the field of atrial
fibrillation cure they could prove to have a strong impact. Further
prospective studies will surely help to define the real role of these
techonologies in the treatment of atrial fibrillation.%
\end{abstract}%



\sloppy


Will the blooming of artificial intelligence modify our approach to
atrial fibrillation cure?

Luigi Sciarra MD\textsuperscript{(1)}, Antonio Scar\selectlanguage{ngerman}à
MD\textsuperscript{(2)}

1 University of L'Aquila -- Department of Cardiovascular disease

2 San Carlo di Nancy Hospital, GVM care and research --
Electrophysiology Unit

Catheter ablation represents nowadays a cornerstone for the treatment of
atrial fibrillation (AF) \textsuperscript{(1)}.

Technological advances have led to significant progress in terms of
procedural safety and acute efficacy \textsuperscript{(2)}. Our group
has also worked in this direction evaluating different types of ablative
solutions \textsuperscript{(3-4)}.

However, in spite of the consistent technological progresses and the
increasing experience of the electrophysiologists, the medium-long term
results of this type of intervention still appear far from success rates
close to 100\% \textsuperscript{(5)}. Moreover, the price that we pay in
terms of procedural-related complications is still not
negligible\textsuperscript{(6)}.

Possible explanation for these improvable results could be searched in
the difficulties of characterizing the various forms of AF and precisely
defining the substrate that induce and maintain the arrhythmia in the
single patients.

To this goal, we tried to evaluate whether an ablative approach,
tailored on the patho-physiological mechanisms of the arrhythmia could
provide optimal results, minimizing the procedural risks for patients.
We demonstrated that some forms of paroxysmal atrial fibrillation can be
triggered by other kind of arrhythmias with focal (atrial tachycardia)
or reentrant mechanism (WPW syndrome or nodal reentrant
tachycardia)\textsuperscript{(7-8)}. On the other hand, in patients with
vagally-mediated AF we also investigated the possibility of using right
atrial ganglion ablation to modify the influence of the autonomic
nervous system on the heart, in order to prevent atrial fibrillation
episodes \textsuperscript{(9)}.

Moreover, technological progress has recently made it possible to carry
out very high-density mapping of the cardiac chambers, and therefore
also of the atria, to try to characterize the underlying arrhythmic
substrate \textsuperscript{(10)}. However, the enormous amount of
information generated is often difficult to interpret, particularly
during the ablative procedure, since the importance of searching for
both structural and functional substrates appears
evident\textsuperscript{(11)}. For this reason, the use of a form of
artificial intelligence (AI), capable of rapidly acquiring and analyzing
data from various sources, seems to be a promising scenario in order to
simplify this issue \textsuperscript{(12)}.

Razeghi and co-authors have explored the possibility of using machine
learning (ML) to personalize AF management strategies and develop
customized ablative approaches, integrating atrial geometry data derived
from CT scans and patient-specific clinical data. Authors should be
commended for exploring such an important and current topic. Obviously,
the routine use of ionizing radiations to develop the anatomical model
by CT-scan (while cardiac MRI or intracardiac echocardiograms were not
considered) could seem questionable, since ALARA (as low as reasonably
achievable) principle could be not applied. However, the horizon that it
opens appears extremely fascinating for many reasons. For example, to
date technology developers are providing efforts to achieve transmural
and efficient lesions (regardless of energy source used) in order to
have a definitive pulmonary veins (PV) isolation. This mainly depends on
ostial/antral PV thickness. So, it could be extremely interesting to
have a predictive model, based on left atrium thickness, and analysed by
artificial intelligence, and to evaluate its impact on the outcomes of
ablation treatment.

Moreover, moving from the paroxysmal to persistent AF, reproducible
ablative strategies that are suitable for all patients are still
lacking. This probably depends on the different structural or functional
substrates in every patient, that today we are often unable to precisely
define. The possibility to analyse an enormous amount of electrical
information, derived from electro-anatomical maps, and to combine them
with additional anatomical features in a predictive model using an AI
could be a desirable option.

We strongly believe that AF catheter ablation can be proposed as a
curative solution for many patients. However, the way to achieve
excellent benefit-risk ratios seems to be long, even nowadays. In this
scenario, the role of ML anf AI seems very promising, particularly in
patients selection and in personalizing treatment strategies.

References

\begin{enumerate}
\tightlist
\item
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\end{enumerate}

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