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\begin{document}

\title{Virtual reality in cardiac interventions -- New tools or new toys?}



\author[1]{Simon Sündermann}%
\author[1]{Anja Hennemuth}%
\author[2]{Jörg Kempfert}%
\affil[1]{Charité Universitätsmedizin Berlin}%
\affil[2]{Deutsches Herzzentrum Berlin}%


\vspace{-1em}



  
  \date{May 27, 2024}


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\selectlanguage{english}
\begin{abstract}
Improvements in medical imaging and a steady increase in computing power
are leading to new possibilities in the field of cardiovascular
interventions. Interventions can be planned in advance in greater
detail, even to the point of simulating procedures. Nevertheless, all
techniques are at an early stage of development. It is of utmost
importance that tools, especially if they can be used as decision
support are intensively validated and their accuracy is demonstrated. In
our commentary, we summarize current techniques for guiding improvement
planning, but also critically discuss the downsides of these techniques.
Following the work of Kenichi and colleagues, we also discuss necessary
steps in advancing new tools and techniques, particularly as they are
used in routine clinical practice. We also discuss the role of
artificial intelligence, which could play a crucial role in this context
in the future.%
\end{abstract}%



\sloppy


\section*{Virtual reality in cardiac interventions -- New tools or new
toys?}

{\label{virtual-reality-in-cardiac-interventions-new-tools-or-new-toys}}

\selectlanguage{ngerman}\subsection*{\texorpdfstring{Simon H.
Sündermann\textsuperscript{1,2,3,*}, Anja Hennemuth\textsuperscript{4},
Jörg
Kempfert\textsuperscript{1,2,3}}{Simon H. Sündermann1,2,3,*, Anja Hennemuth4, Jörg Kempfert1,2,3}}

{\label{simon-h.-suxfcndermann123-anja-hennemuth4-juxf6rg-kempfert123}}

\textsuperscript{1} Charité- Universitätsmedizin Berlin, Department of
Cardiovascular Surgery, Berlin, Germany

\textsuperscript{2} German Heart Center Berlin, Department of
Cardiothoracic and Vascular Surgery, Berlin, Germany

\textsuperscript{3} DZHK~(German Centre for Cardiovascular Research),
partner site Berlin, Berlin, Germany

\textsuperscript{4} Charité- Universitätsmedizin Berlin, Institute of
Computer-assisted Cardiovascular Medicine,

Berlin, Germany

\emph{Corresponding author:}

PD Dr. med. Simon H. Sündermann, MD

Charité- Universitaetsmedizin Berlin

Department of Cardiovascular Surgey

Augustenburger Platz 1

13353 Berlin

Phone: +49 30 450 565841

Email: simon.suendermann@charite.de

Web: https://herzchirurgie.charite.de/

https://tavi.charite.de/

In the study by Kenichi and colleagues published in this issue of the
Journal of Cardiac Surgery, a pig's heart was filled with silicone,
subjected to a CT scan and the images imported into special software to
display it as a virtual 3D model and measure the relevant anatomical
structures. What sounds a little strange at first glance makes perfect
sense for a few reasons: Cardiac imaging has become an important part of
procedure planning in cardiovascular medicine, especially for structural
heart but also coronary artery disease\textsuperscript{1,2}. Image
modalities that play a crucial role in this context are cardiac computed
tomography (CCT)\textsuperscript{3} ,
echocardiography\textsuperscript{4--6}and cardiac magnetic resonance
imaging (CMRI)\textsuperscript{7}. Each modality has its own advantages
and disadvantages. While CCT has a very high spatial resolution,
echocardiography and CMR can be used to perform functional examinations
of the heart. To take advantage of different modalities simultaneously,
fusion of imaging modalities has been proposed, e.g. fusion of
echocardiography and fluoroscopy for mitral
interventions\textsuperscript{8}or fusion of invasive coronary
angiography and myocardial perfusion imaging\textsuperscript{9}and
others. With the increasing computing power of computers, the use of
medical images is now reaching new possibilities: The available data is
used to segment cardiac structures or even whole
hearts\textsuperscript{10--12}. These geometric models can then be used
for further applications, such as simulating cardiac
interventions\textsuperscript{13--15}or 3D printing to better understand
complex anatomies. The latter became popular before interventions
involving congenital heart defects\textsuperscript{16}, as these are
highly individualised from patient to patient. Using a 3D-printed model,
surgical planning can be done ''hands-on''. But the use of 3D models can
also be very helpful in adult cardiac surgery, e.g. mitral valve repair,
where the anatomy on the arrested heart can be very complex to
assess\textsuperscript{17}. In addition to sophisticated modelling of
the anatomical structures of the heart and therapy simulation, a third
application of the use of high-quality cardiac images can be seen in
education and training. Here, too, virtual visualisations of
three-dimensional images, but also 3D-printed models can be used. For
example, training ventricular septal defect repair on a silicone model
of a heart is described as a useful method for
training\textsuperscript{18}. In adult cardiac procedures, 3D-printed
models can help in planning access, for example in surgical minimally
invasive aortic valve replacement\textsuperscript{19} or to avoid
complications in transcatheter aortic valve
implantation\textsuperscript{20}. The very recent trend is to integrate
3D models into virtual reality applications, as described in the paper
by Kenichi and colleagues presented in this issue. Advantages of such
tools are the possibilities to interact with 3D images with little
effort. Models can be rotated in all directions, zoomed in and out and
measurements can be taken. In addition, 3D models can be used in
augmented reality scenarios, e.g. for decision support in complex
surgical procedures\textsuperscript{21}.

Despite numerous achievements in this field, there are still some
limitations to this advanced technology: First of all, the availability
of such tools is limited. Cost can play a role. However, another
important factor is the ''unfriendly'' user interface of many
applications and the complicated integration into the clinical
environment. Development is sometimes done without sufficient
communication with the clinician, who is the end user. However, this
problem arises on both sides of the street. Clinicians often have
limited interest in new tools that could help facilitate procedures in
the future, but are not yet finally developed. The applications are at
an early stage and still have many flaws and drawbacks. Very few are
available as ready-to-use tools. Paradoxically, much input from
clinicians would be important on the way to a ready-to-use solution, but
this is often lacking. Another fact that could lead to mistrust of the
new tools is the very small number of patients that are often included
in studies to validate the tools. A very important feature of
applications used in a medical context is of course the accuracy of the
models and measurements. In many studies, the validation of the models
is not convincing precisely because of the small number of patients and
images that are used to develop and validate the models which may be
enough to produce nice pictures and prove the feasibility of certain
applications or when the model is used for training purposes. However,
as soon as it becomes clinically relevant, the accuracy of the models
must be close to one hundred percent. The method described in this paper
is an interesting concept for an alternative method to validate the
accuracy of a particular tool, in this case virtual reality modelling.
The model was compared with other standard measures like 3D-MPR
assessment and with manual measurements made possible by using the
silicon cast of the original anatomy. Unfortunately, only the
feasibility on one model was shown and the measurements were not
repeated with multiple hearts or with multiple investigators performing
the measurements. However, such a method has the potential to be useful
for validation purposes, as the silicon model is naturally more
available and consistent than patient data. The development of methods
that do not require patient involvement and produce the same results is
highly desirable. A big step has already been taken with the advent of
machine learning algorithms used in cardiovascular modelling. Several
entities are currently being studied, for example, aortic aneurysm.
Liang et al. used datasets from twenty-five patients to sample 729
representative aortic shapes from all shape distributions described by a
statistical shape model. Rupture risk was calculated using finite
element analysis and the backward displacement
method\textsuperscript{22}. They could show that their approach is much
faster than other methods and had a high risk classification accuracy.
Currently, modelling heart structures and simulating therapies is
already possible. Many concepts are on the way and developing rapidly.
To be convincing and relevant in daily clinical routine, comprehensible
validation to prove accuracy is essential. Otherwise, it is dangerous
for clinicians to rely on virtual models. The path is already paved, but
currently cardiac modelling, simulation and virtual reality are still in
development and need to be considered as additional tools for planning
and decision-making in cardiac interventions. In the near future, the
incredible opportunities presented by such techniques are likely to play
a major role in cardiovascular care. Small steps and studies like the
one by Kenichi and colleagues are very important to continue on this
path, so that virtual reality arrives in medicine and is not reserved
for video game lovers.

\subsubsection*{}

{\label{section}}

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{\label{literature}}

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