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can be arranged accordingly to the given problem
that has to be solved:
1. Nodes: These are the basic computational
structures of the network and symbolize the
input, transfer, and output data. They should
work as biological neurons.
2. Links: The links, as the name suggests, represent the mathematical relations between two
or more nodes. These elements simulate biological synapse.
As in the biological brain, ANNs do not
change their initial structure, but change the values associated with the different links and nodes
that work as mathematical operators with which
the input data are treated. Indeed, ANNs and in
general AI algorithms vary from normal computational routines due to their evolution dynamics.
In other words, while in classic informatics algorithms are coded in order to perform a predetermined sequence of actions on the data, AI
algorithms are instead trained. Training a net-
work means presenting an input set of data, and
the elements of its structure change their internal
values in order to treat the data according to the
problem that has to be solved; this phenomenon
is also known as machine learning (ML). An
example of a general training process of an AI
algorithm is represented in Fig.30.7. Training the
network, under an operational point of view,
means extracting the features from the data that
have to be analyzed. These features can vary, but
in general they can be described as the characteristics that unite different groups of data for classication and correlation purposes.
Due to their intrinsic proprieties, neuromorphic EDIs present several advantages with respect
to the classic devices. First of all, measurement
errors that are due to human factor are depleted:
Neuromorphic EDIs, if well trained, are able to
analyze images and distinguish automatically the
desired objects. For this reason, the segmentation
of wounds in the images taken by the device
reaches the highest precision possible. Moreover,
these devices are able to perform other analysis
Fig. 30.7 A “normal” training procedure of an AI algorithm. At rst, the network is designed and coded. Then, a
vector of examples (i.e., a group of initial data) is presented to the network as input. Then, the network evolves:
its nodes and its links change their internal values in order
to sort and analyze the data as required by the developer.
Afterward, there is execution of the algorithm, where its
computational efciency is tested. If the results from the
execution do not meet the required standards, other training sessions are performed until the optimal efciency is
reached
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on the data, for instance, as they are capable to
distinguish and segment a wound; they are also
able to distinguish the different tissues that compose the wound bed and so able to perform a
clinical classication of the lesion with respect to
validated standards such as the WBP score. As all
means of measurement though, these devices are
still affected by two types of measurement errors:
1. Instrumental Error: These devices suffer as
all other EDIs suffer from the instrumental
errors given by the measurement components
that they implement (e.g., any distance sensor
that is used to dimension the pixels that depict
the wound through the distance of the device
from the lesion’s plane).
2. Interface Error: This error occurs during the
training procedure of the algorithm. Its computational efciency (i.e., its actual capability
to correctly analyze the data, such as an
image) is given by the quality and the amount
of data used for the training of its algorithms.
If in such data there are mistakes, or it is lacking intrinsic information, the algorithm will
erroneously learn the mistake or be imprecise
in its outputs.
One of the newest devices that has been
recently clinically validated and is entering the
good clinical practice in wound assessment is the
Wound Viewer (WV), developed by Omnidermal
Biomedics (Italy) [32]. The WV is a skin wound
assessment tool designed to measure and collect
data regarding patients and their wounds. The
device is noninvasive, does not come into contact
with applied and accessible parts, and can be
used for both ward and home visits. The device
and the related software are to be considered as
an adjunct tool for wound care management that
is not intended for diagnostic purposes. This
device was designed to run the proprietary articial intelligence algorithm for wound measurement and assessment. The device is equipped
with a 5MP color CMOS camera sensor to
acquire high-resolution pictures, 16 highprecision IR distance sensors, and 4 white LEDs.
Users are supposed to control the device through
a dedicated front end through a capacitive touchscreen display (Fig.30.8).
The ulcer analysis algorithm implemented in
WV applies a discrete time cellular nonlinear
network (DT-CNN) computing architecture in
order to identify the wound, hence providing relevant measurements of its area, depth, and volume [33–35]. Those acquired measurements
include the wound area expressed in squared centimeters, the wound depth expressed in millimeters, and the wound granulation expressed
through the WBP score. DT-CNN is a parallel
computing paradigm, introduced by Chua and
Itoh [36], similar to articial neural networks for
processing any dimensional signals. As any other
bio-inspired neuromorphic algorithm, the
DT-CNN goes through a learning phase and an
inference phase. The cellular nonlinear network
in the WV algorithm, which processes a twodimensional color image, in the former phase is
provided with statistical information about the
tissue forming the wound bed through a color
analysis. Those statistics are extracted from the
training set by a digital segmentation of wound
areas in the images contained in the training set
(more than 1500 wound pictures). The resulting
statistical information takes the form of a mapping, hereafter named g (·), or in other words an
R3 function, between each of the 16,777,216 possible 24bit in the RGB color space [23].
The nonlinear processing units making up
DT-CNNs are often referred to as neurons or
cells. Those cells can be implemented, depending
on the underlying technology, as arbitrary independently computing units; this results in a very
fast parallel algorithm to run. When using a
DT-CNN to perform an analysis of an image, like
in the case of the WV algorithm, each cell of the
DC-CNN corresponds to a pixel of the digital
picture. Under a mathematical point of view, let I
be the two-dimensional RGB color image and O
the computed black and white image underlying
the wound area (both having dimensions W×H,
Θ (·) be the Heaviside function and N an even
integer number. By appropriately setting the
parameters θ and ρ, which are the cell’s and the
automata threshold levels, respectively, the image
Og
,,
,
()
θρ
01
01
≤≤ −
iH
jW .
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Fig. 30.8 WV device
shown in its (a) top and
(b) bottom view. The
gure highlights its
main elements such as
the type-C IR sensor, the
CMOS camera, the
white LEDs, and the
capacitive touchscreen
a
b
O can be computed by applying the formula in
Equation 30.1, where (i, j) are the coordinates of
a single pixel and I
code, while O
i, j
is the pixel RGB triplet
i, j
is the pixel of the binary output
image:
N
N
i
j
+
2
=
ΘΘ
ij
∑∑
h
=+=
w
121
I
()
ij
where
≤≤ −
As an example, the elaboration result of the
wound is given in Fig.30.9. The original image
in (Fig.30.9a) went rstly through a preprocessing phase and then presented to the trained
DT-CNN. Each automata part of the network,
using the statistical chromatic knowledge stored
(·). Then, the output O
in g
was computed by
i, j
counting the number of pixels in a given proximity (N+1) of the input element I
whose color
i, j
appeared enough times (more than θ) in the
wounds from the training set. The total number of
−
−
(30.1)
pixels veried to be characteristic of a wound
area is then compared to the threshold level ρ. If
this critical value is lower than the weighted
counted number of pixels, then the pixel O
is
i, j
reported to be part of a wound area (set to binary
true, white in (Fig.30.9b)), else it is rejected.
Regarding the wound classication, the algorithm takes solely into account the pixels that
were recognized as part of the wound (i.e., white
elements in Fig.30.9b). Once the whole surface
of the wound is recognized, the highlighted elements are analyzed regarding their color scheme
(RGB). The whole set of possibilities regarding
the color of pixels forming the wound has been
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Fig. 30.9 (a) Example of a wound image before being
analyzed by the WV device’s algorithm. (b) Resulting
binary image where it is possible to note that the edges of
classied into four macrogroups: red, white,
black, and yellow. The wound images in the
training set have been classied through the
WBP score (in granulation) and then matched
taking into account the presence of the four
macrogroups in the wound area. Through this
training phase, the algorithm is able to analyze
these color schemes and perform an automatic
classication.
the wound coincide with the borders of the binary mask
given by the discrete time cellular nonlinear network
(DT-CNN)
The study rationale parted from the fact that
skin ulcers are treated with continuous and periodic dressings that are carried out by the specialist in charge. During a normal a normal
assessment, the wound care specialist manually
or with the help of an EDI measures the extent of
the wound and applies a new dressing. As mentioned in Sect. 30.3, one of the main characteristics that are taken into account is the differences
in the morphology of the lesion between two different assessments. Another variable that is usu-
30.3.3 Clinical andEconomical
Advantages Resulting
fromtheUse
ofNeuromorphicEDIs
ally taken into account is the wound bed and its
composition, and a classication through a standard clinical scale (as the WBP) is performed.
The standard EDIs can result in being time-
consuming and less intuitive since there is no
In this Section neuromorphic EDIs have been
described with respect to the previous generation
of the same device class, describing its advantages in terms of measurement precision and
classication capabilities. In particular, the WV
device has been taken as an example for two reasons: rst to provide a better understanding of the
analysis that these kinds of devices perform in a
wound assessment session and second because it
underwent a clinical trial that proved its measurement efciency as well as its ability to be integrated in the common good clinical practice
described in [23].
process of automatic analysis of the wound, on
top of the fact that the operator actively partici-
pates in the wound measurement increasing the
risk of errors. The purpose of the trial was there-
fore to verify the ease of use of WV in a normal
wound assessment session, comparing its results
with the other three EDIs. Regarding the wound
bed, its correct classication is taken into account
as a critical variable since it can be taken as a
guide for therapeutic decisions. Before neuro-
morphic EDIs, such as WV, wound classication
was the only prerogative of the specialists, and
depending on the degree of its clinical experi-
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ence, this can result in being subjective and difcult to standardize. WV and this class of devices
aim to overcome the subjectivity inherent in the
system through automatic evaluation through its
AI algorithms. In details, the aim of the trial was
to verify that the WV is able to automatically
identify the wound in the image and perform a
correct measurement and classication of the
lesion, suitable for providing high standards of
cure.
The clinical trial has been performed at the
Department of General Surgery at the Azienda
Ospedaliera Universitaria San Luigi Gonzaga
(Orbassano, Italy—protocol number OC15194).
It has been conducted on 150 patients divided
into three cohorts of 50 patients each according
to the type of wound: lower limb ulcer, diabetic
foot ulcer, and pressure ulcer. Once the patients
were enrolled in the study and gave their informed
consent to participate in the trial, their wound
were measured with WV and with other three
classic EDIs, whose measurement capabilities
are universally known as precise. At the same
time, the operator classied the wound through
the WBP score in granulation and then compared
its classication with the one returned automatically with WV and the one that resulted from the
classication of the wound bed tissues made by
one of the another three EDIs.
The measurement distribution of the patient
population was compared through inferential statistical analysis in order to verify the similarity of
the results obtained from all the devices used in
the trial. Through a Kruskal–Wallis one-way
ANOVA analysis, a p ‐value = 0.9 proving that
the distributions were in fact the same and therefore the automatic measurements of the WV could
be considered precise (Fig.30.10a). In addition in
all the cases the WV device was able to correcly
classify the assesd wounds with respect to the
visual assessment of the physician performing the
assessment and differently form the compared
EDI whose classication capability proved to be
unsatisfactory for clinical standards (Fig.30.10b).
The WV has proven in the years to be effectively integrable in the everyday clinical practice,
not only inward, but also in telemedicine procedures. In many places of the world, and most
importantly, after the COVID-19 pandemic, the
capability to monitor a patient while at home
instead of hospitalizing him has become more
and more crucial. Telemedicine in wound care
though presents different issues. First of all, the
caregivers (i.e., physicians and nurses) that treat
a
Fig. 30.10 (a) Distribution and comparison of the mor-
phological measurements performed by WV with respect
to the other EDIs. (b) Comparison of the wound classi-
b
cations (using the WBP in granulation) performed by the
WV with ones performed by the physician and the
employed EDI for this particular scope
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the single patient may vary over time leading to
inconsistencies in the therapeutic plans, in the
prescriptions, and in the clinical assessments.
These inconsistencies, a part from the fact that
they can worsen the quality of life of the patients,
consequently lead to a reduction in the costeffectiveness of the various cures. The lack of a
standardized mean of wound assessment and the
incapability of the hospitals to centrally monitor
the situation of their patients has become one of
the major pains. In a hospital in Italy, the WV was
uptaken for this specic reason and the data
regarding the cost of cures between the year
before the use of the technology and the following were compared (Fig.30.11). It must be noted
that the total cost is divided into three major
expenditures that are the medication costs (i.e.,
the cost of the prescribed dressings, the visit costs
(i.e., the cost of the single specialist traveling
through the territory to perform a medication),
and the cost for the therapeutic plan (i.e., the
administrative costs for medical prescription).
Surprisingly, thanks to the use of WV and its
capability transmit, the information among the
operators directly into the patient’s electronic
medical record (EMR), the wound care specialists were able to administer the right therapy to
the single patients according to their general clin-
ical state. Moreover, it was possible to render
efciently the general operations regarding the
patient management, concentrating the operators
on the patients that required greater treatment
and attention, lowering the number of visits to the
ones that were going through a correct healing
process. From these logical actions taken by the
hospital through the use of his neuromorphic
EDI, the total cost reduction for the hospital (with
a population of around 850 patients per year) has
reduced by 9%, while the cost of cure per single
patient has reduced by 14%.
30.4 Conclusions
Telemedicine is currently at the forefront of integrative technology with the goal of improving
clinical care while reducing costs in all medical
elds, such as wound care. In this chapter, an
excursus of the most employed technologies in
wound care has been presented and described.
Many of the devices and techniques that have
been reported were not available until ten years,
others even less. These last years, under a technological development point of view, have proven
to be the most interesting in this eld, thanks to
the effort of many researchers, clinicians, and
companies that work with the sole goal to provide devices for accurate wound assessment. But
the work is not yet done: Many more advancements in the eld of imaging and measurement in
wound care will come in the future years. This is
mostly due to the fact the COVID-19 pandemic
has shown the world that these kinds of devices
can have a great positive impact on clinical practice. The last class of devices presented is the
neuromorphic EDIs: These represent the most
recent advancement in this particular eld, and
with the example given by the WV device, their
efcacy is clearly proven.
Fig. 30.11 Reduction in the cost of cure (total and per
single patient) in telemedical procedures regarding
patients with chronic wounds. The values reported in the
gure are in Euros [=C]
Acknowledgements The author would like to thank the
whole editorial board of this book. A special thanks is
reserved Dr. Elia Ricci for this opportunity and his everlasting mentorship and cooperation in the author’s
research work.
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Wound Measurement
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ValentinaDini andGiammarcoGranieri
31
31.1 Introduction
Wound measurement is important in monitoring
the healing process of chronic wounds and in
evaluating the effect of treatment. Several tools
are available to support the caregivers in absence
of consensus on which tools should be used to
implement wound care. The tools should be easy
to use by practitioners; at the same time, the measurements acquired should be accurate and reliable and user friendly. The research is increasingly
focusing on methods that allow wound analysis
and provide reproducible quantitative indications
through devices that integrate multiple imaging
methods leading to multiparametric analysis of
the lesions. Instrument miniaturization and portability are a key point in wound imaging research.
These new techniques have many potential major
advantages such as: standardization of wound
measurements, objective data to evaluate therapeutic procedures and protocols, and sequential
comparisons.
31.2 Clinical Wound Assessment
In the context of a holistic approach, the assessment of wound bed, wound edge, and perilesional
skin is fundamental in wound management. The
V. Dini (*) · G. Granieri
University of Pisa, Pisa, Italy
triangle of wound assessment has been introduced to develop an integrated assessment tool
that focus on these three aspects of wound to
enhance patient outcomes, improving early identication of patients at risk of ulcer development
and a proper and prompt treatment [1]. Wound
bed, wound edge, and perilesional skin could be
considered as three axes of a triangle, each with a
specic importance in wound assessment. The
evaluation of wound bed includes the assessment
of the type of tissue and exudate, the signs of
infection or inammation, leading to the granulation tissue promotion. At the wound edge, the
main objective is to reduce the barrier to healing
identifying rolled, thickened, undermining, macerated, or dehydrated edges. The periwound area
damage contributes to delayed wound healing
and can cause pain reducing the quality of life of
patient. The assessment of perilesional skin highlights the presence of maceration, excoriation,
dry skin, hyperkeratosis, and eczema. The framework can be used to guide health professionals
when evaluating a wound, setting management
goals, and selecting treatment options. It has
been showed how a Spanish hospital introduced
the framework in the diabetic foot ulcer care
pathway used in the region [2]. The triangle of
wound assessment is a new tool that improves the
concept of wound bed preparation [3] and time
beyond the wound edges. The wound assessment
charts are easy to use and useful to collect all relevant areas and is based on recent anthropological
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2023
M. Maruccia et al. (eds.), Pearls and Pitfalls in Skin Ulcer Management,
https://doi.org/10.1007/978-3-031-45453-0_31
339
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V. Dini and G. Granieri
research [4] that has shown integration of the
periwound area within wound assessment is
important to the patient, the clinician, for healing
and for patient outcomes.
31.3 Wound Size Analysis
The measurement of wound area is one of the
most relevant prognostic indexes. It has been
demonstrated that a 40% reduction in ulcer area
after 4weeks is a good predictor of wound healing [5]. The accuracy, agreement, reliability, and
feasibility of wound assessment methods are a
central issue in wound management. The focus in
the last few decades has been on two-dimensional
(2D) methods to measure wound area, which can
be divided into contact methods (manual and
digital planimetry) and non-contact methods [6].
Three-dimensional (3D) methods for measuring
wound volume allowed to evaluate all dimensions including depth. A 3D approach provides a
more accurate evaluation of the biological
changes and thus results in more relevant data
than a simple two-dimensional approach.
However, none of the newer methods appear to
be used on a routine basis, possibly due in part to
the absence of valid comparative studies.
31.4 Wound Surface Area
Measurement
31.4.1 Two-Dimensional Contact
Methods
31.4.1.1 Simple Ruler Method
The simple ruler method is inexpensive and easy
to use, it consists of multiplying the greatest
length and width of the wound to obtain the surface area. Different studies demonstrated that the
traditional linear measurement has the least accuracy especially in irregular wounds. It has been
reported a 29–43% overestimation by the simple
ruler method compared with manual planimetry
[7]. Some authors found that the simple ruler
method overestimated the wound area by an average of 41% compared with digital planimetry [8].
The wound area can be calculated using standard
mathematical formulae assuming that most
wounds are spherical or elliptical. The most common approach is the elliptical method, in which
the area is calculated by multiplying 𝜋 (𝜋 = 3.14)
by the shortest and longest radii of the wound.
Kantor and Margolis found a strong positive correlation between area measurements using the
ellipse formula compared with digital planimetry
[9], but the correlation was lower for wounds
larger than 40 cm2. Other authors proposed an
area measurement using a new area formula
0.73×L×W (L=length, W=width) based on a
shape factor, which is an index of wound circularity between 0 and 1 (1 is a perfect circle). This
method was found to be more accurate than the
elliptical model when compared with digital planimetry [10].
31.4.2 Planimetric Measurement
Planimetric measurements can be manual or
electronic. In the manual method, a transparent
lm is placed on the lesions and the wound border is traced with a pen. The tracing is placed on
a metric grid and wound area is determined by
counting the number of squares in the grid covered by the traced area. In digital planimetry, the
margin of the wound is retraced onto a tablet
computer that performs the same calculations
[11]. Planimetric methods consider the body curvature and are relatively easy to learn, accurate,
and reliable. Digital planimetry is slightly more
accurate and reliable than manual planimetry.
Both methods involve contact with the wound
increasing the risk of infection.
31.4.3 Stereophotogrammetry
A stereographical camera linked to a computer
captures an image of the wound. The image is
downloaded to the computer, and the wound
perimeter is simply traced by moving the cursor
on the monitor. The computer software calculates
wound size and volume. This is an accurate, noncontact method, which reduces the risk of wound
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