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Conference Paper

A Novel Dermatological Diagnosis Support Device Based on 
Electrical Impedance Spectroscopy 

Alexandros Moraitopoulos1,*, Konstantinos Mitsopoulos1, Christina Kemanetzi2, Panagiotis Bamidis1 and Alexandros 
Astaras3

1 Lab of Medical Physics & Digital Innovation, AUTH, Greece. 
2 Department of Dermatology-Venereology, Papageorgiou General Hospital AUTH, Greece.
3 Robotics Laboratory, Computer Science, American College of Thessaloniki, Greece. 

* Corresponding Author Email: alexandrosmor@hotmail.com 

ABSTRACT

Our team has engineered a mobile and cost-efficient diagnostic tool that leverages Electrical Impedance Spectroscopy (EIS) 
technology to conduct differential assessment of the electrical impedance of skin tissue. Now in its third prototype iteration, the 
DermaSense apparatus performs non-invasive data collection from the epidermal layer, processes and analyzes the data, and 
serves as a support tool in dermatological diagnostic decisions. Device development focuses on an array of skin malignancies 
and relevant precursor conditions, such as actinic keratosis. Subsequent to rigorous evaluations in both controlled lab environ-
ments and clinical scenarios, our empirical data suggests that DermaSense holds promise in enhancing the precision of skin 
condition classification. Crucially, impedance measurements derived from individuals with certain pre-existing dermatological 
ailments appear to be distinguishable from those acquired from healthy patches of skin from the same subject, as well as those 
from other healthy subjects.

Keywords—Medical devices, Dermatology, Electrical impedance spectroscopy, EIS, Actinic keratosis, Melanoma, Biomedi-
cal engineering. 

Copyright © 2024. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY): Creative Commons - 
Attribution 4.0 International - CC BY 4.0. The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright 
owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduc-
tion is permitted which does not comply with these terms.

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69 J Global Clinical Engineering Vol.6 Special Issue 6: 2024

INTRODUCTION

Dermatological diseases represent a pervasive health 
challenge, impacting a substantial segment of the global 
population.1 This encompasses a spectrum of cutaneous 
pathological conditions, ranging from mild afflictions 
such as acne and eczema to more severe diseases such 
as actinic keratosis and melanoma, a variant of aggres-
sive cutaneous malignancy.2 The diagnostic approach 
in dermatology is based upon an array of procedures, 
including visual clinical assessment, surgical excision, 
and histopathological evaluation.3,4 This research delves 
into and introduces an EIS prototype scanner designed 
to augment the aforementioned conventional dermato-
logical methodologies using novel biomarkers, potentially 
enhancing the precision and specificity of dermatologi-
cal diagnoses, thereby facilitating prompt and effective 
therapeutic interventions.5

MATERIALS AND METHODS 

Materials 

Hardware

The prototype diagnostic system consists of a primary 
unit with a USB-2020 data acquisition mixed signal elec-
tronics board, a scanning head featuring nine spherical 
stainless steel electrodes (Figure 1), a signal generator 
to excite the skin and a microcontroller that acts as the 
central processing unit. A PC is utilized to run the control 
and visualization software. 

Software

Programming has been primarily carried out using 
the C++ language, which generates robust and efficient 
executables. In addition to C++, LabVIEW is employed 
to provide a user-friendly interface and facilitate the 
visualization of data.

Experimental Setup

Human skin impedance is modeled via an electrical 
circuit comprising a capacitor and resistors (Figure 2). 
Measurements employ Ohm’s law, using root mean square 
(RMS) values for alternating voltage and current. 

A comprehensive characterization of electrical imped-
ance can be achieved through an analogous electrical 
circuit model, as posited by.6 However, the inherent non-
linear and time-variant attributes of the skin’s electrical 
response necessitate a more intricate representation than 
a mere passive circuit. To address this, a circuit model 
encompassing a capacitor and two resistors in series has 
been proposed as an elementary yet effective framework 
for elucidating the intricacies of electrical impedance.7

To quantify the skin’s impedance, one can employ the 
renowned Ohm’s law, articulated as E = IR. For this com-
putation, it is imperative to utilize the root mean square FIGURE 1. The prototype scanning head comprising of nine 

spherical stainless steel electrodes encased by copper.

FIGURE 2. A simulation of the human impedance circuit simu-
lated in PSPICE software.

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J Global Clinical Engineering Vol.6 Special Issue 6: 2024 70

(RMS) values, especially when dealing with alternating 
current and voltage, to ensure accuracy. 

An illustrative experimental circuit, depicted in Figure 
2, serves as a testament to the empirical findings derived 
from the scientific literature. Within this configuration, 
the parallel arrangement of capacitor C2 and resistor 
R3 is designed to compensate for the capacitive effects 
intrinsic to the skin. Concurrently, the series resistor, 
R2, provides insights into the impedance characteristics 
of the subcutaneous tissue layers. Notably, Resistor R1, 
while not directly representing any skin property, plays 
a reference role in the data acquisition process, facilitat-
ing the measurement of aggregate current. Furthermore, 
the inclusion of C1, a coupling capacitor, is of paramount 
importance, ensuring the segregation of AC and DC signals, 
thereby preserving the circuit's equilibrium state amidst 
the introduction of alternating currents. 

Methods

Our objective was to assess the operability and ap-
plicability of the 3rd generation DermaSense prototype 
apparatus that we engineered. Initial trials were executed 
in a regulated laboratory environment, employing a tri-
electrode setup (comprising power supply electrodes and 
a data acquisition electrode). Three experimental sets 
were undertaken, with electrodes consistently positioned 
within an identical skin region on a participant’s forearm, 
modulating electrode distances from 150 mm to 450 
mm. To discern the influence of electrode categorization 
on the acquired signals, two discrete electrode variants, 
specifically adhesive ECG electrodes and spherical stain-
less steel electrodes, were utilized, and their resultant 
data were compared. 

Upon corroborating the operability of the prototype 
device, clinical measurements were procured from three 
male subjects, each suffering from various dermatological 
pathologies across diverse cutaneous areas of the skin. 
These assessments were orchestrated under the aegis of 
a dermatologist at the 2nd Department of Dermatology-
Venereology inside the Dermatological Clinic of Papageorgiou 
Hospital. Among the two electrode categories chosen for 
this investigation, adhesive ECG electrodes were deemed 

inappropriate due to their expansive contact surface area 
with the epidermis, obstructing the establishment of an 
electrode matrix conducive to comparative differential 
evaluations. 

RESULTS
In this study, two types of electrodes were evaluated. 

The adhesive ECG electrodes were deemed inappropri-
ate for the intended purpose. Their unsuitability arises 
from even the smallest ones having a significant skin 
contact surface area, which hinders the formation of an 
electrode array for comparative differential readings. Our 
experimental regimen subjected three healthy individu-
als to a consistent voltage (approximately 1.68 V) across 
escalating frequencies (spanning from 100 Hz to 14 kHz). 
Data retrieval outcomes were replicable and congruent 
with simulation findings, affirming the operability of the 
prototype apparatus. Initial clinical trials encompassed 
measurements from both healthy and pathological skin 
of three male subjects of varied ages. Each participant 
exhibited specific dermatological pathologies, as veri-
fied by a clinical evaluation executed by a dermatologist 
prior to data acquisition with the DermaSense prototype 
apparatus. 

The first patient, aged 71, presented multiple suspi-
cious lesions dispersed across facial regions and other 
cranial areas, with a singular lesion being quantifiable 
due to the restrictive geometric design of the prototype 
scanner. The subsequent patient, aged 50, presented with 
potential malignant lesions on the posterior aspect of his 
left foot sole; EIS measurements were procured using the 
prototype apparatus upon the dermatologist’s directive. 
The tertiary patient, aged 63, was diagnosed with pro-
nounced actinic keratosis on both forearms. 

Notably, the measurement locale of this patient’s skin 
was especially apt, aligning with the region employed in 
the preliminary validation trials, facilitating a robust com-
parison against an expansive dataset previously gathered. 
Remarkably, data derived from the trio of patients unveiled 
significant findings, particularly pertaining to the third  
patient (Figures 3 and 4), whose measurements exhib-
ited a pronounced deviation from the consistent pattern 
observed in the results of healthy participants (Figure 5).

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71 J Global Clinical Engineering Vol.6 Special Issue 6: 2024

The RMS voltage values of the measurements typi-
cally ranged from −1 standard deviation (STD) to +1 STD. 
However, for the third patient, the measurements deviated 
more significantly, spanning beyond ±2 STD. Additionally, 
the phase difference measurements were not distinct 
enough to draw any definitive conclusions or assumptions.  

DISCUSSION 
The newly developed DermaSense system holds 

promise for assisting non-invasive and accurate diag-
noses of various skin conditions, although it remains a 
work-in progress. To optimize the scanner’s functional-
ity, forthcoming iterations will feature modular heads, 
engineered to conform to the topographical intricacies of 

skin surfaces.8 Furthermore, the database will undergo 
augmentation to encompass a broader demographic, 

thereby enhancing the comprehensiveness and fidelity 
of the reference dataset. The integration of advanced 
machine learning algorithms is projected to fine-tune 
data categorization, thereby amplifying the system’s 
diagnostic precision and robustness.9,10 Progressive 
enhancements in scanner technology, data procurement 
methodologies, and artificial intelligence competencies 
are expected to perpetually refine the DermaSense ap-
paratus, priming it for standard clinical deployment.

CONCLUSION
The laboratory outcomes validate the prototype 

DermaSense device’s performance when using stainless 
steel electrodes compared to adhesive ECG electrodes, 
as indicated by the statistical analysis. Additionally, 
time series analyses showed minimal signal variations, 
implying stable data capture under changing conditions. 
Meanwhile, the clinical findings supported the device’s 

FIGURE 3. Measurements taken from the left forearms of a 
healthy male subject aged 27 (top) and a male patient aged 
63 presenting actinic keratosis (bottom).

FIGURE 4. Phase differences of measurements obtained from 
the left forearms of a healthy male subject aged 27 (top) and 
a male patient aged 71 presenting actinic keratosis (bottom).

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J Global Clinical Engineering Vol.6 Special Issue 6: 2024 72

effectiveness, as the impedance measurements from 
patients with dermatological issues significantly differed 
from those of healthy individuals. Furthermore, derma-
tologists confirmed the utility of the device in assisting 
with diagnostic decisions, particularly in complicated 
cases involving various skin conditions. 

In light of the research presented, the DermaSense 
system appears to be a promising support tool for tra-
ditional dermatological diagnostic methods. The third 
generation of this prototype device has demonstrated its 
capability to non-invasively and more accurately assess 
the electrical impedance of the epidermal layer, offering 

promising insights into the electrical characteristics of skin 
tissue. Our experimental findings, both from controlled 
laboratory settings and real-world clinical scenarios, 
underscore the device's improved efficacy, especially 
when utilizing stainless steel electrodes.  

The significant deviations in impedance measurements 
between patients with dermatological pathologies and 
healthy subjects further bolster the device’s potential 
to enhance the specificity and accuracy of skin condi-
tion classification. Moreover, the positive feedback from 
dermatologists accentuates the DermaSense apparatus’s 
potential role in aiding diagnostic decisions, especially 
in intricate cases with multiple skin conditions. 

In summary, the DermaSense apparatus, with its 
innovative use of EIS, stands poised to revolutionize 
dermatological diagnostics, offering a cost-effective, 
mobile, and precise tool that could potentially expedite 
and enhance therapeutic interventions for a myriad 
of skin pathologies. Future endeavors should focus on 
refining the device’s design for broader applicability 
and further validating its efficacy across a more diverse 
patient demographic.11

 REFERENCES

1. Sinikumpu, S.P., Jokelainen, J., Haarala, A.K., et al. The 
high prevalence of skin diseases in adults aged 70 
and older. J Am Geriatr Soc. 2020;68(11):2565–2571. 
https://doi.org/10.1111/jgs.16706.

2. Kelbore, A.G., Owiti, P., Reid, A.J., et al. Pattern of 
skin diseases in children attending a dermatology 
clinic in a referral hospital in Wolaita Sodo, southern 
Ethiopia. BMC Dermatol. 2019;19(1):5. https://doi.
org/10.1186/s12895-019-0085-5.

3. Blume-Peytavi, U., Bagot, M., Tennstedt, D., et al. 
Dermatology today and tomorrow: from symptom 
control to targeted therapy. J Eur Acad Dermatol Ve-
nereol. 2019;33(S1):3–36. https://doi.org/10.1111/
jdv.15335.

FIGURE 5. Measurements taken from the left forearm of a 
healthy male subject aged 30.

http://www.globalce.org
http://globalce.org
http://globalce.org
https://doi.org/10.1111/jgs.16706
https://doi.org/10.1186/s12895-019-0085-5
https://doi.org/10.1186/s12895-019-0085-5
https://doi.org/10.1111/jdv.15335
https://doi.org/10.1111/jdv.15335


73 J Global Clinical Engineering Vol.6 Special Issue 6: 2024

4. Piccolo, V. Update on dermoscopy and infectious skin 
diseases. Dermatol Pract Concept. 2019;10(1):e2020003. 
https://doi.org/10.5826/dpc.1001a03.

5. Litchman, G.H., Teplitz, R.W., Marson, J.W., et al. Im-
pact of electrical impedance spectroscopy on der-
matologists’ number needed to biopsy metric and 
biopsy decisions for pigmented skin lesions. J Am 
Acad Dermatol. 2021;85(4):976–979. https://doi.
org/10.1016/j.jaad.2020.09.011. 

6. Lackermeier, A., Pirke, A., McAdams, E.T., et al. Non-
linearity of the skin’s AC impedance. In Proceedings 
of 18th Annual International Conference of the IEEE 
Engineering in Medicine and Biology Society; IEEE 
Xplore: 1997; pp. 1945–1946. https://doi.org/10.1109/
IEMBS.1996.646332. 

7. Kukucka, M. and Krajcuskova, Z. The Frequency and 
the Shape of Driving Signal Influence in Measure-
ment of the Active Points. Adv. Electr. Electron. Eng. 
2012;10(3):181–186. https://doi.org/10.15598/
aeee.v10i3.641. 

8. Han, T., Kundu, S., Nag, A., et al. 3D Printed Sensors for 
Biomedical Applications: A Review. Sensors (Basel). 
2019;19(7):1706. https://doi.org/10.3390/s19071706. 

9. Du-Harpur, X., Watt, F.M., Luscombe, N.M., et al. What 
is AI? Applications of artificial intelligence to derma-
tology. Br J Dermatol. 2020;183(3):423–430. https://
doi.org/10.1111/bjd.18880.

10. Hogarty, D.T., Su, J.C., Phan, K., et al., Artificial intelligence 
in dermatology—where we are and the way to the fu-
ture: a review. Am J Clin Dermatol. 2020;21(1):41–47. 
https://doi.org/10.1007/s40257-019-00462-6.

11. Litchman, G.H., Marson, J.W., Svoboda, R.M., et al. 
Integrating electrical impedance spectroscopy into 
clinical decisions for pigmented skin lesions improves 
diagnostic accuracy: a multitiered study. SKIN J Cutan 
Med. 2020;4(5):424–430. https://doi.org/10.25251/
skin.4.5.5.

http://www.globalce.org
http://globalce.org
http://globalce.org
https://doi.org/10.5826/dpc.1001a03
https://doi.org/10.1016/j.jaad.2020.09.011
https://doi.org/10.1016/j.jaad.2020.09.011
https://doi.org/10.1109/IEMBS.1996.646332
https://doi.org/10.1109/IEMBS.1996.646332
https://doi.org/10.15598/aeee.v10i3.641
https://doi.org/10.15598/aeee.v10i3.641
https://doi.org/10.3390/s19071706
https://doi.org/10.1111/bjd.18880
https://doi.org/10.1111/bjd.18880
https://doi.org/10.1007/s40257-019-00462-6
https://doi.org/10.25251/skin.4.5.5
https://doi.org/10.25251/skin.4.5.5

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