



























J Global Clinical Engineering Vol.6 Special Issue 6: 2024 4

Editor’s Corner

Biomedical Technology and Clinical Engineering in Greece 
after the Pandemic: Highlighted Works from the Panhellenic 
Conference of Biomedical Technology

Aris Dermitzakis1,2,*, Vasiliki Zilidou1,3, Eleftheria Vellidou1,4, Alkinoos Athanasiou1,3

1 Hellenic Society of Biomedical Technology (ELEVIT), Athens, Greece.
2 Institute of Biomedical Technology (INBIT), Patras Science Park, Rio, Patras, Greece.
3 Lab of Medical Physics & Digital Innovation, School of Medicine, Faculty of Health Sciences, Aristotle University of Thessaloniki (AUTH), 
Thessaloniki, Greece.
4 Laboratory of Biomedical Engineering, School of Electrical and Computer Engineering, National Technical University of Athens (NTUA), 
Athens, Greece.

* Corresponding Author Email: Dermitzakis@inbit.gr

Statement: All papers submitted to the ELEVIT 10th conference were subjected to a peer review. The review was conducted by 
experienced and qualified members of the profession. Of the accepted papers the reviewers selected the best papers and recom-
mended them to be published. The Conference was held in Thessaloniki, Greece from 6 to 8 October 2023. The Editor-in-Chief of 
the Global CE Journal communicated with the Guest Editors and participated in the selected paper review.

ABSTRACT
The period of COVID-19 dominated the biomedical and clinical engineering workflows, as researchers and front-line health 

practitioners raced against time to offer solutions to the disruption caused to global healthcare. The Hellenic Society of Biomedi-
cal Technology reacted to the challenge in accordance with European and global biomedical and clinical engineering societies, 
waging the information battle and engaging with the public and the research community. Nonetheless, as the globe was slowly 
returning to its usual pre-pandemic practices, biomedical technology also entered a transition period, evolving through the chal-
lenges of the pandemic and started resembling a sort of scientific normality. In that environment, this special issue constitutes a 
selection of works that were presented during the last two Panhellenic Conferences of Biomedical Technology. The articles were 
due to their scientific interest and excellence, but also to portray the transition of the biomedical audience’s research interests 
from the pandemic to their more usual endeavors, albeit with the lingering influence of what transpired and how the biomedical 
and clinical engineering community reacted both globally and in Greece.

Keywords—Biomedical technology, Clinical engineering, COVID-19 impact, Digital health transformation, COVID-19 
transition.

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.

http://www.globalce.org
http://globalce.org
http://globalce.org
mailto:Dermitzakis@inbit.gr
https://creativecommons.org/licenses/by/4.0/
https://creativecommons.org/licenses/by/4.0/


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

INTRODUCTION

The period of the COVID-19 pandemic, from 2020 up 
to 2023, disrupted societies worldwide including every 
aspect of everyday life.1,2 Moreover, COVID dominated 
the biomedical and clinical engineering workflows, as 
researchers and front-line health practitioners raced 
against time to offer solutions to the disruption caused 
by global healthcare.3 The pandemic of COVID-19 has 
caused over seven million confirmed deaths as of August 
20244, leaving forever its mark on societies around the 
globe but also on the scientific communities that waged 
the battle for discovery, prevention, containment, and 
eventually treatment.5 More specifically, it altered in both 
positive and negative ways6 that the current generation of 
biomedical and clinical engineers are thinking with regard 
to communicable diseases, the urgency of research7, and 
the importance of the fields as a whole.8

The Hellenic Society of Biomedical Technology (ELEVIT) 
reacted to the challenge in accordance with European and 
global biomedical and clinical engineering societies, wag-
ing the information battle and engaging with the public 
and the research community through webinars and other 
events.9 During the last two conferences of Society, this 
swift was also evident both in the given directions by the 
organizers and in the submission of research works by 
the Greek biomedical community. Nonetheless, as the 
globe was slowly returning to its usual pre-pandemic 
practices, biomedical technology also entered a transition 
period, evolving through the challenges of the pandemic 
and started resembling a sort of scientific normality.10 In 
that environment, this special issue constitutes a selec-
tion of works that were presented during the Panhellenic 
Conference of Biomedical Technology in 2021, a hybrid 
event due to the ongoing pandemic, and mainly in 2023, 
which marked the return to normal face-to-face scientific 
events for ELEVIT. The articles included in this collection 
were invited among the submissions of the two confer-
ences due to their scientific interest and excellence, but 
also to portray the transition of the biomedical audience’s 
research interests from the pandemic to their more usual 
endeavors, albeit with the lingering influence of what 
transpired and how the biomedical and clinical engineer-
ing community reacted both globally and in Greece.

CONTRIBUTIONS’ OUTLINE

Education

The shift in medical education towards a student-centered 
approach has emphasized the importance of active learning 
and improving clinical reasoning skills over traditional 
passive learning and memorization.11 Dratsiou et al. 
have explored the integration of Virtual Patients (VPs) 
into the medical curriculum, which simulates real-life 
clinical scenarios, allowing students to practice safely and 
repeatedly, anytime and anywhere, with resources available 
for mobile use called Mobile Virtual Patients (MVPs).12 
MVPs were incorporated into the H2020 SHAPES project13 
with a focus on assisting healthcare professionals and 
medical students in improving their abilities to handle, 
identify, and address symptoms in older patients, as well 
as enhancing their clinical reasoning and decision-making 
abilities. The researchers aim to investigate the experiences 
of healthcare professionals and students experience with 
Problem-Based Learning (PBL) using MVPs, particularly 
in terms of usability, technology acceptance, and clinical 
reasoning. The research emphasizes the importance of 
customizing MVPs to address the unique requirements 
of different groups in order to enhance their educational 
impact and support clinical reasoning development.

The path of health services towards a digital and value-
based transformation is now a one-way street, with drastic 
and immediate effects that are capable of disrupting the 
sector and making it sustainable.14 The most defining 
issue is how an organization adapts its organizational 
culture, strategy, and leadership and mostly prepares the 
staff to operate effectively in a digital world, adding value 
to users and sustaining prosperity.15 This paper investi-
gates the perceptions of health professionals regarding 
the usability and ease of use of digital transformation 
applications. Healthcare professionals who worked in 
various hospitals and health providers in Northern Greece 
were invited to fill in the USE questionnaire in a paper-
less format. The acceptance of digital transformation 
in healthcare professionals is based on understanding 
the concerns and feelings of insecurity that overwhelm 
healthcare professionals. Results can help the community 
better understand the factors that influence the adoption 
of new digital technologies. Likely, this will help to reduce 

http://www.globalce.org
http://globalce.org
http://globalce.org


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

the time required to make all the structural changes that 
are necessary. As people accept change at different rates, 
there is no time for delay and their preparation should 
immediately begin to catch up with the post-COVID era.

Serious games (SG) incorporate learning and educa-
tional strategies commonly used in special education, 
and they have been proposed as assistive tools for people 
with developmental difficulties.16 ID-GAMING e-training 
toolkit encloses an SG named “Qool City” available as 
a board and digital game, a game catalogue, and train-
ing materials on cognitive functions and quality of life. 
This paper describes the methodology and preliminary 
outcomes of the toolkit’s validation actions. A four-step 
methodology was formed to specify the interaction of 
participants with the toolkit and a qualitative validation 
tool was developed to assess the participants’ performance 
during the session. The target groups were young adults 
and adults with intellectual disabilities (PwID), profes-
sionals, and relatives. The ID-GAMING e-training toolkit 
seems to lead to improvement in various cognitive func-
tions of PwID including memory, attention, language, and 
spatial orientation. Components of quality of life such as 
wellbeing and independence were also promoted. PwID 
remained engaged until the end of their interaction with 
the toolkit components while both PwID and educators 
were satisfied with the toolkit. SG’s vigorous validation 
is of high potential for various strands of biomedical 
engineering spanning from rehabilitation, and training 
all the way to adherence and quality of life strategies.17

Services and Devices

Job applicants’ skills evaluation has become increas-
ingly difficult for companies and individuals especially in 
the tech industry due to its constantly evolving nature. 
This difficulty takes its toll with decisions of negative 
impact. As a solution to this problem, Bamidis et al. 
utilized a machine learning-based model that can effec-
tively classify the software knowledge of developers, by 
recognizing the different technologies and programming 
languages implemented by them, thus assisting companies 
in managing their workforce based on acquired skills.18 
With previous work as their starting point, the authors 
implemented source code analysis by applying Natural 
Language Processing (NLP) techniques. The resulting 

model can be used as an effective tool for assessing the 
software knowledge of developers. The analysis helps 
obtain valuable insights into the effectiveness of neural 
networks and the benefits of transfer learning using 
pre-trained models. The potential for developing an as-
sessment tool for developers of all flavors is of high value 
in the very demanding field of biomedical engineering.

Managing medical device data accurately is essential 
for patient safety and regulatory compliance in healthcare 
systems. Liontou et al. introduce a novel approach combin-
ing web scraping and API integration to streamline the 
retrieval and validation of medical device information.19 
By leveraging Unique Device Identifiers (UDIs) and the 
Global Medical Device Nomenclature (GMDN), the proposed 
method enhances device authentication, categorization, 
and data accuracy.20 The research developed a code that 
integrates data from the AccessGUDID database with ad-
ditional information obtained through web scraping. This 
hybrid approach ensures comprehensive data coverage, 
addressing the challenges posed by unstructured and 
disparate data sources. The results showed a 74% success 
rate in accurately matching medical device records, dem-
onstrating the effectiveness of this system in improving 
data reliability. In the context of post-COVID healthcare, 
this study highlights the importance of advanced data 
management solutions in Greek biomedical engineering. 
By enabling more efficient device tracking and verifica-
tion, these technologies support safer and more compliant 
healthcare environments, ultimately enhancing decision-
making processes for medical professionals.

Neural Rehabilitation

Neurological diseases such as Cerebral Palsy, Parkin-
son’s Disease, and Spinal Cord Injury greatly affect move-
ment, balance, and posture.21 Robot-assisted therapies 
have been created in the last few years to improve hand 
functionality for people with certain diseases, especially 
those that impact daily activities.22 Sarra et al. focus on 
the mathematical analysis of human hand kinematics and 
dynamics to improve rehabilitation devices. A wearable 
soft robotic glove prototype with pneumatic actuators 
and sensors was developed incorporating a jacket, glove, 
and a neurorehabilitation serious game application. This 
study introduces a kinematic hand model and analyzes the 

http://www.globalce.org
http://globalce.org
http://globalce.org


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

dynamic interactions. Personalized rehabilitation systems 
can be created by utilizing the Denavit-Hartenberg method 
to model robot-human interaction movements and forces. 

People who have suffered from Spinal Cord Injury or 
have had a stroke often face difficulties in mobility, which 
can make their functional rehabilitation more challenging.23 
The lack of visible muscle activation in movement loss cases 
is causing difficulties for rehabilitation practitioners who 
need to find a solution. The issue was investigated by Lys-
sas et al. through the development of a Machine Learning 
model designed to identify and classify Electromyography 
(EMG) signals generated from skeletal muscle activation. 
This model replicates the state machine of the human 
skeletal muscle, identifying three key states: no activation 
state, activation state, and muscle fatigue state.24 Three 
different machine learning models, including a Random 
Forest, a Support Vector Machine, and a Shallow Neural 
Network, were utilized and evaluated based on their speed 
and accuracy.25 The authors conclude that in order to train 
the model effectively, a comprehensive database of EMG 
signals is required, which would improve the accuracy 
and efficiency of rehabilitation procedures.

The absence of basic rules and set procedures for 
building databases for surface electromyography (sEMG) 
signals, is a major challenge necessary for diagnosing and 
analyzing neuromuscular disorders. sEMG, a non-invasive 
method for evaluating muscle activity, is extensively 
utilized in clinical settings but lacks standardized data-
bases for biometric comparison.26 This obstacle limits 
its practical use in rehabilitation research, particularly in 
clinical settings. Arvanitidis et al. create a dynamic, scal-
able, consistent, available, and partition-tolerant NoSQL 
database. The database includes normative sEMG values 
from a diverse participant pool, covering both healthy 
individuals and those with spinal cord injuries or stroke, 
and also takes into account factors like gender, age, and 
BMI.27 This scalable and flexible database seeks to improve 
accuracy in diagnosis and effectiveness in treatment in 
neurorehabilitation, providing researchers and clinicians 
with valuable tools for upcoming research.

Understanding the movement of humans is crucial, 

especially in cases of injury or illness, to develop success-
ful rehabilitation techniques. Margaritis et al. explore 
this topic by providing a thorough analysis of lower limb 
kinematics and dynamics, while also outlining a plan for 
implementing a wearable device to assist in rehabilita-
tion. Their main focus is on people who have tetraplegia 
or paraplegia to model the lower limbs using rigid links 
connected by joints with specific degrees of freedom and 
range of motion. The movement capacities of the hip, 
knee, and ankle joints are examined using the Rigid Body 
Segment Model Approach and the Denavit-Hartenberg 
convention to establish kinematic chains.28 The matri-
ces of these joints assist in determining the location and 
alignment of the end-effector, crucial for both forward 
and inverse kinematics. The study distinguishes between 
Geometric and Analytical Jacobian matrices, utilizing 
the former to convert joint velocities to Cartesian space 
velocities. Dynamic equations, based on the Lagrangian 
method, elucidate the connection between motion and 
force, facilitating the analysis of intricate systems. This 
comprehensive approach aims to improve rehabilitation 
strategies through precise modeling and analyzing lower 
limb movements.

Functional Electrical Stimulation (FES) is widely used 
in neurorehabilitation to aid recovery in patients with 
neurological conditions like stroke and spinal cord injury 
(SCI).29 By synchronizing electrical pulses with natural 
movements, FES enhances neuromuscular adaptation 
and brain reorganization, leveraging neuroplasticity for 
improved functional recovery.30 Arsenidis et al. explored 
optimizing FES parameters using conventional and AI-based 
techniques to maximize therapeutic outcomes. The authors 
developed an AI-driven system that adjusts stimulation 
parameters in real-time, considering individual patient 
responses to achieve optimal results. Preliminary in vivo 
experiments demonstrated the potential of these methods 
in reducing muscle fatigue and discomfort during therapy, 
paving the way for more effective rehabilitation protocols. 
This research underscores the role of innovative tech-
nologies in refining rehabilitation practices, particularly 
in the post-COVID era, where personalized, data-driven 
approaches are essential for enhancing patient outcomes 
in Greek biomedical engineering.

http://www.globalce.org
http://globalce.org
http://globalce.org


J Global Clinical Engineering Vol.6 Special Issue 6: 2024 8

Novel Applications

Magnetoencephalogram (MEG) and consequently 
analysis of the images it produces31 is at the forefront 
of research when it comes to implementing automated 
methods for the detection, diagnosis, and prediction of 
epileptic activity32, with epilepsy being one of the most 
common neurological disorders with tens of millions of 
patients globally suffering from seizures. Such methods 
should help reduce human errors by specialized person-
nel while analyzing MEG images. Advanced models have 
been previously published for Electroencephalogram 
(EEG), however, the number of publications addressing 
MEG classification is scarce. Stylianou et al. tested, com-
pared, and evaluated some basic models to build a solid 
understanding of the characteristics of the available data 
and gain insights into the model’s behavior. The results 
corroborated the power of MEG as a diagnostic tool for 
epilepsy as even less sophisticated models performed 
well. In the post-COVID era, it is important to demonstrate 
results that will eventually find their way into everyday 
neurology clinic settings in Greece and beyond.

Astrocytes play a significant role in the pathogenesis 
of multiple sclerosis (MS), a chronic neurodegenerative 
disease affecting millions globally.33 These glial cells 
are crucial for maintaining neural homeostasis but also 
contribute to the disease by influencing inflammation 
and neuronal repair.34 Tsimperi et al. employed biophysi-
cally realistic models to simulate astrocytes’ impact on 
MS, focusing on axonal conduction and sodium channel 
facilitation in demyelinated axons. By examining astro-
cyte morphology and its effects on cellular functions, 
the research highlighted the dynamic roles of astrocytes 
in both physiological and pathological conditions, offer-
ing valuable insights into their complex behavior. The 
results underscore the potential for advanced model-
ing techniques to deepen our understanding of MS and 
guide future therapeutic developments.35 In the context 
of post-COVID Greek biomedical engineering, this study 
emphasizes the importance of innovative computational 
tools in enhancing our ability to explore complex biologi-
cal processes and improve the management of chronic 
neurological conditions. 

Accurate patient categorization is of vital importance, 
especially for those who suffer from cardiovascular diseases 

(CVDs), the leading cause of death worldwide. In the 
particular case of aortic valve stenosis (AS), the primary 
method used for categorizing, i.e., assessing, the severity 
of AS is the non-invasive echocardiography.36 Therefore, in 
recent years, there has been an increased demand for more 
effective assessment of individuals with AS and a deeper 
understanding of the flow field along the aortic valve.37 In 
this context, Makropoulos et al. discuss the construction of 
a computational fluid dynamics (CFD) model for simulat-
ing the flow along the aortic valve, utilizing real patient 
data. Moreover, a comprehensive analysis is conducted 
of the impact of aortic valve stenosis on the flow field. In 
this manner, this research aims to analyze the flow along 
the aortic valve for various constriction configurations, 
thereby enhancing our understanding of the phenomenon 
and facilitating future investigations in the quest for an 
additional index that will serve as a supportive tool in 
patient categorization. Greek multidisciplinary research 
groups have focused on the challenging topics of our era, 
producing important results. 

Skin conditions, from benign issues to severe malig-
nancies like melanoma, pose a significant challenge in 
dermatology. Moraitopoulos et al. present the DermaSense 
device, utilizing Electrical Impedance Spectroscopy (EIS), 
which is a novel diagnostic tool designed to enhance the 
accuracy of skin condition assessments.38 This mobile, 
cost-efficient device aims to differentiate between healthy 
and pathological skin through non-invasive impedance 
measurements, improving dermatological diagnostic 
decisions. The study tested the third prototype of Der-
maSense in both lab and clinical settings, showing its 
capability to distinguish skin conditions like actinic 
keratosis from healthy tissue. The results confirmed the 
device’s precision, especially when using stainless steel 
electrodes, providing reliable data that supports clinical 
decisions. Future enhancements will incorporate machine 
learning for refined data categorization, further boosting 
diagnostic performance. This represents a significant 
advancement in Greek biomedical engineering, offering 
a more precise, accessible tool for dermatologists. Its 
development underscores the importance of integrat-
ing innovative technologies into healthcare to improve 
patient outcomes and streamline diagnostic processes. 39

http://www.globalce.org
http://globalce.org
http://globalce.org


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

CONCLUDING REMARKS

Fourteen articles in total were included in this collec-
tion spanning a wide spectrum of biomedical and clinical 
engineering topics, ranging from education (e-training 
toolkits, software skills identification, virtual patients 
for digital problem-based learning), to services and de-
vices management (digital health management services, 
medical device management), to rehabilitation (wearable 
robotics, kinematics, normative data, functional electrical 
stimulation) and novel applications (electrical impendence 
spectrography, vessel flow modeling, astroglial dynam-
ics, magnetoencephalography for epilepsy). The variety 
of topics underlie the dynamic of Greek biomedical and 
clinical engineering communities and the perseverance 
of research and development directions through and after 
the COVID-19 pandemic. In the same spirit, we invite the 
readers to explore these dynamics, the topics, and the 
specific works included and hopefully to get inspiration 
for their own work and research endeavors.

CONFLICTS OF INTEREST

The authors declare that the research was conducted in 
the absence of any commercial or financial relationships 
that could be construed as a potential conflict of interest.

REFERENCES

1. Consilium. What is the EU doing in response to the 
COVID-19 coronavirus pandemic. Available online: 
https://www.consilium.europa.eu/en/policies/
coronavirus-pandemic/.

2. Mockaitis, A.I., Butler, C.L., Ojo, A. COVID-19 pan-
demic disruptions to working lives: A multilevel 
examination of impacts across career stages. J Vocat 
Behav. 2022;138:103768. https://doi.org/10.1016/j.
jvb.2022.103768.

3. COVID-19 has caused major disruptions and backlogs 
in health care, new WHO study finds. Available online: 
https://www.who.int/europe/news/item/20-07-
2022-covid-19-has-caused-major-disruptions-and-
backlogs-in-health-care--new-who-study-finds.

4. Mathieu, E., Ritchie, H., Rodés-Guirao, L., et al. Coronavirus 
Pandemic (COVID-19). Our World Data. Available online: 
https://ourworldindata.org/coronavirus#introduction.

5. Park, S., Lim, H.J., Park, J., et al. Impact of COVID-19 
Pandemic on Biomedical Publications and Their Cita-
tion Frequency. J Korean Med Sci. 2022;37(40):e296. 
https://doi.org/10.3346/jkms.2022.37.e296.

6. Izquierdo-Useros, N., Marin Lopez, M.A., Monguió-
Tortajada, M., et al. Impact of COVID-19 lockdown in 
a biomedical research campus: A gender perspective 
analysis. Front Psychol. 2022;13:906072. https://doi.
org/10.3389/fpsyg.2022.906072.

7. Cook, N.L. and Lauer, M.S. Biomedical Research COVID-19 
Impact Assessment: Lessons Learned and Compel-
ling Needs. NAM Perspect. 2021:10.31478/202107e. 
https://doi.org/10.31478/202107e.

8. Bahl, S., Iyengar, K.P., Bagha, A.K. Bioengineering 
Technology in Context of COVID-19 Pandemic: Po-
tential Roles and Applications. J Ind Integr Manag. 
2021;06(02):193–207. https://doi.org/10.1142/
S2424862221500056.

9. Dermitzakis, A., Bamidis, P.D., Athanasiou, A., et al. In-
formation Battle Against Covid19: Public Engagement 
through Biomedical Technology Webinars. Glob Clin 
Eng J. 4(SI4):67. Available online: https://globalce.
org/index.php/GlobalCE/issue/view/16/16.

10. Ellaway, R.H. The quest for normality. Adv Health Sci 
Educ Theory Pract. 2022;27(3):573–576. https://doi.
org/10.1007/s10459-022-10147-1.

11. Dafli, E., Fountoukidis, I., Hatzisevastou-Loukidou, C., 
et al. Curricular integration of virtual patients: a unify-
ing perspective of medical teachers and students. BMC 
Med Educ. 2019;19(1):416. https://doi.org/10.1186/
s12909-019-1849-7.

12. Heinrichs, L., Dev, P., Davies, D. Virtual environments 
and virtual patients in healthcare. In Healthcare Simula-
tion Education; Nestel, D., Kelly, M., Jolly, B., et al., eds. 
John Wiley & Sons: Hoboken, NJ, USA; 2017; pp. 69–79. 
https://doi.org/10.1002/9781119061656.ch10.

13. SHAPES H2020. Available online: https://shapes2020.
eu/.

http://www.globalce.org
http://globalce.org
http://globalce.org
https://www.consilium.europa.eu/en/policies/coronavirus-pandemic/
https://www.consilium.europa.eu/en/policies/coronavirus-pandemic/
https://doi.org/10.1016/j.jvb.2022.103768
https://doi.org/10.1016/j.jvb.2022.103768
https://www.who.int/europe/news/item/20-07-2022-covid-19-has-caused-major-disruptions-and-backlogs-in-health-care--new-who-study-finds
https://www.who.int/europe/news/item/20-07-2022-covid-19-has-caused-major-disruptions-and-backlogs-in-health-care--new-who-study-finds
https://www.who.int/europe/news/item/20-07-2022-covid-19-has-caused-major-disruptions-and-backlogs-in-health-care--new-who-study-finds
https://ourworldindata.org/coronavirus#introduction
https://doi.org/10.3346/jkms.2022.37.e296
https://doi.org/10.3389/fpsyg.2022.906072
https://doi.org/10.3389/fpsyg.2022.906072
https://doi.org/10.31478/202107e
https://doi.org/10.1142/S2424862221500056
https://doi.org/10.1142/S2424862221500056
https://globalce.org/index.php/GlobalCE/issue/view/16/16
https://globalce.org/index.php/GlobalCE/issue/view/16/16
https://doi.org/10.1007/s10459-022-10147-1
https://doi.org/10.1007/s10459-022-10147-1
https://doi.org/10.1186/s12909-019-1849-7
https://doi.org/10.1186/s12909-019-1849-7
https://doi.org/10.1002/9781119061656.ch10
https://shapes2020.eu/
https://shapes2020.eu/


J Global Clinical Engineering Vol.6 Special Issue 6: 2024 10

14. Walsh, M.N. and Rumsfeld, J.S. Leading the Digital 
Transformation of Healthcare. J Am Coll Cardiol. 
2017;70(21):2719–2722. https://doi.org/10.1016/j.
jacc.2017.10.020.

15. Mirković, V., Lukić, J., Lazarević, S., et al. Key Charac-
teristics of Organizational Structure that Supports 
Digital Transformation. In 2019: Proceedings of the 
24th International Scientific Conference Strategic 
Management and Decision Support Systems in Strate-
gic Management, Subotica, Serbia, 2019. https://doi.
org/10.46541/978-86-7233-380-0_46.

16. Serret, S., Hun, S., Iakimova, G., et al. Facing the chal-
lenge of teaching emotions to individuals with low- and 
high-functioning autism using a new Serious game: 
a pilot study. Mol Autism. 2014;5(1):37. https://doi.
org/10.1186/2040-2392-5-37.

17. Wouters, P., van Nimwegen, C., van Oostendorp, H., et al. 
A meta-analysis of the cognitive and motivational effects 
of serious games. J Educ Psychol. 2013;105(2):249–265.  
https://doi.org/10.1037/a0031311.

18. Kourtzanidis, S., Chatzigeorgiou, A., Ampatzoglou, A. 
RepoSkillMiner: identifying software expertise from 
GitHub repositories using natural language processing. 
In ings of the 35th IEEE/ACM International Confer-
ence on Automated Software Engineering, New York, 
NY, USA; Association for Computing Machinery: New 
York, NY, USA, 2021; pp. 1353–1357. https://doi.
org/10.1145/3324884.3415305.

19. Khder, M.A. Web Scraping or Web Crawling: State of 
Art, Techniques, Approaches and Application. Int J Adv 
Soft Comput Its Appl. 2021;13(3):145–168. https://
doi.org/10.15849/ijasca.211128.11.

20. Health C for D and R. FDA. Global Unique Device 
Identification Database (GUDID). FDA; 2023. Avail-
able online: https://www.fda.gov/medical-devices/
unique-device-identification-system-udi-system/
global-unique-device-identification-database-gudid.

21. Tulsky, D.S., Kisala, P.A., Victorson, D., et al. Overview of 
the Spinal Cord Injury–Quality of Life (SCI-QOL) measure-
ment system. J Spinal Cord Med. 2015;38(3):257–269. 
https://doi.org/10.1179/2045772315Y.0000000023.

22. Schmitt, F., Piccin, O., Barbé, L., et al. Soft Robots 
Manufacturing: A Review. Front Robot AI. 2018;5:84. 
https://doi.org/10.3389/frobt.2018.00084.

23. Athanasiou, A., Mitsopoulos, K., Praftsiotis, A., et al. 
Neurorehabilitation Through Synergistic Man-Machine 
Interfaces Promoting Dormant Neuroplasticity in Spinal 
Cord Injury: Protocol for a Nonrandomized Controlled 
Trial. JMIR Res Protoc. 2022;11(9):e41152. https://
doi.org/10.2196/41152.

24. Let, A.M., Filip, V., Let, D., et al. A Review in Biome-
chanics Modeling. In Proceedings of the International 
Conference of Mechatronics and Cyber–MixMechatron-
ics–2020; Gheorghe, G.I., eds; Cham: Springer Inter-
national Publishing; 2020. pp. 156–164. https://doi.
org/10.1007/978-3-030-53973-3_17.

25. Yousefi, J., and Hamilton-Wright, A. Characterizing 
EMG data using machine-learning tools. Comput 
Biol Med. 2014;51:1–13. https://doi.org/10.1016/j.
compbiomed.2014.04.018.

26. Luca C.J.D. The Use of Surface Electromyography in 
Biomechanics. J Appl Biomech. 1997;13(2):135–163. 
https://doi.org/10.1123/jab.13.2.135.

27. Mitsopoulos, K., Fiska, V., Tagaras, K., et al. NeuroSui-
tUp: System Architecture and Validation of a Motor 
Rehabilitation Wearable Robotics and Serious Game 
Platform. Sensors. 2023;23(6):3281. https://doi.
org/10.3390/s23063281.

28. Ziegler, J., Reiter, A., Gattringer, H., et al. Simultaneous 
identification of human body model parameters and 
gait trajectory from 3D motion capture data. Med Eng 
Phys. 2020;84:193–202. https://doi.org/10.1016/j.
medengphy.2020.08.009.

29. Karamian, B.A., Siegel, N., Nourie, B., et al. The role of 
electrical stimulation for rehabilitation and regeneration 
after spinal cord injury. J Orthop Traumatol. 2022;23(1):2. 
https://doi.org/10.1186/s10195-021-00623-6. 

30. Milosevic, M., Marquez-Chin, C., Masani, K., et al. Why 
brain-controlled neuroprosthetics matter: mechanisms 
underlying electrical stimulation of muscles and nerves 
in rehabilitation. Biomed Eng Online. 2020;19(1):81. 
https://doi.org/10.1186/s12938-020-00824-w.

http://www.globalce.org
http://globalce.org
http://globalce.org
https://doi.org/10.1016/j.jacc.2017.10.020
https://doi.org/10.1016/j.jacc.2017.10.020
 https://doi.org/10.46541/978-86-7233-380-0_46
 https://doi.org/10.46541/978-86-7233-380-0_46
https://doi.org/10.1186/2040-2392-5-37
https://doi.org/10.1186/2040-2392-5-37
https://doi.org/10.1037/a0031311
https://doi.org/10.1145/3324884.3415305
https://doi.org/10.1145/3324884.3415305
https://doi.org/10.15849/ijasca.211128.11
https://doi.org/10.15849/ijasca.211128.11
https://www.fda.gov/medical-devices/unique-device-identification-system-udi-system/global-unique-device-identification-database-gudid
https://www.fda.gov/medical-devices/unique-device-identification-system-udi-system/global-unique-device-identification-database-gudid
https://www.fda.gov/medical-devices/unique-device-identification-system-udi-system/global-unique-device-identification-database-gudid
https://doi.org/10.1179/2045772315Y.0000000023
https://doi.org/10.3389/frobt.2018.00084
https://doi.org/10.2196/41152
https://doi.org/10.2196/41152
https://doi.org/10.1007/978-3-030-53973-3_17
https://doi.org/10.1007/978-3-030-53973-3_17
https://doi.org/10.1016/j.compbiomed.2014.04.018
https://doi.org/10.1016/j.compbiomed.2014.04.018
https://doi.org/10.1123/jab.13.2.135
https://doi.org/10.3390/s23063281
https://doi.org/10.3390/s23063281
https://doi.org/10.1016/j.medengphy.2020.08.009
https://doi.org/10.1016/j.medengphy.2020.08.009
https://doi.org/10.1186/s10195-021-00623-6
https://doi.org/10.1186/s12938-020-00824-w


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

31. Styliadis, C., Ioannides, A.A., Bamidis, P.D., et al. Mapping 
the Spatiotemporal Evolution of Emotional Processing: 
An MEG Study Across Arousal and Valence Dimen-
sions. Front Hum Neurosci. 2018;12:322. https://doi.
org/10.3389/fnhum.2018.00322.

32. Geller, A.S., Teale, P., Kronberg, E., et al. Magnetoen-
cephalography for Epilepsy Presurgical Evaluation. 
Curr Neurol Neurosci Rep. 2024;24(2):35–46. https://
doi.org/10.1007/s11910-023-01328-5.

33. Ponath, G., Park, C., Pitt, D. The Role of Astrocytes in 
Multiple Sclerosis. Front Immunol. 2018;9:217. https://
doi.org/10.3389/fimmu.2018.00217.

34. Henstridge, C.M., Tzioras, M., Paolicelli, R.C. Glial Con-
tribution to Excitatory and Inhibitory Synapse Loss in 
Neurodegeneration. Front Cell Neurosci. 2019;13:63. 
https://doi.org/10.3389/fncel.2019.00063.

35. Savtchenko, L.P., Bard, L., Jensen, T.P., et al. Disen-
tangling astroglial physiology with a realistic cell 
model in silico. Nat Commun. 2018;9(1):3554. doi: 
10.1038/s41467-018-05896-w. Erratum in: Nat Com-
mun. 2019;10(1):5062. https://doi.org/10.1038/
s41467-019-12712-6.

36. Ring, L., Shah, B.N., Bhattacharyya, S., et al. Echocar-
diographic assessment of aortic stenosis: a practical 
guideline from the British Society of Echocardiogra-
phy. Echo Res Pract. 2021;8(1):G19–G59. https://doi.
org/10.1530/ERP-20-0035.

37. Yang. C.S., Marshall, E.S., Fanari, Z., et al. Discrepancies 
between direct catheter and echocardiography-based 
values in aortic stenosis. Catheter Cardiovasc Interv. 
2016;87(3):488–497. https://doi.org/10.1002/
ccd.26033.

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

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

http://www.globalce.org
http://globalce.org
http://globalce.org
https://doi.org/10.3389/fnhum.2018.00322
https://doi.org/10.3389/fnhum.2018.00322
https://doi.org/10.1007/s11910-023-01328-5
https://doi.org/10.1007/s11910-023-01328-5
https://doi.org/10.3389/fimmu.2018.00217
https://doi.org/10.3389/fimmu.2018.00217
https://doi.org/10.3389/fncel.2019.00063
https://doi.org/10.1038/s41467-019-12712-6
https://doi.org/10.1038/s41467-019-12712-6
https://doi.org/10.1530/ERP-20-0035
https://doi.org/10.1530/ERP-20-0035
https://doi.org/10.1002/ccd.26033
https://doi.org/10.1002/ccd.26033
https://doi.org/10.1016/j.jaad.2020.09.011
https://doi.org/10.1016/j.jaad.2020.09.011
https://doi.org/10.1111/jdv.15335

	Editor’s Corner
	Biomedical Technology and Clinical Engineering in Greece after the Pandemic: Highlighted Works from the Panhellenic Conference of Biomedical Technology
	Aris Dermitzakis1,2,*, Vasiliki Zilidou1,3, Eleftheria Vellidou1,4, Alkinoos Athanasiou1,3

	Digital Transformation Management in Health Services: Health Professionals Perceptions as an Implementation Factor
	Theodoros S. Tanis*, Chryssoula Chatzigeorgiou, Ioanna Simeli, and Evangelia Stalika

	Validating the ID-GAMING e-Training Toolkit for People with Intellectual Disabilities in Greece
	Niki Pandria*, Anastasia Barboudi, Vasileia Petronikolou, Panagiotis Antoniou and Panagiotis D. Bamidis

	Novel Functional Electrical Stimulation Parameter Optimization for Neurorehabilitation Using Both Conventional and AI Techniques 
	Arsenios Arsenidis1, Alexandros Moraitopoulos2, Alkinoos Athanasiou2, Alexandros Vildiridis3, Panagiotis Bamidis2, Petros Stefaneas4 and Alexandros Astaras5 

	Leveraging Web Scraping and API Integration for Efficient Medical Device Data Management 
	Agapi Konstantina Liontou1,*, Spilios Zisimopoulos2 and Aris Dermitzakis1 

	Human Muscle State Machine Using Electromyography Classification with Machine Learning 
	George Lyssas1,*, Konstantinos Mitsopoulos1, Dimitris Zantzas2, Anestis Kalfas2, Panagiotis D. Bamidis1 

	Kinematic and Dynamic Analysis of Lower Limb Movement: Towards the Design of a Wearable Rehabilitation Assistant Device 
	Filippos Margaritis1,*, Konstantinos Mitsopoulos1, Kostas Nizamis2, Alkinoos Athanasiou1 and Panagiotis D. Bamidis1 

	A Novel Dermatological Diagnosis Support Device Based on Electrical Impedance Spectroscopy 
	Alexandros Moraitopoulos1,*, Konstantinos Mitsopoulos1, Christina Kemanetzi2, Panagiotis Bamidis1 and Alexandros Astaras3

	Software Skills Identification: A Multi-Class Classification on Source Code Using Machine Learning
	Dimitris Bamidis, Ilias Kalouptsoglou, Apostolos Ampatzoglou, Alexandros Chatzigeorgiou*

	Improvement of Aortic Valve Stenosis Classification in Patients Through Computational Fluid Dynamics Model
	Ioannis Makropoulos, Dimitris Zantzas, Vasilis Gkoutzamanis, Anestis Kalfas*

	Kinematic and Dynamic Analysis of the Human Hand’s Articulation for Wearable Soft-Robotic Device Applications
	Paschalina-Danai Sarra, Vasiliki Fiska, Konstantinos Mitsopoulos, Diamanto Mylopoulou, and Panagiotis D. Bamidis*

	Deep Learning Classification of Epileptic Magnetoencephalogram 
	Andreas Stylianou1, Lefteris Koumakis2, Maria Hadjinicolaou3, Adam Adamopoulos1,* and Alkinoos Athanasiou4


