=Paper=
{{Paper
|id=Vol-2608/paper6
|storemode=property
|title=Monitoring system for the tests of the Mg implants
|pdfUrl=https://ceur-ws.org/Vol-2608/paper6.pdf
|volume=Vol-2608
|authors=Galyna Tabunshchyk,Vadym Shalomeev,Peter Arras
|dblpUrl=https://dblp.org/rec/conf/cmis/TabunshchykSA20
}}
==Monitoring system for the tests of the Mg implants==
https://ceur-ws.org/Vol-2608/paper6.pdf
Monitoring System for Tests of the Mg Implants
Galyna Tabunshchyk1[0000-0003-1429-5180], Vadym Shalomeev1[0000-0002-6091-837X], Peter
Arras2[0000-0002-9625-9054]
1
National University “Zaporizhzhia Polytechnic”, Zaporizhzhia, Ukraine
2
KU Leuven, Campus De Nayer, Belgium
galina.tabunshchik@gmail.com, shalomeev@radiocom.net.ua,
peter.arras@kuleuven.be
Abstract –In the article authors are providing requirements analysis for the in-
formational systems for medical trials. The author considered types of the ex-
periments which could be conducted remotely with the Mg implants and de-
signed an experiment for the in vivo tests of the Mg stem. In the paper the au-
thors present three level architecture for the monitoring system the corrosion
and solubility of Mg implants and model for data integration and transmission.
Also suggested the model for the data integration and transition in to repository.
Keywords —critical systems, implants, monitoring, data analysis, hardware and
software systems, robust models.
1 Introduction
Implementation of IoT-technology in the research process allows users to collect,
process and visualise data. Especially in critical fields as for example the testing of
new materials for artificial implants, an IoT approach can present new opportunities
for speeding up and get more accurate data.
Internet of medical things (IoMT), a connected infrastructure of medical devices,
software applications and health systems and services, transforming greatly the sector
of medical trials improving the quality of data and its interoperability[1]. It enables
machine-to-machine interaction and real-time data streaming between an almost infi-
nite range of medical device and involve multiple participants, such as doctors and
patients, hospitals, and research institutions in multi.
New regulations, digitisation, data analytics, artificial intelligence, automation and
the development of value-based health care represent some of the numerous chal-
lenges as well as opportunities facing the medical technology industry [2]. And one
of the biggest challenge is interoperability, including complying with various national
and international standards and protocols around the exchange and use of data.
Real-time monitoring systems could be implemented as for in-vivo and for in-vitro
test systems and complement to the existing approaches in both stages. Within the
international Erasmus+ KA2 project BIOART (Innovative Multidisciplinary Curricu-
Copyright © 2020 for this paper by its authors. Use permitted under Creative
Commons License Attribution 4.0 International (CC BY 4.0).
lum in Artificial Implants for Bio-Engineering BSc/MSc Degrees [3] in NU
“Zaporizhzhia Polytechnic” are developed new Mg alloys for implants [4]. To test
these new materials it is required to create a new robust testing system, which could
provide researchers with prompt and reliable information on their material tests. For
development of the requested monitoring system there is required integration of the
biomedical data, material science data and IoT.
2 Investigation of the in-vivo and in-vitro real-time monitoring
approaches
Mg has a particular material property which makes it suitable for using as an implant
material: it is soluble in a watery environment. If the solubility rate of the material can
be controlled it opens up a range of possibilities in the implant-surgery as it would
avoid a second surgery for taking the temporary implants out again, making in more
comfortable for the patient, and giving an economic advantage in avoiding extra
medical costs.
The aim of the current research is to accurately determine the solubility rate of the
material and to match it with other retrieved data. The rate is measured by putting
stems of Mg in water and monitoring its state under pressure. After a predetermined
time the stems are taken out and their characteristics (weight and corrosion state) are
measured. The stems should dissolve in a controlled concrete time. The continuous
real-time measurement data of the this process should be collected at a server and
then used for decision making in in-vivo tests. In in-vivo test the state of the implant
is monitored by making X-rays or tomography. The base hypothesis is that the rate of
dissolving is the same in-vitro as in-vivo. By measuring and comparing a prediction
of the dissolving time will be possible.
The combination of a test-method(s) and appropriate techniques for assessing solu-
bility in-vitro and validating it in-vivo is the basis for the research. Underneath there
is an overview of the different state-of-the-art solubility tests and currently used
measuring techniques.
For the actual monitoring during the in-vitro test an EIS-technique is used for rea-
sons of efficient measuring and digitalizing of signals over a longer period of time.
In the work [5,5] there is provided an overview solubility test-methods for Mg al-
loys:
─ Short term immersion test (hydrogen evolution) in NHCO3/CO2 – buffered simu-
lated body fluid (SBF) at 37°C – up to 14 days.
─ Electrochemical impedance spectroscopy and immersion tests in DMEM supple-
mented by 10% (v/v) fetal bovine serum (FBS) pre-conditioned at 37°C up to 28
days.
─ Long-term immersion test (up to 365 days) in Dulbecco’s Modified Eagle Medium
(DMEM) supplemented with 10 vol.% fetal bovine serum buffered with 4-(2-
hydroxyethyl)-1-piperazineethanesulfonic acid (HEPES) at 37°C and refreshed pe-
riodically.
Assessment techniques for in-vivo corrosion:
─ SEM (Scanning Electron Microscopy) and WDS (Windows Deployment Services)
for the morphological imaging of retrieved implants and on-the-spot semi-
quantitative chemical composition analysis.
─ FTIR (Fourier-transform infrared spectroscopy (FTIR) is a technique used to ob-
tain an infrared spectrum of absorption or emission of a solid, liquid or gas. An
FTIR spectrometer simultaneously collects high-spectral-resolution data over a
wide spectral range. This confers a significant advantage over a dispersive spec-
trometer, which measures intensity over a narrow range of wavelengths at a time.
─ Histology: also known as microscopic anatomy or microanatomy, is the branch of
biology which studies the microscopic anatomy of biological tissues. Histology is
the microscopic counterpart to gross anatomy, which looks at larger structures
visible without a microscope.
─ USG: Medical ultrasound (also known as diagnostic sonography or ultrasonogra-
phy) is a diagnostic imaging technique.
─ X-ray radiography - non-invasive hard tissue imaging, produce 2D image of gross
tissue (combination of hard and soft tissue), image nearly all biomaterials, similar
to USG, qualitative analysis by visual observation of radiographs, quantitative
analysis by means of image processing software
─ µCT - Enhanced capabilities of X-ray radiography with possibility to generate a
complete 3D image, as a product of stacking discontinuous 2D slices, of an im-
plant and its surrounding tissue
─ MRI - non-invasive medical diagnostic tool for real-time soft and hard tissue im-
aging
Real-time in-vitro corrosion monitoring of absorbable metal implants [6]:
─ Electromechanical-based monitoring systems - quantitatively measure corrosion
rate and electrochemical properties of corroded surface in short-time (accelerated
process);
─ Electrochemical impedance spectroscopy (EIS): EIS is a highly sensitive charac-
terization technique used to establish the electrical response of chemical systems in
a nondestructive manner. EIS systems characterize the time response of chemical
systems using low amplitude alternating current (AC) voltages over a range of fre-
quencies. Using an electrode setup consisting of a working, reference, and counter
electrodes a known voltage is passed from the working electrode through an elec-
trolytic solution and into the counter electrode. Quantitative measurements are
produced by the EIS and enable the evaluation of small scale chemical mechanisms
at the electrode interface and within the electrolytic solution. Therefore, EIS is use-
ful in determining a wide range of dielectric and electrical properties of compo-
nents in research fields studying batteries, corrosion, etc.
─ Sensor-based monitoring systems - accurately measure certain corrosion parame-
ters using specific sensors, e.g., H2 sensor, Mg ion sensor, possible use for real-
time and continuous in-vivo corrosion monitoring as the sensor is connected to a
data acquisition device.
─ Micro-dialysis-based monitoring system: Micro dialysis is a minimally-invasive
sampling technique that is used for continuous measurement of free, unbound ana-
lyte concentrations in the extracellular fluid of virtually any tissue. Analytes may
include endogenous molecules (e.g. neurotransmitter, hormones, glucose, etc.) to
assess their biochemical functions in the body, or exogenous compounds (e.g.
pharmaceuticals) to determine their distribution within the body or fluid.
3 Design of the in vivo-experiment
Advances in microelectronics, in particular, such as miniaturization, computing
power increase per given area of silicon, reduced power consumption, increased effi-
ciency of radio frequency telecommunications and monolithic system-on-chip (SoC)
integration are leading to data acquisition equipment which is becoming increasingly
portable and unobtrusive, permitting uninterrupted real-time monitoring in a variety
of scenarios [7].
For the setup of the experiment (Fig.1) there was chosen an immersion test which
resembles body conditions. The Mg stems are put in a row in a plastic tube which is
pressurized with a saline solution by a pump. The saline solution is kept at a constant
temperature of 36.6°C to mimic body conditions.
The saline solution dissolves the Mg from the stem and as such will change its
electric properties. The impedance change of the solution is measured in real-time and
logged. After each test period a stem is removed and measured for weight and visual
(microscopic) inspection to assess the rate of dissolving. The other stems are kept in
the saline solution for the next test.
The pressure and flow of the saline will be controlled to assess the influence of the
different parameters. The H2 which is set free form the reaction Mg + H2O => MgO
+ H2 is collected in a gas reservoir to measure the dissolving rate.
The control system is managing the temperature of the saline, pump pressure, peri-
odic pictures of the stems, data from the sensors. It is developed on the Xilinx plat-
form [9], which makes its flexible for integration with the user application and verifi-
cation of the system functioning.
Web-interface is providing access to the experiments measurements in the real
time, allowing user to change the conditions of the experiments and manually control
measurements.
Fig. 1. Experimental setup for immersion test on Mg -stems
4 Monitoring system for Mg implants
A feature of modern monitoring is the extensive use of various instruments, sensors
and communication infrastructures capable of transmitting and processing data in
real-time. At the same time, a data collection system with low energy costs and the
ability to simultaneously service a large number of IoT devices is necessary for effi-
cient collection and processing of data on end nodes of information systems based on
IoT.
For the monitoring system we will consider a three layer architecture [10] which
contains device level, server level and application level (Fig.2).
At device level data is collected from different sources, but in general it could be
divided into two types: sensor-information and images. Both types can be collected at
in-vitro tests. For in-vivo tests mostly only imaging data (X-ray, tomography, scans..)
can be used, and in the case of surgical extraction of implants other data (loss of ma-
terial…) can be measured too.
At sever level, the technique to classify real-time data from several different
sources involves the following eight steps: data normalization; prediction future
points; analysis of residuals; probability calculation; conflicts definition; data fusion;
classification; estimation of classification accuracy.
At application/end user level tools are needed for the collection, the analysis, and
the representation of results of the experiments.
Device level Server level Application level
(Cloud level) (End users applications)
Data classification and Data collection/Stream Web and mobile applica-
aggregation publication/ tions for collection, analy-
Control system adjusting sis, and presentation re-
sults of the experiments
Fig. 2. Architecture of the monitoring system
Data which is received from sensors during the tests is also used for the manipula-
tion of the test control system.
Data resulting from in-vivo tests should be used later for the further in-vitro tests.
There should be possibility for continuous update of all the information and the
matching of the different types of data.
For the testing of the Mg implants a monitoring system is developed, which is de-
scribed in Fig.3. We are implementing a single model of combining all data which
makes it possible to develop a high quality system for supporting medical trial deci-
sions. In the system we integrate medical, biomedical, physical and clinical data .
Prediction model for implant corrosion
In-Vitro data In-Vivo Data
Experimental data and results Real time data for implant corrosion
Clinical data
Experiments data repository Clinical repository
Data Integration
Predictive Models Data-driving trials Diagnostic alerts
Fig. 3. Integration model for the trial data
Data integration model will be considered as a multidimensional cube:
Mdata = H(D, A), (1)
where D= {d1,d2,…, di} is set of dimensions (d1 – "set of material properties of the
sample", d2 – "set of trial conditions", d3 – "source of information", d4 – “time dimen-
sion”), А = Аd1 Аd2 … Аdi– set of attributes:
Adi a1i , a2i , ... agi , (2)
where i = 1, 2, 3 – set of attributes of dimension di.
Each cell of the hypercube of data corresponds to the only possible set of meas-
urement attributes containing unique experimental data for set of measurements in
concrete moment of time.
As developed test system is system with limited resourses we need periodical data
transfer to the cloud, and for the data transfer period from the testing system to the
cloud we will use [11]:
1
1 B C , (3)
S net
where ω – safety factor; B – volume of the modified data within specified period;
C –volume of the recorded data or ∆ period; τ – the predicted device operating time; σ
– predicted volume of the recording data; υnet – mean speed of data transmission S –
available volume.
For realisation of the interoperability repositories should comply following require-
ments:
(1) it should be a digital system able to manage N-dimensional hyper-cubes and
allow data analytics;
(2) it should be accessible and interoperable at the software level from the external
channels;
(4) it should have managing mechanism;
(5) the realisation of the data exchange should be organised regarding to the health
information technology standards[12] to have possibility for the further in-vivo moni-
toring.
The main regulation standards for file format are xml and json, for electronic
health information architecture is ISO 18308:2011 [14], for messaging of healthcare
data are HL7 v2.x [15], v3, FHIR [16], openEHR [13], for clinical documents
(Clinical Document Architecture – CDA), for medical imaging and communica-
tion (DICOM) [17], and for patient health summaries (Continuity of Care Re-
cord – CCR) [15].
5 Conclusion
In the paper authors suggest an architecture of the real-time monitoring system
which allows to check the state of the artificial implants based on Mg alloys.
Starting from a predictive model on the dissolving rate of the Mg in the human
body, data from in-vitro tests and in-vivo checks is combined to validate and update
the predictive model.
The architecture of the monitoring systems contains aggregation data from differ-
ent sources, processing, automated control of the testing equipment, and decision
support system for the researcher.
The setup for in-vitro experiments was constructed, which could be used as the re-
mote experiments for research and education purposes.
The aim is to have a robust and validated prediction for the dissolving of the Mg-
implants in the body.
6 Acknowledgement
This work is carried out partly with the support of Erasmus + KA2 project
586114-EPP-1-2017-1-ES-EPPKA2-CBHE-JP «Innovative Multidisciplinary Cur-
riculum in Artificial Implants for Bio-Engineering BSc/MSc Degrees» [BIOART].
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