Medical Design Briefs - August 2021 - 41

Inkjet Printing Shows Promise
for Making E-Textiles
Researchers have demonstrated
that they can print layers of electriInkjet
processing capacitor with
e-inks. (Credit: Inhwan Kim)
cally conductive ink on polyester
fabric to make an e-textile that
could be used in the design of
future wearable devices. Because
the printing method can be completed at room temperature
and in normal atmospheric conditions, researchers believe
inkjet printing could offer a simpler and more effective method
of manufacturing electronic textiles, also known as e-textiles.
The researchers used a FUJIFILM Dimatix inkjet printer to
create a durable and flexible e-textile material, what they did
to reliably create the e-textile, and its properties. Part of their
challenge was to find the right composition of materials so that
the liquid ink would not seep through the porous surface of
the textile materials and lose its ability to conduct electricity.
They created the e-textile by printing layers of electrically
conductive silver ink like a sandwich around layers of two liquid
materials, which acted as insulators. They printed those sandwich
layers on top of a woven polyester fabric. After they printed
the layers of silver ink and insulating materials - made of
urethane-acrylate, and poly(4-vinylphenol) - they monitored
the surface of the material using a microscope. They found that
the chemical properties of the insulating materials, as well as of
the textile yarns, were important to maintaining the ability of
the liquid silver ink to conduct electricity and to prevent it from
penetrating through the porous fabric.
For more information, visit www.medicaldesignbriefs.com/
roundup/0821/e-textiles.
An illustration of how the
diagnostic AI tool functions.
(Credit: NTU Singapore)
AI Tool Could Speed Up
Diagnosis of Cardiovascular
Diseases
A new tool could speed up the diagnosis
of cardiovascular diseases.
Powered by artificial intelligence (AI),
the innovation uses electrocardiograms
(ECGs) to diagnose coronary
artery disease, myocardial infarction,
and congestive heart failure to an
accuracy of more than 98.5 percent.
The researchers devised the diagnostic
tool by using an AI machine learning algorithm called
Gabor-Convolutional Neural Network (Gabor-CNN), which
mimics
the structure and function of the human brain,
enabling computers to learn from past experiences like a
human. Using the algorithm, they trained their tool to recognize
patterns in patients' ECGs by inputting examples of ECG
signals that reflect cardiovascular diseases.
The diagnostic tool is the first to use GaborCNN to allow for
ECG signals to be directly entered into the system for analysis
and could lead to advancements in the pursuit of merging AI
with medical solutions. The proposed system is equipped to be
validated with a bigger database and has the potential to aid clinicians
in screening for cardiovascular diseases using ECGs. In
a pilot study, the researchers used the tool to analyze ECG signals
from 92 healthy individuals, as well as seven patients with
coronary artery disease, 148 patients with myocardial infarction,
and 15 patients with congestive heart failure.
For more information, visit www.medicaldesignbriefs.com/
roundup/0821/AI.
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Medical Design Briefs - August 2021

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