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Alzheimer's disease is the main cause of
dementia worldwide.
Although there is no cure, early detection is considered crucial for
being able to develop effective treatments that act before
its progress is irreversible.
Mild cognitive impairment is a phase that
precedes the disease, but not everyone who suffers from it
ends up developing Alzheimer's. A study led by scientists at
the Universitat Oberta de Catalunya (UOC) and published in
the IEEE Journal of Biomedical and Health
Informatics, has succeeded in
precisely distinguishing
between people whose deterioration is
stable and those who will progress to having the
disease. The new technique, which uses
specific artificial intelligence methods to compare magnetic
resonance images, is more effective than the other methods
currently in use.
Fine-tuning the diagnosis
Alzheimer's disease affects more than 50
million people worldwide, and the ageing of
the population means that there may be many more sufferers in
the coming decades. Although it usually develops without any
symptoms over many years, it is generally preceded by what is
known as mild cognitive impairment, which is much milder than
the impairment presented by people with Alzheimer's, but more
severe than would be expected for someone of their age.
"These patients may progress and worsen or remain in the
same condition as time passes. That is why it is important to
distinguish between progressive and stable cognitive
impairment in order to prevent the rapid progression of the
disease," said Mona Ashtari-Majlan, a UOC researcher in
the AI for Human Wellbeing (AIWELL)
group, which is affiliated to the eHealth Center and the Faculty
of Computer Science, Multimedia and Telecommunications.
She is a student on the doctoral programme in Network and Information Technologies,
supervised by David Masip, and the
lead author of the article.
Identifying these cases correctly could help
to improve the quality of clinical trials used to test
treatments, which increasingly seek to target the initial
phases of the disease. To do so, the researchers used a
method involving a multi-stream
convolutional neural network, which
is a
technique based on artificial intelligence and deep learning
that is very useful for image recognition and classification.
"We first compared MRIs from patients
with Alzheimer's disease and healthy people to find distinct
landmarks," explained Ashtari-Majlan. After training the
system, they fine-tuned the proposed architecture with
resonance images from people who had already been diagnosed
with stable or progressive cognitive impairment with much
smaller differences. In total, almost 700 images from
publicly available datasets were used.
According to Ashtari-Majlan, the process
"overcomes the complexity of learning caused by the
subtle structural changes that occur between the two forms of
mild cognitive impairment, which are much smaller than those
between a normal brain and a brain affected by the disease.
Furthermore, the proposed method could address the small
sample size problem, where the number of MRIs for mild
cognitive impairment cases is lower than for Alzheimer's."
The new method enables the two forms of mild
cognitive impairment to be distinguished and classified with
an accuracy rate close to 85%. "The
evaluation criteria show that our proposed method outperforms
existing ones," she said, including more conventional
and other deep learning-based methods, even when they are
combined with biomarkers such as age and cognitive tests. In
addition, "we can share our implementation with anyone
wishing to reproduce the results and compare their methods
with ours. We believe that this method can help professionals
to expand the research," she concluded.
This research contributes to achieving
Sustainable Development Goal (SDG) 3, Ensure healthy lives and well-being
for all at all ages.
Reference article
M. Ashtari-Majlan, A. Seifi and M. M. Dehshib
(2022). "A multi-stream convolutional neural network for
classification of progressive MCI in Alzheimer's disease
using structural MRI images," in IEEE Journal of
Biomedical and Health Informatics, doi: 10.1109/JBHI.2022.3155705.
UOC R&I
The UOC's research and innovation (R&I) is
helping overcome pressing challenges faced by global
societies in the 21st century, by studying interactions between
technology and human & social sciences with
a specific focus on the network society, e-learning and e-health.
Over 500 researchers and 51 research groups work
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the Internet Interdisciplinary Institute (IN3) and
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as well as UOC
community entrepreneurship and knowledge transfer via
the Hubbik platform.
The United Nations' 2030 Agenda for
Sustainable Development and open knowledge serve
as strategic pillars for the UOC's teaching, research and
innovation. More information: research.uoc.edu #UOC25years
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