Assessing state of the art in AI for brain disease treatment
A review of artificial intelligence for understanding brain disease
reveals the most advanced algorithms available to clinicians

Date:
October 14, 2020
Source:
American Institute of Physics
Summary:
The range of AI technologies available for dealing with brain
disease is growing fast, and exciting new methods are being applied
to brain problems as computer scientists gain a deeper understanding
of the capabilities of advanced algorithms. Researchers conducted
a systematic literature review to understand the state of the art
in the use of AI for brain disease. Their qualitative review sheds
light on the most interesting corners of AI development.



FULL STORY ========================================================================== Artificial intelligence is lauded for its ability to solve problems
humans cannot, thanks to novel computing architectures that process
large amounts of complex data quickly. As a result, AI methods, such as
machine learning, computer vision, and neural networks, are applied to
some of the most difficult problems in science and society.


==========================================================================
One tough problem is the diagnosis, surgical treatment, and monitoring of
brain diseases. The range of AI technologies available for dealing with
brain disease is growing fast, and exciting new methods are being applied
to brain problems as computer scientists gain a deeper understanding of
the capabilities of advanced algorithms.

In a paper published this week in APL Bioengineering, by AIP Publishing, Italian researchers conducted a systematic literature review to understand
the state of the art in the use of AI for brain disease. Their search
yielded 2,696 results, and they narrowed their focus to the top 154 most
cited papers and took a closer look.

Their qualitative review sheds light on the most interesting corners
of AI development. For example, a generative adversarial network was
used to synthetically create an aged brain in order to see how disease
advances over time.

"The use of artificial intelligence techniques is gradually bringing
efficient theoretical solutions to a large number of real-world clinical problems related to the brain," author Alice Segato said. "Especially
in recent years, thanks to the accumulation of relevant data and the development of increasingly effective algorithms, it has been possible
to significantly increase the understanding of complex brain mechanisms."
The authors' analysis covers eight paradigms of brain care, examining AI methods used to process information about structure and connectivity characteristics of the brain and in assessing surgical candidacy,
identifying problem areas, predicting disease trajectory, and for intraoperative assistance. Image data used to study brain disease,
including 3D data, such as magnetic resonance imaging, diffusion tensor imaging, positron emission tomography, and computed tomography imaging,
can be analyzed using computer vision AI techniques.

But the authors urge caution, noting the importance of "explainable
algorithms" with paths to solutions that are clearly delineated, not a
"black box" -- the term for AI that reaches an accurate solution but
relies on inner workings that are little understood or invisible.

"If humans are to accept algorithmic prescriptions or diagnosis, they
need to trust them," Segato said. "Researchers' efforts are leading to
the creation of increasingly sophisticated and interpretable algorithms,
which could favor a more intensive use of 'intelligent' technologies in practical clinical contexts."

========================================================================== Story Source: Materials provided by American_Institute_of_Physics. Note: Content may be edited for style and length.


========================================================================== Journal Reference:
1. Alice Segato, Aldo Marzullo, Francesco Calimeri, Elena De Momi.

Artificial intelligence for brain diseases: A systematic review. APL
Bioengineering, 2020; 4 (4): 041503 DOI: 10.1063/5.0011697 ==========================================================================

Link to news story: https://www.sciencedaily.com/releases/2020/10/201014141102.htm

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