FM JANUARY 2019 - digital edition

adiology
NEXTGEN
RADIOLOGY
POWERED BY AI
Radiomics, which integrates AI into radiology, offers
great promise to accelerate precision medicine
DR RAJANI KANTH VANGALA
Radiomics, the application of
artificial intelligence (AI) to
radiology, may well be the trail
blazer that the rest of the specializations
in healthcare have been waiting for. In
November 2018 M*Modal announced
a cloud-based version of its radiology
reporting solution designed with the
help of Microsoft and Aligned Imaging
Solutions, a radiology company
focused on X-rays. In March 2018, GE
Healthcare introduced the LOGIQ E10,
its next-generation radiology ultrasound
technology. This digital system integrates
artificial intelligence, cloud connectivity
and advanced algorithms to gather and
reconstruct imaging data faster than
ever before.
The progress of radiology since
Wilhelm Roentgen’s discovery of X-rays
in 1895 can now be propelled into
the next century if we can use AI with
good practice guidelines and validated
biomarkers. Radiologists are not new
to the concept of AI, as there has been
pioneering work in this field since
1985 (Krupinski, Elizabeth A, Academic
Radiology, 2003), when several symbolic
interpretations of medical images
based on human decisions were used
for high-level assessments (Matsuyama
T, Comput Vision Graph, 1989). This
approach involved simple processes;
for example, binarising / thresholding
geometric structures in an image
and evolving a set of logical rules for
further diagnosis. This approach had
a strong human involvement as the
decision is taken based on human
medical knowledge. However, it did
not prove to be a successful decision
support system. The second approach
‘RADIOMICS’ IS A DATA-
DRIVEN APPROACH, WHERE
A SET OF CHARACTERISTIC
LABELED OR UNLABELLED
APPEARANCES OF ORGANS
ARE USED FOR TRAINING
of probabilistic interpretation of
medical images was driven by models
which used combinatorial systems.
This statistical approach depended
on human decision-making expertise
along with labeled parameters from the
reference data set using probabilistic
methods that are likely to determine
the best solutions. This approach has
numerous strengths, like aggregation of
information across populations, expert
knowledge and human-understandable
models. However, the
choices of the statistical
methods and the process of
building appropriate models which
successfully form a reference data-set
have become huge challenges.
Data-driven approach
The limitations of the above methods
lie in the requirement for expert human
knowledge. Moreover, converting
this into a model system can be
challenging, especially when the said
expertise/knowledge is incomplete.
‘Radiomics’ is a data-driven / modelfree
approach, where a set of
characteristic labeled (supervised) or
unlabelled (unsupervised) appearances/
representations of organs are used
28 / FUTURE MEDICINE / JANUARY 2019