Pennington Biomedical Contributes to Study Advancing Medical Imaging on Body Fat and Muscle Distribution – PR.com

Baton
Rouge,
LA,
February
06,
2025
–(PR.com)– A
recent
study
introduces
an
innovative
method
for
analyzing
body
composition
using
advanced
3D
imaging
and
deep
learning
techniques.
This
approach
aims
to
provide
more
accurate
assessments
of
body
fat
and
muscle
distribution,
which
are
crucial
for
understanding
health
risks
associated
with
various
conditions.

The
study,
“3D
Convolutional
Deep
Learning
for
Nonlinear
Estimation
of
Body
Composition
from
Whole
Body
Morphology,”
authored
by
researchers
from
Pennington
Biomedical
Research
Center,
University
of
Washington,
University
of
Hawaii
and
University
of
California-San
Francisco
was
recently
published
in
NPJ
Digital
Medicine,
a
journal
of
the
Nature
portfolio.

This
study
introduces
an
innovative
approach
utilizing
deep,
nonlinear
methods
to
enhance
the
estimation
of
body
composition
parameters,
surpassing
the
accuracy
of
previous
linear
models.
This
advancement
holds
potential
for
improving
assessments
in
clinical
settings
and
research
applications.

Authors
of
“3D
Convolutional
Deep
Learning
for
Nonlinear
Estimation
of
Body
Composition
from
Whole
Body
Morphology”
included
Pennington
Biomedical
Research
Center’s
Dr.
Steven
Heymsfield,
University
of
Washington’s
Dr.
Isaac
Tian,
Dr.
Jason
Liu
and
Dr.
Brian
Curless;
University
of
Hawaii’s
Dr.
Michael
Wong,
Nisa
Kelly,
Yong
Liu
and
Dr.
John
Shepherd;
and
University
of
California-San
Francisco’s
Dr.
Andrea
K.
Garber.

“Dr.
Steven
Heymsfield
has
extensive
experience
in
human
obesity,
energy
balance
regulation,
and
the
development
of
methods
for
evaluating
body
composition,”
said
Dr.
John
Kirwan,
Pennington
Biomedical
Executive
Director.
“His
contributions
to
this
field
have
been
pivotal
in
advancing
the
understanding
of
human
metabolism
and
the
application
of
new
technologies
such
as
3D
optical
imaging
in
medical
research.”

This
development
represents
a
step
forward
in
medical
imaging
and
health
assessment,
offering
a
more
reliable
tool
for
clinicians
and
researchers
to
evaluate
body
composition
and
associated
health
risks.

“To
easily
and
quickly
create
a
detailed
digital
map
of
a
person’s
body
shape
and
then
to
use
that
information
to
generate
not
only
accurate
estimates
of
their
body
composition
and
health
risks,
but
also
corresponding
3D
images,
was
almost
unimaginable
just
a
few
years
ago,”
said
Dr.
Heymsfield,
Professor
of
Metabolism
&
Body
Composition
at
Pennington
Biomedical.
“Technological
advances
like
this
require
skills
from
a
broad
range
of
scientists
and
I’m
pleased
to
have
the
opportunity
here
at
Pennington
Biomedical
to
work
with
colleagues
from
across
the
country
and
the
world.”

Key
Highlights
of
the
study
include:

-Advanced
Imaging:
The
researchers
utilized
3D
imaging
technology
to
capture
detailed
representations
of
the
body’s
shape.
-Deep
Learning
Application:
By
applying
sophisticated
deep
learning
algorithms,
the
study
achieved
more
precise
estimations
of
body
composition
compared
to
traditional
methods.
-Health
Implications:
Accurate
body
composition
analysis
is
essential
for
assessing
health
risks
related
to
obesity,
cardiovascular
diseases,
and
other
metabolic
disorders.

These
findings
are
results
from
the
Shape
Up!
Studies,
funded
through
A.K.G.:
NICHD
#R01HD082166,
National
Institutes
of
Health
NORC
Center
Grants
(P30DK072476,
Pennington/Louisiana
and
P30DK040561,
Harvard);
J.A.S.:
National
Institute
of
Diabetes
and
Digestive
and
Kidney
Diseases
(NIDDK)
(R01DK109008
and
R01DK111698).