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Application of an artificial neural network and morphing techniques in the redesign of dysplastic trochlea.
[trochlear dysplasia]
Segmentation
and
computer
assisted
design
tools
have
the
potential
to
test
the
validity
of
simulated
surgical
procedures
,
e
.
g
.
,
trochleoplasty
.
A
repeatable
measurement
method
for
three
dimensional
femur
models
that
enables
quantification
of
knee
parameters
of
the
distal
femur
is
presented
.
Fifteen
healthy
knees
are
analysed
using
the
method
to
provide
a
training
set
for
an
artificial
neural
network
.
The
aim
is
to
use
this
artificial
neural
network
for
the
prediction
of
parameter
values
that
describe
the
shape
of
a
normal
trochlear
groove
geometry
.
This
is
achieved
by
feeding
the
artificial
neural
network
with
the
unaffected
parameters
of
a
dysplastic
knee
.
Four
dysplastic
knees
(
Type
A
through
D
)
are
virtually
redesigned
by
way
of
morphing
the
groove
geometries
based
on
the
suggested
shape
from
the
artificial
neural
network
.
Each
of
the
four
resulting
shapes
is
analysed
and
compared
to
its
initial
dysplastic
shape
in
terms
of
three
anteroposterior
dimensions
:
lateral
,
central
and
medial
.
For
the
four
knees
the
trochlear
depth
is
increased
,
the
ventral
trochlear
prominence
reduced
and
the
sulcus
angle
corrected
to
within
published
normal
ranges
.
The
results
show
a
lateral
facet
elevation
inadequate
,
with
a
sulcus
deepening
or
a
depression
trochleoplasty
more
beneficial
to
correct
trochlear
dysplasia
.