Rare Diseases Symptoms Automatic Extraction
Home
A random Abstract
Our Project
Our Team
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
.
Diseases
Validation
Diseases presenting
"e"
symptom
allergic bronchopulmonary aspergillosis
aromatase deficiency
cadasil
child syndrome
dracunculiasis
gm1 gangliosidosis
inclusion body myositis
kallmann syndrome
krabbe disease
neonatal adrenoleukodystrophy
pleomorphic liposarcoma
pyomyositis
trochlear dysplasia
wolf-hirschhorn syndrome
You can validate or delete this automatically detected symptom
Validate the Symptom
Delete the Symptom