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d
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C
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g
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in
c
re
a
se
d
c
o
st,
a
n
d
su
b
jec
ti
v
it
y
.
Artifi
c
ial
in
telli
g
e
n
c
e
(AI)
b
a
se
d
so
l
u
ti
o
n
i
s
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d
t
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h
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se
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d
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n
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s
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e
sp
it
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ts
p
o
ten
t
ial
c
o
n
tri
b
u
ti
o
n
ti
l
l
d
a
te.
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e
re
fo
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,
t
h
e
p
ro
p
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se
d
stu
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y
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re
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ste
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with
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n
d
n
o
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o
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m
e
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x
h
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b
it
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a
r
fa
c
ts.
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e
n
o
v
e
lt
y
o
f
th
is
wo
r
k
re
sid
e
s
in
i
ts
c
o
m
p
a
ra
ti
v
e
a
n
a
l
y
sis,
tem
p
o
ra
l
m
a
p
p
in
g
o
f
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tren
d
s
,
a
n
d
it
s
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ra
ti
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m
e
wo
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.
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a
lso
c
o
n
tri
b
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tes
to
wa
rd
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g
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n
d
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re
a
s,
th
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ti
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c
ti
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ia
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sig
h
t
t
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s
th
e
fu
tu
re
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irec
ti
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n
o
f
imp
le
m
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n
.
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y
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lev
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t
b
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a
ls
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ffe
rs su
sta
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n
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b
le
d
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n
o
stics
.
K
ey
w
o
r
d
s
:
Ar
tific
ial
in
tellig
en
ce
C
lin
ical
Dee
p
lear
n
in
g
Diag
n
o
s
tic
Neu
r
o
im
ag
in
g
P
e
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v
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CC B
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SA
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C
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p
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A
uth
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:
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b
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Srir
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Sap
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lleg
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in
ee
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in
g
a
f
f
iliated
to
Vis
v
esv
ar
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T
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h
n
o
lo
g
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Un
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s
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B
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g
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u
5
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Kar
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I
n
d
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am
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co
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1.
I
NT
RO
D
UCT
I
O
N
Per
v
asiv
e
d
ev
elo
p
m
en
t
d
is
o
r
d
er
(
PDD)
ca
n
b
e
ter
m
ed
as
a
s
p
ec
tr
u
m
o
f
n
e
u
r
o
d
ev
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p
m
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d
is
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th
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f
ea
tu
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ed
b
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eh
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co
m
m
u
n
icatio
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d
s
o
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tio
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also
in
v
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l
v
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m
u
ltip
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elate
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d
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g
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R
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h
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o
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d
is
in
teg
r
ativ
e
d
is
o
r
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C
DD)
,
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er
g
er
’
s
s
y
n
d
r
o
m
e,
au
tis
tic
d
is
o
r
d
er
.
At
p
r
esen
t,
th
e
r
e
ar
e
v
ar
io
u
s
s
cr
ee
n
i
n
g
tech
n
iq
u
e
s
th
at
is
d
iv
id
ed
in
t
o
v
a
r
io
u
s
class
es
a
s
ar
tific
ial
in
tellig
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c
e
(
AI
)
-
ass
is
ted
co
m
p
u
tatio
n
al
m
eth
o
d
,
clin
ical
(
b
eh
av
io
r
al)
,
an
d
n
eu
r
o
p
s
y
c
h
o
l
o
g
ical
m
eth
o
d
.
T
h
e
b
eh
av
io
r
al
an
d
clin
ical
s
cr
ee
n
in
g
to
o
l
in
v
o
lv
es
th
e
u
s
e
o
f
th
e
m
o
d
if
ied
c
h
ec
k
l
is
t
f
o
r
au
tis
m
in
to
d
d
ler
s
-
r
ev
is
ed
with
f
o
llo
w
-
u
p
(
M
-
C
HAT
-
R
/F),
au
tis
m
d
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n
o
s
tic
o
b
s
er
v
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n
s
ch
ed
u
le
,
2
n
d
ed
itio
n
(
ADOS
-
2
)
,
au
tis
m
d
iag
n
o
s
tic
in
ter
v
iew
-
r
ev
is
ed
(
ADI
-
R
)
,
ch
ild
h
o
o
d
d
iag
n
o
s
tic
in
ter
v
ie
w
-
r
ev
is
ed
(
C
DI
-
R
)
,
th
e
ch
ild
h
o
o
d
au
tis
m
r
atin
g
s
ca
l
e
(
C
AR
S
-
2
)
,
an
d
s
o
cial
co
m
m
u
n
icatio
n
q
u
esti
o
n
n
air
e
(
SC
Q)
[
1
]
.
T
h
e
n
eu
r
o
p
h
y
s
io
lo
g
ical
a
n
d
n
e
u
r
o
im
ag
in
g
a
p
p
r
o
ac
h
es
in
v
o
l
v
e
ey
e
-
tr
ac
k
i
n
g
an
d
g
az
e
an
aly
s
is
,
m
ag
n
etic
r
eso
n
an
ce
im
ag
i
n
g
(
MRI)
o
r
f
u
n
ctio
n
al
m
ag
n
etic
r
eso
n
a
n
ce
im
ag
in
g
(
f
MRI)
,
a
n
d
b
r
ain
ac
tiv
ity
p
atter
n
s
cr
ee
n
in
g
[
2
]
.
T
h
e
AI
-
b
ased
co
m
p
u
tatio
n
al
s
cr
ee
n
in
g
ap
p
r
o
ac
h
es
i
n
v
o
lv
es
m
ac
h
in
e
l
ea
r
n
in
g
(
ML
)
,
d
ee
p
Evaluation Warning : The document was created with Spire.PDF for Python.
I
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2
2
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8
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I
n
t J Ar
tif
I
n
tell
,
Vo
l.
1
5
,
No
.
4
,
Au
g
u
s
t 2
0
2
6
:
3
0
8
1
-
3
0
8
9
3082
lear
n
in
g
(
DL
)
,
a
n
d
h
y
b
r
id
m
o
d
els.
Fro
m
th
e
v
iewp
o
in
t
o
f
ef
f
icien
c
y
,
clin
ical
s
cr
ee
n
in
g
to
o
l
ar
e
h
ig
h
ly
r
eliab
le
b
u
t
th
ey
ar
e
s
u
b
jectiv
e
an
d
tim
e
-
in
te
n
s
iv
e
,
wh
ile
ac
cu
r
ac
y
is
s
tr
o
n
g
ly
d
ep
en
d
en
t
o
n
p
r
o
f
ess
io
n
al
ex
p
er
tis
e
.
AI
s
cr
ee
n
in
g
to
o
ls
also
o
f
f
er
h
ig
h
er
ac
c
u
r
ac
y
in
a
co
n
tr
o
lled
r
esear
ch
en
v
ir
o
n
m
en
t;
h
o
wev
er
,
th
e
y
h
av
e
lack
o
f
ex
p
lain
a
b
ilit
y
an
d
d
ata
b
ias
r
esu
ltin
g
in
r
estric
ted
clin
ical
tr
an
s
latio
n
.
Neu
r
o
p
h
y
s
io
lo
g
ical
to
o
ls
p
r
o
v
id
e
h
ig
h
l
y
o
b
jectiv
e
in
s
ig
h
t
b
u
t
t
h
ey
h
av
e
in
c
r
ea
s
ed
co
s
t
an
d
r
ed
u
ce
d
s
ca
lab
ilit
y
[
3
]
–
[
5
]
.
He
n
ce
,
th
e
s
h
o
r
tco
m
in
g
s
o
f
th
e
cu
r
r
en
t state
o
f
s
cr
ee
n
in
g
to
o
ls
ar
e
ex
p
l
ain
ab
ilit
y
is
s
u
es in
AI
m
o
d
els,
in
f
er
io
r
in
teg
r
atio
n
b
etwe
en
clin
ical
p
r
ac
tices
an
d
AI
,
d
iv
e
r
s
ity
g
ap
s
,
d
ata
s
ca
r
city
,
lim
ited
ea
r
ly
d
etec
tio
n
,
an
d
h
u
m
an
b
ias.
I
t
was
also
n
o
ted
th
at
th
e
a
d
o
p
t
io
n
o
f
AI
-
b
ased
s
o
lu
tio
n
s
is
in
cr
ea
s
in
g
in
its
p
ac
e
t
o
war
d
s
d
iag
n
o
s
is
o
f
PDD.
Scr
ee
n
in
g
p
latf
o
r
m
s
u
s
in
g
AI
m
o
d
els
is
k
n
o
wn
to
p
lay
a
r
o
le
o
f
ea
r
ly
s
cr
ee
n
in
g
an
d
d
etec
tio
n
,
f
ea
tu
r
e
ex
tr
ac
tio
n
an
d
p
atter
n
r
ec
o
g
n
itio
n
,
p
r
e
d
ictiv
e
d
iag
n
o
s
is
,
d
ec
is
io
n
s
u
p
p
o
r
t
f
o
r
clin
i
cian
s
,
p
er
s
o
n
alize
d
in
ter
v
en
tio
n
p
lan
n
in
g
.
I
n
co
n
tr
ast
to
co
n
v
en
tio
n
al
d
iag
n
o
s
is
m
eth
o
d
s
,
ML
c
o
n
tr
ib
u
te
s
to
war
d
s
lear
n
in
g
d
is
cr
im
in
ativ
e
f
ea
tu
r
es f
r
o
m
b
eh
av
io
r
al/clin
ical
d
ata.
DL
co
n
tr
ib
u
tes
to
war
d
s
th
e
au
to
m
atic
ex
tr
ac
tio
n
o
f
c
o
m
p
lex
r
ep
r
esen
tatio
n
o
f
clin
ic
al
d
ata
o
f
v
ar
io
u
s
f
o
r
m
s
.
I
t
is
also
s
ee
n
t
h
at
n
atu
r
al
la
n
g
u
a
g
e
p
r
o
ce
s
s
in
g
(
NL
P)
co
n
tr
ib
u
tes
to
war
d
s
an
aly
zin
g
lin
g
u
is
tic
p
atter
n
s
as
well
as
s
p
ee
ch
s
em
an
tics
f
r
o
m
v
ar
io
u
s
c
o
m
m
u
n
icatio
n
s
am
p
les
wh
ile
m
u
lti
m
o
d
al
f
u
s
io
n
m
o
d
els
o
f
f
er
in
teg
r
atio
n
o
f
b
e
h
av
io
r
a
l
f
ea
tu
r
es
with
m
u
ltime
d
ia
f
o
r
b
etter
p
r
o
f
ilin
g
s
co
p
e
[
6
]
.
Hen
ce
,
an
AI
-
b
ased
s
o
lu
tio
n
is
k
n
o
wn
to
o
f
f
e
r
in
cr
ea
s
ed
p
r
ed
ictiv
e
ac
cu
r
ac
y
a
lo
n
g
with
b
etter
s
ca
lab
ilit
y
an
d
au
to
m
atio
n
.
I
t
is
also
k
n
o
wn
to
ad
d
r
ess
is
s
u
es
p
er
tain
in
g
to
s
u
b
jectiv
e
b
ias
th
at
is
f
r
eq
u
en
tly
s
ee
n
in
cl
in
ician
-
r
ep
o
r
ted
o
r
p
ar
en
t
-
r
e
p
o
r
ted
s
ca
les.
Stu
d
ie
s
u
s
in
g
AI
m
o
d
els
ca
n
en
h
an
c
e
o
v
e
r
p
er
io
d
o
f
tim
e
with
in
c
r
ea
s
in
g
ex
p
o
s
u
r
e
to
u
n
s
ee
n
d
ata
,
wh
ile
v
is
io
n
-
b
a
s
ed
an
d
NL
P
-
b
ased
s
o
lu
tio
n
s
ar
e
k
n
o
wn
f
o
r
its
co
s
t
ef
f
ec
tiv
en
ess
.
AI
-
b
ased
d
iag
n
o
s
tic
m
o
d
els
en
co
u
n
ter
v
ar
io
u
s
co
n
s
tr
ain
ts
to
o
v
iz.
in
teg
r
atio
n
with
th
e
h
ea
lth
ca
r
e
s
y
s
tem
,
p
r
iv
ac
y
an
d
eth
ical
co
n
ce
r
n
,
clin
ical
v
ali
d
atio
n
g
ap
,
tr
a
n
s
p
ar
en
c
y
an
d
ex
p
lain
ab
ilit
y
,
im
b
ala
n
ce
an
d
d
ata
s
ca
r
city
.
I
n
a
n
u
ts
h
ell,
AI
ac
ts
as
p
r
o
ac
tiv
e
s
o
lu
tio
n
to
b
r
id
g
e
th
e
g
ap
b
etwe
en
co
m
p
u
tatio
n
a
l
in
tellig
en
ce
an
d
b
eh
av
io
r
al
s
cien
ce
to
o
f
f
er
ad
ap
tiv
e,
o
b
jectiv
e,
a
n
d
s
ca
lab
le
d
iag
n
o
s
tic
s
o
lu
tio
n
.
I
n
o
r
d
e
r
to
o
f
f
e
r
co
m
p
r
eh
e
n
s
iv
e
in
s
ig
h
ts
to
war
d
s
th
e
cu
r
r
en
t
f
o
r
m
s
o
f
d
iag
n
o
s
tics
,
it
i
s
ess
en
tial
to
u
n
d
er
s
tan
d
th
e
s
ca
le
o
f
ef
f
ec
tiv
en
ess
alo
n
g
with
t
h
eir
p
o
s
s
ib
le
co
n
s
tr
ain
ts
o
r
s
h
o
r
tco
m
in
g
s
ass
o
ciate
d
with
th
e
cu
r
r
en
t
m
eth
o
d
s
ass
o
ciate
d
with
d
iag
n
o
s
is
o
f
PDD.
Var
io
u
s
r
elate
d
wo
r
k
s
h
av
e
b
ee
n
s
tu
d
ie
d
in
th
is
r
e
g
ar
d
to
f
i
n
d
th
e
o
r
ien
tatio
n
o
f
d
if
f
er
e
n
t
m
eth
o
d
o
l
o
g
ies.
Hu
d
a
et
a
l.
[
7
]
h
av
e
p
r
esen
ted
d
is
cu
s
s
io
n
to
war
d
s
v
ar
ied
s
cr
ee
n
in
g
to
o
ls
ass
o
ciate
d
with
id
en
tific
atio
n
o
f
s
u
ch
d
is
o
r
d
er
s
.
Similar
d
ir
ec
tio
n
o
f
s
tu
d
y
with
s
p
ec
if
ic
em
p
h
asis
o
n
I
n
d
ian
p
o
p
u
latio
n
h
ig
h
lig
h
tin
g
in
d
ig
e
n
o
u
s
r
eso
u
r
ce
s
with
p
o
ten
tial
in
f
o
r
m
atio
n
h
as
b
ee
n
ca
r
r
ied
o
u
t
b
y
Sre
elak
s
h
m
i
et
a
l
.
[
8
]
.
Gk
in
to
n
i
et
a
l.
[
9
]
h
av
e
s
tu
d
i
ed
v
ar
io
u
s
DL
-
b
ased
ap
p
r
o
ac
h
es
to
war
d
s
p
r
ed
ictio
n
o
f
s
u
c
h
f
o
r
m
o
f
d
is
o
r
d
er
em
p
ir
ically
ev
alu
ated
o
n
m
u
ltip
le
s
tan
d
ar
d
d
atasets
.
T
h
e
wo
r
k
ca
r
r
ied
o
u
t
b
y
L
iu
a
n
d
Ma
[
1
0
]
h
a
v
e
p
r
esen
ted
a
d
is
cu
s
s
io
n
to
war
d
s
v
ar
ied
f
o
r
m
s
o
f
s
cr
ee
n
in
g
a
n
d
e
v
alu
a
tio
n
m
eth
o
d
s
co
n
s
id
er
in
g
m
u
l
tip
le
p
s
y
ch
o
m
etr
ic
p
r
o
p
er
ties
to
o
f
f
er
s
ig
n
if
ica
n
t
in
s
ig
h
ts
to
war
d
s
v
ar
ied
p
er
s
p
ec
tiv
e
o
f
d
ia
g
n
o
s
is
.
T
h
e
s
tu
d
y
ca
r
r
ied
o
u
t
b
y
Sp
ey
er
et
a
l.
[
1
1
]
h
av
e
p
r
esen
t
ed
v
ar
io
u
s
non
-
p
h
ar
m
ac
o
lo
g
ical
in
ter
v
en
tio
n
s
to
f
in
d
its
ef
f
e
ctiv
en
ess
.
Af
ter
s
tu
d
y
in
g
th
e
ab
o
v
e
r
elate
d
wo
r
k
,
f
o
llo
win
g
r
esear
ch
p
r
o
b
lem
s
h
av
e
b
ee
n
a
r
r
iv
ed
v
iz.
i)
ex
is
tin
g
r
ev
iew
p
ap
er
s
is
n
o
ted
to
eith
er
f
o
c
u
s
o
n
AI
o
r
clin
ical
m
eth
o
d
s
s
ep
ar
ately
le
ad
in
g
t
o
f
r
ag
m
e
n
ted
in
s
ig
h
ts
,
ii)
alm
o
s
t
all
th
e
c
u
r
r
en
t
m
o
d
els
d
o
esn
’
t
r
ep
o
r
t
o
f
u
n
if
o
r
m
b
en
c
h
m
ar
k
s
t
h
e
r
eb
y
r
en
d
e
r
in
g
it
ch
allen
g
in
g
to
ev
alu
ate
an
d
d
o
co
m
p
ar
is
o
n
o
f
v
a
r
io
u
s
m
o
d
els,
iii)
th
e
tem
p
o
r
al
ev
alu
atio
n
s
ass
o
ciate
d
with
d
iag
n
o
s
tic
m
eth
o
d
s
h
av
e
n
o
t
b
ee
n
ca
p
tu
r
ed
ef
f
ec
tiv
ely
i
n
e
x
is
tin
g
s
tu
d
ies,
an
d
i
v
)
t
h
er
e
a
r
e
o
n
ly
f
ew
s
tu
d
ies
th
at
h
as
ev
er
attem
p
ted
to
co
r
r
elate
d
s
cien
tific
-
ce
n
tr
ic
o
u
t
co
m
es
with
clin
ical
in
ter
p
r
etab
ilit
y
.
Hen
ce
,
it
is
clea
r
f
r
o
m
ex
is
tin
g
s
tu
d
ies
th
at
AI
-
b
ased
m
eth
o
d
s
ar
e
g
ain
in
g
p
ac
e
wh
ile
n
o
n
-
AI
b
ased
m
eth
o
d
s
also
ex
is
t
.
Ho
wev
er
,
it
is
n
o
t
clea
r
wh
ic
h
o
n
e
is
d
o
m
in
a
n
t
an
d
ef
f
ec
ti
v
e
to
war
d
s
d
ia
g
n
o
s
is
o
f
PDD
.
T
h
ese
is
s
u
es
an
d
ch
allen
g
es
ar
e
ad
d
r
ess
ed
in
th
e
p
r
o
p
o
s
ed
s
tu
d
y
b
y
in
te
g
r
a
tin
g
AI
-
b
ased
m
eth
o
d
o
l
o
g
y
with
non
-
AI
-
b
ased
d
iag
n
o
s
tic
ap
p
r
o
ac
h
es
,
th
er
eb
y
f
ac
ilit
atin
g
a
h
o
lis
tic
an
d
u
n
iv
er
s
ally
r
elev
an
t
u
n
d
e
r
s
tan
d
in
g
o
f
th
e
p
r
esen
t
s
tate
o
f
d
iag
n
o
s
tic
m
eth
o
d
s
.
T
h
e
aim
o
f
th
e
p
r
o
p
o
s
ed
s
tu
d
y
is
to
ca
r
r
y
o
u
t
a
co
m
p
r
eh
en
s
iv
e
an
d
u
p
d
ated
in
s
ig
h
t
in
to
AI
an
d
non
-
AI
-
b
ased
r
esear
ch
ap
p
r
o
a
ch
es
ad
o
p
ted
f
o
r
th
e
d
iag
n
o
s
i
s
o
f
PDD.
T
h
e
n
o
tio
n
o
f
th
is
s
tu
d
y
is
to
d
r
aw
a
co
n
clu
s
iv
e
r
em
a
r
k
ass
o
ciate
d
with
s
tr
en
g
th
a
n
d
s
h
o
r
tco
m
i
n
g
s
o
f
th
e
cu
r
r
en
t
s
tate
o
f
r
e
s
ea
r
ch
m
o
d
els.
T
h
e
co
n
tr
ib
u
tio
n
s
o
f
th
e
c
u
r
r
e
n
t stu
d
y
ar
e
as
f
o
llo
ws
:
−
T
h
e
p
a
p
er
r
ev
iews
d
u
al
-
d
o
m
ain
m
eth
o
d
s
ass
o
ciate
d
with
b
o
th
AI
an
d
n
o
n
-
AI
tech
n
iq
u
es
,
co
m
p
ly
i
n
g
with
th
e
an
aly
tical
f
r
am
ewo
r
k
.
−
Ad
o
p
tin
g
a
s
tan
d
ar
d
a
n
d
u
n
i
v
er
s
ally
ac
ce
p
ted
r
esear
c
h
m
eth
o
d
,
th
e
s
tu
d
y
o
f
f
er
s
a
q
u
a
n
titativ
e
in
s
ig
h
t
th
at
in
v
o
lv
es tr
en
d
s
o
f
d
ataset
ad
o
p
tio
n
s
an
d
p
r
ac
tical
r
an
g
es
o
f
ac
cu
r
ac
y
.
−
T
h
e
cu
r
r
en
t
s
tu
d
y
o
f
f
er
s
a
tem
p
o
r
al
m
ap
p
i
n
g
wh
er
e
r
ese
ar
ch
tr
en
d
s
o
f
last
5
-
y
ea
r
s
p
u
b
licatio
n
s
ar
e
r
ev
iewe
d
an
d
an
aly
ze
d
alo
n
g
with
d
is
tin
ctio
n
b
etwe
en
im
p
l
em
en
tatio
n
an
d
r
ev
iew
-
o
r
ien
te
d
s
tu
d
ies.
−
T
h
e
s
tu
d
y
o
f
f
er
s
a
u
n
i
q
u
e
lear
n
in
g
o
u
tco
m
e
id
e
n
tify
in
g
ce
r
t
ain
u
n
d
e
r
ex
p
lo
r
ed
ar
ea
s
alo
n
g
with
h
ig
h
lig
h
ts
o
f
g
ap
s
an
d
an
s
wer
s
to
cr
a
f
ted
r
esear
ch
q
u
esti
o
n
s
(
R
Q)
in
lin
e
o
f
d
iag
n
o
s
tic
m
eth
o
d
s
f
o
r
P
DD.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J Ar
tif
I
n
tell
I
SS
N:
2252
-
8
9
3
8
A
d
va
n
ce
s
in
c
u
r
r
en
t sta
te
o
f d
ia
g
n
o
s
tic
a
p
p
r
o
a
ch
es to
w
a
r
d
s
in
ve
s
tig
a
tin
g
…
(
A
mb
ika
S
r
ir
a
n
g
a
)
3083
T
h
e
o
r
g
an
izatio
n
o
f
th
e
m
an
u
s
cr
ip
t
is
as
f
o
llo
ws.
Sectio
n
2
p
r
esen
ts
a
d
is
cu
s
s
io
n
o
f
th
e
ad
o
p
ted
r
esear
ch
m
eth
o
d
.
Sectio
n
3
p
r
esen
ts
th
e
r
esu
lts
an
d
d
is
cu
s
s
i
o
n
o
f
t
h
e
s
tu
d
y
.
Fin
ally
,
th
e
c
o
n
clu
s
io
n
s
o
f
th
is
s
tu
d
y
ar
e
d
is
cu
s
s
ed
in
s
ec
tio
n
4
.
2.
M
E
T
H
O
D
T
h
e
p
r
esen
t
r
ev
iew
wo
r
k
is
ca
r
r
ied
o
u
t
co
n
s
id
er
in
g
s
y
s
tem
atic
liter
atu
r
e
r
ev
iew
(
SLR).
Fig
u
r
e
1
p
r
esen
ts
a
d
etailed
o
v
er
v
iew
o
f
th
e
p
r
o
ce
s
s
.
E
ac
h
o
f
th
e
m
ain
s
tep
s
is
clea
r
ly
illu
s
tr
ated
f
o
r
clar
ity
an
d
u
n
d
er
s
tan
d
i
n
g
in
th
e
r
esp
ec
tiv
e
s
ec
tio
n
s
.
A
s
y
s
tem
atic
s
ea
r
ch
was
co
n
d
u
cte
d
ac
r
o
s
s
I
E
E
E
Xp
lo
r
e,
Sp
r
in
g
er
L
in
k
,
E
ls
ev
ier
,
MD
PI,
an
d
Pu
b
Me
d
u
s
in
g
u
s
er
d
esire
d
s
ea
r
ch
k
ey
wo
r
d
s
(
PDD
,
au
tis
m
s
p
ec
tr
u
m
d
is
o
r
d
er
(
ASD)
,
d
iag
n
o
s
is
,
ML
,
DL
,
m
u
ltimo
d
al,
an
d
au
d
io
an
aly
s
is
)
.
I
n
o
r
d
er
to
h
a
v
e
ac
ce
s
s
ib
ilit
y
o
n
ly
o
p
en
-
ac
ce
s
s
jo
u
r
n
al
ar
ticles
a
r
e
tak
en
in
to
co
n
s
id
er
atio
n
.
T
h
e
in
itial
s
ea
r
ch
y
ield
e
d
a
to
t
al
o
f
1
,
5
1
6
r
ec
o
r
d
s
.
Af
ter
r
em
o
v
in
g
7
0
6
d
u
p
lica
tes,
8
1
0
s
tu
d
ies
r
em
ain
ed
f
o
r
titl
e
an
d
ab
s
tr
ac
t
s
cr
ee
n
in
g
.
At
th
is
s
tag
e,
5
1
5
r
ec
o
r
d
s
wer
e
ex
clu
d
ed
f
o
r
b
ein
g
n
o
n
-
r
ele
v
an
t,
n
o
n
-
p
e
er
-
r
ev
iewe
d
,
o
r
p
u
r
el
y
clin
ica
l
in
n
atu
r
e
with
o
u
t
co
m
p
u
tatio
n
al
m
o
d
ellin
g
.
T
h
e
f
u
ll
tex
ts
o
f
2
9
5
s
tu
d
ies
wer
e
th
en
ass
ess
ed
f
o
r
elig
ib
ilit
y
b
ased
o
n
th
e
in
clu
s
io
n
cr
iter
ia
(
p
ee
r
-
r
e
v
ie
wed
,
E
n
g
lis
h
la
n
g
u
a
g
e,
p
u
b
li
s
h
ed
b
etwe
en
2
0
2
1
an
d
2
0
2
5
,
an
d
f
o
c
u
s
in
g
o
n
co
m
p
u
tatio
n
al
ap
p
r
o
ac
h
es
to
PDD/AS
D
d
iag
n
o
s
is
)
.
Fo
llo
w
in
g
th
is
ass
e
s
s
m
en
t,
2
6
8
ar
ticles
wer
e
ex
clu
d
ed
d
u
e
to
in
s
u
f
f
icien
t
m
eth
o
d
o
l
o
g
ical
d
etails
o
r
lack
o
f
co
m
p
u
tatio
n
al
a
n
aly
s
is
,
r
esu
ltin
g
in
a
f
in
al
s
et
o
f
2
7
s
tu
d
ies
in
clu
d
ed
in
th
e
r
ev
iew.
T
h
e
s
elec
tio
n
p
r
o
ce
s
s
o
f
t
h
e
in
f
o
r
m
atio
n
is
ca
r
r
ie
d
o
u
t
co
n
s
id
er
i
n
g
p
r
ef
er
r
e
d
r
ep
o
r
tin
g
item
s
f
o
r
s
y
s
tem
atic
r
ev
iews
an
d
m
e
ta
-
an
aly
s
es
(
PR
I
SMA)
b
ased
m
eth
o
d
o
lo
g
y
f
o
r
en
s
u
r
in
g
r
e
p
r
o
d
u
cib
le
tech
n
iq
u
e.
R
e
c
o
r
d
s
i
d
e
n
t
i
f
i
e
d
t
h
r
o
u
g
h
d
a
t
a
b
s
e
s
e
a
r
c
h
i
n
g
(
I
E
E
E
X
p
l
o
r
e
,
S
p
r
i
n
g
e
r
L
i
n
k
,
E
l
s
e
v
i
e
r
,
P
u
b
M
e
d
)
(
n
=
1
5
1
6
)
I
d
e
n
t
i
f
i
e
d
D
u
p
l
i
c
a
t
e
s
(
n
=
7
0
6
)
R
e
c
o
r
d
S
c
r
e
e
n
e
d
(
n
=
8
1
0
)
R
e
c
o
r
d
E
x
c
l
u
d
e
d
(
n
=
5
1
5
)
F
u
l
l
-
t
e
x
t
a
r
t
i
c
l
e
s
a
s
s
e
s
s
e
d
f
r
o
e
l
i
g
i
b
i
l
i
t
y
(
n
=
2
9
5
)
F
u
l
l
-
t
e
x
t
a
r
t
i
c
l
e
s
e
x
c
l
u
d
e
d
(
n
=
2
6
8
)
S
t
u
d
i
e
s
i
n
c
l
u
d
e
d
i
n
q
u
a
l
i
t
a
t
i
v
e
s
y
n
t
h
e
s
i
s
(
n
=
2
7
)
I
d
e
n
t
i
f
i
c
a
t
i
o
n
S
c
r
e
e
n
i
n
g
E
l
i
g
i
b
i
l
i
t
y
I
n
c
l
u
d
e
d
Fig
u
r
e
1
.
PR
I
SMA
f
lo
w
d
iag
r
am
o
f
s
tu
d
y
s
elec
tio
n
p
r
o
ce
s
s
f
o
r
th
e
s
y
s
tem
atic
r
ev
iew
Ad
o
p
tin
g
th
e
m
eth
o
d
o
lo
g
y
,
it
is
n
o
te
d
th
at
ap
p
r
o
ac
h
es
to
w
ar
d
s
d
iag
n
o
s
is
o
f
PDD
is
ev
e
n
tu
ally
o
f
two
ty
p
es
v
iz.
AI
-
b
ased
an
d
n
o
n
-
AI
b
ased
.
Alth
o
u
g
h
,
AI
-
b
ased
m
eth
o
d
o
l
o
g
y
is
in
cr
ea
s
in
g
y
et
th
e
s
ig
n
if
ican
ce
o
f
n
o
n
-
AI
b
ased
m
eth
o
d
o
lo
g
y
ca
n
n
ev
e
r
b
e
d
is
ca
r
d
ed
.
B
o
th
h
as
th
eir
o
wn
s
ig
n
if
ican
ce
an
d
is
s
u
es
to
o
.
Hen
ce
,
it
is
an
ticip
ated
th
at
a
d
o
p
tio
n
o
f
th
is
m
eth
o
d
o
l
o
g
y
s
h
o
u
ld
p
r
o
v
id
e
an
s
wer
s
to
v
ar
io
u
s
o
n
g
o
in
g
u
n
s
o
lv
ed
q
u
esti
o
n
s
.
T
h
e
p
r
esen
t r
e
v
iew
is
g
u
id
ed
b
y
th
e
f
o
llo
win
g
RQ
:
−
RQ
1
:
wh
at
co
m
p
u
tatio
n
al
ap
p
r
o
ac
h
es
cu
r
r
en
tly
ex
is
t
f
o
r
d
i
ag
n
o
s
in
g
PDD,
an
d
h
o
w
h
av
e
th
ey
ev
o
lv
e
d
ac
r
o
s
s
m
o
d
alities
s
u
ch
as tex
t,
au
d
io
,
im
a
g
e,
an
d
m
u
ltimo
d
al
f
r
am
ewo
r
k
s
?
−
RQ
2
:
wh
at
ar
e
th
e
p
r
im
ar
y
ch
allen
g
es
an
d
lim
itatio
n
s
as
s
o
ciate
d
with
ex
is
tin
g
m
o
d
el
s
,
p
ar
ticu
lar
ly
r
eg
ar
d
in
g
d
ataset
av
ailab
ilit
y
,
g
en
er
aliza
b
ilit
y
,
in
ter
p
r
etab
ilit
y
,
an
d
co
m
p
u
tatio
n
al
co
s
t?
−
RQ
3
:
wh
at
o
p
p
o
r
tu
n
ities
ca
n
b
e
id
en
tifie
d
f
o
r
f
u
tu
r
e
r
esear
ch
to
en
h
a
n
ce
d
ia
g
n
o
s
tic
ac
c
u
r
ac
y
,
im
p
r
o
v
e
m
o
d
el
r
o
b
u
s
tn
ess
,
an
d
ad
d
r
ess
g
ap
s
in
cr
o
s
s
-
lin
g
u
al,
d
em
o
g
r
ap
h
ic,
an
d
m
u
ltimo
d
al
ap
p
lica
b
ilit
y
?
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3.
RE
SU
L
T
S AN
D
D
I
SCU
SS
I
O
N
T
h
is
s
ec
tio
n
p
r
esen
ts
th
e
o
u
t
co
m
es
o
f
th
e
p
r
esen
ted
r
ev
ie
w
f
in
d
in
g
s
th
at
o
f
f
er
s
a
co
m
p
r
eh
en
s
iv
e
in
s
ig
h
t
to
war
d
s
th
e
p
r
esen
t
s
ta
te
o
f
r
esear
c
h
wo
r
k
f
o
r
PDD.
Ad
o
p
tin
g
th
e
r
esear
ch
m
eth
o
d
o
lo
g
y
d
is
cu
s
s
ed
in
p
r
io
r
s
ec
tio
n
,
th
e
in
f
o
r
m
atio
n
co
llected
was
c
o
n
tex
tu
alize
d
with
a
n
o
tio
n
to
with
d
r
aw
t
h
e
ess
en
tial
in
s
ig
h
ts
.
T
h
e
id
ea
tio
n
o
f
th
is
r
ev
iew
f
in
d
in
g
s
is
to
war
d
s
u
n
d
er
s
tan
d
in
g
th
e
ef
f
ec
tiv
en
ess
o
f
cu
r
r
en
t
s
tate
-
of
-
th
e
-
ar
t
m
eth
o
d
s
f
o
r
th
e
d
ia
g
n
o
s
is
o
f
P
DD.
3
.
1
.
Rev
iew
f
ind
ing
s
T
ab
le
1
p
r
esen
ts
th
e
r
esear
ch
tr
en
d
s
to
war
d
s
cu
r
r
en
t
p
u
b
li
ca
tio
n
s
ass
o
ciate
d
with
im
p
le
m
en
tatio
n
p
ap
er
s
(
I
P)
an
d
r
ev
iew
-
p
a
p
er
(
R
P).
T
h
e
tab
u
lated
o
u
tco
m
e
s
h
o
wca
s
es
co
n
s
is
ten
t
g
r
o
wth
to
war
d
s
r
esear
c
h
ac
tiv
ity
in
last
5
y
ea
r
s
.
A
clo
s
er
lo
o
k
s
h
o
ws th
at
n
u
m
b
er
o
f
IP
h
av
e
s
ig
n
if
ican
tly
in
c
r
ea
s
ed
in
ev
er
y
asp
ec
t.
T
ab
le
1
.
R
esear
ch
tr
en
d
s
to
war
d
s
p
u
b
licatio
n
s
r
elate
d
to
d
ia
g
n
o
s
is
o
f
PDD
Y
e
a
r
D
a
t
a
b
a
s
e
Jo
u
r
n
a
l
O
p
e
n
a
c
c
e
ss
C
o
n
f
e
r
e
n
c
e
To
t
a
l
IP
RP
IP
RP
IP
RP
IP
RP
2
0
2
0
S
c
o
p
u
s
85
22
42
10
60
12
1
8
7
44
2
0
2
1
I
EEE
X
p
l
o
r
e
73
25
38
13
78
15
1
8
9
53
2
0
2
2
S
p
r
i
n
g
e
r
Li
n
k
96
28
51
15
70
18
2
1
7
61
2
0
2
3
S
c
i
e
n
c
e
D
i
r
e
c
t
1
0
8
31
64
18
88
22
2
6
0
71
2
0
2
4
P
u
b
M
e
d
1
2
2
34
75
20
92
25
2
8
9
79
2
0
2
5
A
C
M
d
i
g
i
t
a
l
l
i
b
r
a
r
y
1
3
5
39
80
24
1
0
0
28
3
1
5
91
T
ab
le
2
h
ig
h
lig
h
ts
th
at
au
tis
m
b
r
ain
im
ag
i
n
g
d
ata
e
x
ch
an
g
e
(
AB
I
DE
)
is
o
n
e
o
f
th
e
m
o
s
t
d
o
m
in
an
t
ad
o
p
ted
in
r
esear
ch
s
tu
d
ies
till
d
ate
d
u
e
to
its
p
o
ten
tial
co
m
m
u
n
ity
s
u
p
p
o
r
t,
h
i
g
h
ly
s
tr
u
ct
u
r
ed
n
e
u
r
o
im
a
g
in
g
d
ata,
an
d
ac
ce
s
s
ib
ilit
y
.
T
h
er
e
ar
e
also
in
c
r
ea
s
in
g
tr
e
n
d
n
o
t
ed
f
o
r
p
r
iv
ate/c
lin
ical
d
ataset
as
well
as
Simo
n
s
s
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llectio
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SS
C
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ata
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in
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t
tim
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m
p
ar
ed
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o
AB
I
DE
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Similar
p
atter
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also
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o
ted
f
o
r
n
atio
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al
d
atab
ased
f
o
r
au
tis
m
r
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ch
(
NDAR)
an
d
s
y
n
th
etic
d
ataset.
T
ab
le
2
.
Data
s
et
ad
o
p
tio
n
tr
en
d
Y
e
a
r
A
B
I
D
E
SSC
NDAR
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r
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t
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d
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s
2
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62
35
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71
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51
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57
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73
48
T
ab
le
3
s
h
o
wca
s
es
th
at
DL
a
p
p
r
o
ac
h
es
ar
e
g
ain
in
g
in
cr
ea
s
in
g
p
ac
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in
co
n
tr
ast
to
ML
m
o
d
els
as
f
o
u
n
d
in
th
e
last
5
y
ea
r
s
.
T
h
e
ad
o
p
tio
n
r
ate
f
o
r
DL
is
h
ig
h
er
b
ec
a
u
s
e
o
f
its
ca
p
ab
ilit
y
to
war
d
s
h
an
d
lin
g
m
u
ltimo
d
al
d
ataset
an
d
co
m
p
lex
s
ce
n
ar
io
s
m
ain
l
y
.
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t
h
e
s
am
e
tim
e
,
th
er
e
is
in
cr
e
asin
g
ad
o
p
tio
n
f
o
r
ex
p
lain
ab
le
ar
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in
tellig
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ce
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XAI
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t
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o
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u
e
to
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ca
p
ab
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f
er
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ter
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etab
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lth
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r
e
d
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o
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tics
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t
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o
ted
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o
p
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o
f
h
y
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r
id
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els
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ce
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lear
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R
L
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els
ar
e
q
u
ite
less
in
co
n
tr
ast to
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th
er
AI
s
ch
em
es
.
T
ab
le
3
.
Ad
o
p
tio
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o
f
AI
mod
e
ls
in
PDD
r
esear
ch
Y
e
a
r
ML
DL
XAI
RL
H
y
b
r
i
d
/
e
n
sem
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l
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m
o
d
e
l
s
To
t
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c
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t
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us
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g
A
I
2
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34
3
1
3
An
in
v
esti
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is
also
ca
r
r
ied
o
u
t
to
u
n
d
e
r
s
tan
d
th
e
in
f
lu
en
ce
o
f
two
eq
u
ally
d
o
m
in
atin
g
tech
n
iq
u
es
i.e
.
,
AI
-
b
ased
an
d
n
o
n
-
AI
b
ased
a
p
p
r
o
ac
h
es.
A
clo
s
er
lo
o
k
in
T
ab
le
4
s
h
o
ws
th
at
ac
cu
r
ac
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ac
co
m
p
lis
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ed
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q
u
ite
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y
AI
-
b
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ap
p
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co
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AI
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d
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ig
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ly
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f
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to
m
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en
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PD
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Yet,
t
h
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s
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tain
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ter
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p
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d
d
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estrict
clin
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d
ep
lo
y
m
en
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r
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n
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.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
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2252
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8
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A
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AI
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b
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se
d
met
h
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d
s
ML
[
1
2
]
–
S
u
p
p
o
r
t
s
c
l
a
ssi
f
i
c
a
t
i
o
n
o
f
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e
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r
o
i
m
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s
.
−
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r
l
y
d
i
a
g
n
o
si
s
a
n
d
p
a
t
t
e
r
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r
e
c
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n
i
t
i
o
n
.
−
A
c
c
u
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a
c
y
r
a
n
g
e
:
7
6
–
8
9
%
.
–
D
e
man
d
s
f
e
a
t
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r
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e
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g
i
n
e
e
r
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g
.
–
l
i
m
i
t
e
d
sca
l
a
b
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l
i
t
y
f
o
r
l
a
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e
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o
g
e
n
e
o
u
s
d
a
t
a
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e
t
s
.
DL
[
1
3
]
–
Le
a
r
n
s c
o
m
p
l
e
x
r
e
p
r
e
se
n
t
a
t
i
o
n
s
.
–
En
h
a
n
c
e
d
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a
g
n
o
s
t
i
c
p
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e
c
i
si
o
n
.
–
A
c
c
u
r
a
c
y
r
a
n
g
e
:
8
3
–
9
6
%
.
–
Li
mi
t
e
d
i
n
t
e
r
p
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t
a
b
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t
y
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l
i
n
i
c
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se
.
–
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o
m
p
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t
a
t
i
o
n
a
l
l
y
e
x
p
e
n
s
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e
.
N
LP
[
1
4
]
–
A
n
a
l
y
z
e
s
c
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m
p
l
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c
o
mm
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–
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c
c
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y
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6
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X
A
I
[
1
5
]
–
I
mp
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–
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9
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8
9
%
.
–
La
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R
L
[
1
6
]
–
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–
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–
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A
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[
1
7
]
,
[
1
8
]
–
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–
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–
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r
a
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k
s
[
1
9
]
–
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r
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me
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–
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d
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:
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1
%
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–
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t
a
.
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a
n
sf
e
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a
r
n
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g
[
2
0
]
–
S
u
p
p
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a
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–
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[
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1
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–
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[
2
2
]
–
R
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d
.
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e
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b
s
e
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d
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s
[
2
3
]
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s s
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n
d
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o
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o
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–
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a
n
d
v
a
r
i
a
b
i
l
i
t
y
a
m
o
n
g
r
a
t
e
r
s.
G
e
n
e
t
i
c
a
n
d
b
i
o
mar
k
e
r
-
b
a
s
e
d
mo
d
e
l
s
[
2
4
]
–
D
e
t
e
r
m
i
n
e
s
b
i
o
mar
k
e
r
s
a
n
d
g
e
n
e
t
i
c
c
o
r
r
e
l
a
t
i
o
n
s
f
o
r
P
D
D
r
i
sk
.
–
A
c
c
u
r
a
c
y
r
a
n
g
e
:
6
1
–
7
1
%
.
–
C
o
m
p
l
i
c
a
t
e
d
p
a
t
t
e
r
n
s
o
f
i
n
h
e
r
i
t
a
n
c
e
.
–
Lo
w
d
i
a
g
n
o
st
i
c
s
p
e
c
i
f
i
c
i
t
y
.
S
u
r
v
e
y
-
b
a
se
d
a
n
d
q
u
a
l
i
t
a
t
i
v
e
me
t
h
o
d
s
[
2
5
]
–
F
u
ses
p
e
r
c
e
p
t
i
o
n
s fr
o
m
c
a
r
e
g
i
v
e
r
s
.
–
R
e
l
i
a
b
i
l
i
t
y
:
5
6
–
7
1
%
.
–
Bias
.
–
La
c
k
s
o
b
j
e
c
t
i
v
e
q
u
a
n
t
i
f
i
c
a
t
i
o
n
.
C
o
g
n
i
t
i
v
e
/
p
s
y
c
h
o
me
t
r
i
c
t
e
st
i
n
g
-
b
a
s
e
d
mo
d
e
l
s
[
2
6
]
–
Ev
a
l
u
a
t
e
s
a
t
t
e
n
t
i
o
n
i
n
P
D
D
i
n
d
i
v
i
d
u
a
l
s.
–
S
c
r
e
e
n
i
n
g
v
a
l
i
d
i
t
y
:
6
6
–
7
9
%
.
–
C
u
l
t
u
r
a
l
a
n
d
l
i
n
g
u
i
s
t
i
c
v
a
r
i
a
b
i
l
i
t
y
a
f
f
e
c
t
s
g
e
n
e
r
a
l
i
z
a
t
i
o
n
.
N
e
u
r
o
f
e
e
d
b
a
c
k
-
b
a
se
d
e
x
p
e
r
i
me
n
t
s
[
2
7
]
–
B
e
h
a
v
i
o
r
r
e
g
u
l
a
t
i
o
n
a
n
d
e
mo
t
i
o
n
a
l
c
o
n
t
r
o
l
.
–
Ef
f
e
c
t
i
v
e
n
e
ss:
7
1
–
8
4
%
.
–
V
a
r
i
a
b
l
e
o
u
t
c
o
mes
a
c
r
o
ss
i
n
d
i
v
i
d
u
a
l
s.
–
Ti
me
-
i
n
t
e
n
si
v
e
.
3
.
2
.
L
ea
rning
o
utc
o
m
es
On
e
o
f
th
e
k
ey
o
u
tco
m
es
o
f
th
e
s
tu
d
y
is
s
h
o
wn
in
Fig
u
r
e
2
,
wh
ich
ev
en
tu
ally
s
h
o
w
ca
s
es
th
e
ad
o
p
tio
n
o
f
AI
-
ap
p
r
o
ac
h
es is
g
r
o
win
g
s
ig
n
if
ican
tly
h
ig
h
er
i
n
co
n
tr
ast to
n
o
n
-
AI
m
et
h
o
d
s
(
Fig
u
r
e
2
(
a)
)
.
At
th
e
s
am
e
tim
e
,
ad
o
p
tio
n
o
f
AB
I
DE
d
ataset
is
f
o
u
n
d
co
n
s
is
ten
tly
to
b
e
ad
o
p
ted
in
th
e
last
5
y
ea
r
s
(
Fig
u
r
e
2
(
b
)
)
.
Up
o
n
r
ev
iewin
g
v
ar
io
u
s
r
ese
ar
ch
m
et
h
o
d
o
lo
g
ies
an
d
s
tu
d
y
in
g
t
h
eir
e
f
f
ec
tiv
en
ess
,
th
e
ex
tr
ac
ted
lear
n
in
g
o
u
tco
m
es o
f
th
e
s
tu
d
y
a
r
e
as f
o
llo
ws:
−
T
h
e
n
u
m
b
er
o
f
r
esear
ch
wo
r
k
s
to
war
d
s
non
-
AI
-
b
ased
ap
p
r
o
ac
h
es
is
v
e
r
y
less
in
co
n
tr
a
s
t
to
AI
-
b
ased
ap
p
r
o
ac
h
es
,
wh
ile
th
is
u
n
b
ala
n
ce
d
ec
o
s
y
s
tem
o
f
f
er
s
a
s
co
p
e
o
f
in
ter
v
en
tio
n
-
o
r
ien
ted
AI
to
o
.
−
T
h
e
ar
ea
o
f
h
y
b
r
i
d
m
o
d
els
an
d
m
u
lti
-
m
o
d
al
ap
p
r
o
ac
h
es
is
in
cr
ea
s
in
g
in
im
p
lem
e
n
tatio
n
wo
r
k
s
th
at
is
n
o
ted
to
co
m
b
in
e
f
ea
t
u
r
e
r
e
lated
to
lan
g
u
ag
e
,
b
eh
a
v
io
r
,
an
d
n
eu
r
o
im
ag
i
n
g
in
o
r
d
er
to
estab
lis
h
co
m
p
r
eh
e
n
s
iv
e
p
r
o
f
ilin
g
o
f
P
DD.
−
AB
I
DE
an
d
NDAR
is
o
n
e
o
f
th
e
m
o
s
t
wid
ely
ad
o
p
ted
d
at
asets
,
an
d
s
till
its
ad
o
p
tio
n
r
a
te
r
em
ain
s
th
e
s
am
e
in
cu
r
r
en
t state
o
f
r
esear
ch
p
u
b
licatio
n
in
co
n
tr
ast to
o
t
h
er
f
o
r
m
s
o
r
ty
p
es o
f
d
atasets
.
−
At
p
r
esen
t,
th
e
d
ataset
i
s
h
ig
h
ly
s
p
ec
if
ic
o
f
g
eo
g
r
a
p
h
ic
r
e
g
io
n
s
f
r
o
m
wh
er
e
th
e
s
am
p
l
es
h
av
e
b
ee
n
co
llected
an
d
c
u
r
ated
.
Hen
ce
,
its
r
esear
ch
p
ap
er
s
co
n
s
id
er
i
n
g
s
u
ch
r
e
g
io
n
-
s
p
ec
if
ic
d
ataset
ca
n
n
o
t
claim
f
o
r
g
e
n
er
aliza
tio
n
o
r
ex
ten
s
iv
e
ap
p
licab
ilit
y
o
f
t
h
eir
wo
r
k
.
T
h
e
id
en
tifie
d
r
esear
ch
g
a
p
s
ar
e
as f
o
llo
ws:
−
All
th
e
ex
is
tin
g
o
p
en
d
ataset
ar
e
q
u
ite
s
m
all
in
n
u
m
b
er
a
n
d
y
et
th
ey
ar
e
f
o
u
n
d
to
p
o
te
n
tially
d
o
m
in
a
n
t
th
e
IP
wo
r
k
to
war
d
s
PDD.
He
n
ce
,
d
ataset
b
iasi
n
g
is
q
u
ite
in
ev
itab
le
wh
ile
th
er
e
is
r
estrict
ed
d
iv
er
s
ity
in
th
e
d
ataset
r
en
d
in
g
th
e
f
in
d
in
g
s
less
ap
p
licab
le
to
lar
g
er
s
ca
le.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
2
5
2
-
8
9
3
8
I
n
t J Ar
tif
I
n
tell
,
Vo
l.
1
5
,
No
.
4
,
Au
g
u
s
t 2
0
2
6
:
3
0
8
1
-
3
0
8
9
3086
−
T
h
e
ap
p
r
o
ac
h
es
e.
g
.
,
g
r
ap
h
n
eu
r
al
n
etwo
r
k
an
d
R
L
h
av
e
q
u
ite
a
b
etter
p
o
ten
tial
to
war
d
s
b
o
o
s
tin
g
d
etec
tio
n
an
d
class
if
icatio
n
o
f
PDD;
h
o
wev
er
,
cu
r
r
en
t
s
tate
o
f
p
u
b
licatio
n
s
in
th
is
r
eg
ar
d
ar
e
v
er
y
f
ew
to
f
in
d
b
e
n
ch
m
ar
k
in
g
o
u
tco
m
es.
−
T
h
er
e
is
a
p
o
ten
tial
g
ap
b
etw
ee
n
wh
at
is
p
r
esen
ted
in
r
esear
ch
wo
r
k
an
d
wh
at
is
r
ea
lly
d
em
an
d
e
d
o
n
r
ea
l
-
tim
e
to
war
d
s
PDD
d
iag
n
o
s
is
.
W
ith
le
s
s
r
esear
ch
p
a
p
er
s
o
n
XAI
,
th
e
in
n
o
v
ativ
e
n
ess
to
war
d
s
v
alid
atio
n
an
d
in
ter
p
r
etab
ilit
y
is
s
till
le
s
s
.
−
T
h
e
r
esear
ch
p
u
b
licatio
n
s
f
o
r
AI
-
b
ased
a
p
p
r
o
ac
h
es
ar
e
h
ig
h
ly
d
is
p
r
o
p
o
r
tio
n
ate
to
n
o
n
-
AI
-
b
ased
ap
p
r
o
ac
h
es.
Alth
o
u
g
h
,
it
is
en
co
u
r
ag
in
g
f
o
r
p
r
o
ac
tiv
e
d
iag
n
o
s
tic
m
eth
o
d
s
in
PDD
d
iag
n
o
s
is
,
y
et
alm
o
s
t
all
AI
m
eth
o
d
s
ar
e
n
o
t
with
o
u
t
f
laws
(
u
n
r
eliab
le
p
r
ed
ictio
n
,
o
v
e
r
f
itti
n
g
,
i
n
cr
ea
s
ed
r
esp
o
n
s
e
tim
e,
an
d
ex
ten
s
iv
e
r
eso
u
r
ce
d
ep
en
d
en
ci
es)
th
at
ar
e
y
et
to
b
e
in
v
esti
g
at
ed
.
(
a)
(
b
)
Fig
u
r
e
2
.
C
o
r
e
s
tu
d
y
f
in
d
in
g
s
f
o
r
tr
en
d
s
o
f
(
a
)
AI
an
d
n
o
n
-
A
I
b
ased
m
eth
o
d
s
an
d
(
b
)
d
ataset
ad
o
p
tio
n
3
.
3
.
Ans
wer
s
t
o
re
s
ea
rc
h ques
t
io
n
T
h
e
id
en
tifie
d
s
o
lu
tio
n
to
th
e
RQ
1
is
as:
th
e
co
m
p
u
tatio
n
al
a
p
p
r
o
ac
h
es to
war
d
s
d
iag
n
o
s
is
o
f
PDD
ar
e
ev
o
lu
tio
n
ar
y
tr
en
d
s
,
m
u
lti
m
o
d
al
ap
p
r
o
ac
h
es,
im
ag
e
-
b
ased
ap
p
r
o
ac
h
es,
au
d
i
o
-
b
as
ed
ap
p
r
o
ac
h
,
an
d
tex
t
-
b
ased
ap
p
r
o
ac
h
.
Fu
r
th
er
,
it
is
n
o
ted
th
at
co
m
p
u
tatio
n
a
l
m
eth
o
d
s
h
a
v
e
s
lo
wly
tr
an
s
f
o
r
m
ed
its
elf
to
DL
an
d
m
u
ltimo
d
al
m
o
d
els
f
r
o
m
s
im
p
le
an
d
m
an
u
al
-
o
p
er
ati
o
n
b
ased
co
n
v
e
n
tio
n
al
m
o
d
el.
T
h
is
n
ewly
ev
o
lv
ed
m
o
d
el
is
ca
p
a
b
le
o
f
in
te
g
r
atin
g
n
eu
r
o
im
ag
i
n
g
d
ata,
lin
g
u
is
tic
tr
aits
,
an
d
co
m
p
le
x
b
e
h
av
io
r
f
o
r
m
o
r
e
s
ca
lab
le
an
d
ac
cu
r
ate
d
iag
n
o
s
is
o
f
PDD.
T
h
e
id
en
tifie
d
s
o
lu
tio
n
to
R
Q
2
is
a
s
:
t
h
e
p
r
im
ar
y
ch
allen
g
es
as
well
a
s
lim
i
tatio
n
ass
o
ciate
d
with
th
e
co
n
v
en
tio
n
al
m
o
d
els
ar
e
m
ain
ly
r
elate
d
to
in
cr
ea
s
ed
co
m
p
u
tatio
n
al
co
s
t,
r
ed
u
ce
d
in
ter
p
r
etab
ilit
y
,
p
o
o
r
g
en
er
aliza
tio
n
,
an
d
lim
ited
d
ataset
av
ailab
ilit
y
.
T
h
er
e
ar
e
also
f
ew
s
tu
d
ies
th
at
h
av
e
ad
d
r
ess
ed
is
s
u
e
s
p
er
tain
in
g
to
lo
n
g
itu
d
in
al
d
at
a
n
eith
er
s
u
f
f
icien
t
wo
r
k
is
c
ar
r
ied
o
u
t
t
o
war
d
s
ad
ap
tiv
e
lear
n
in
g
f
r
am
ewo
r
k
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J Ar
tif
I
n
tell
I
SS
N:
2252
-
8
9
3
8
A
d
va
n
ce
s
in
c
u
r
r
en
t sta
te
o
f d
ia
g
n
o
s
tic
a
p
p
r
o
a
ch
es to
w
a
r
d
s
in
ve
s
tig
a
tin
g
…
(
A
mb
ika
S
r
ir
a
n
g
a
)
3087
T
h
e
ev
o
lv
in
g
ap
p
r
o
ac
h
es
r
ela
ted
to
lar
g
e
tr
an
s
f
o
r
m
er
-
o
r
ien
ted
NL
P
m
o
d
els,
an
aly
s
is
o
f
n
eu
r
o
im
a
g
in
g
,
o
r
m
u
ltimo
d
al
f
u
s
io
n
d
em
a
n
d
s
s
ig
n
if
ican
tly
h
i
g
h
co
m
p
u
tatio
n
a
l r
eso
u
r
ce
s
.
T
h
e
id
en
tifie
d
s
o
lu
tio
n
to
R
Q
3
is
:
th
er
e
ar
e
v
ar
io
u
s
q
u
an
tifi
ed
o
p
p
o
r
tu
n
ities
to
war
d
s
f
u
tu
r
e
r
esear
ch
m
ain
ly
in
d
ir
ec
tio
n
o
f
em
er
g
i
n
g
m
eth
o
d
o
lo
g
ies
e.
g
.
,
AI
-
ass
is
ted
th
er
ap
y
,
f
r
am
ewo
r
k
s
d
esig
n
ed
u
s
in
g
R
F
an
d
g
r
ap
h
n
e
u
r
al
n
etwo
r
k
.
T
h
er
e
is
al
s
o
en
o
u
g
h
s
co
p
e
to
w
ar
d
s
ex
p
lo
r
atio
n
o
f
p
er
s
o
n
al
ized
an
d
ad
ap
tiv
e
in
ter
v
en
tio
n
s
b
y
A
I
-
p
r
ed
ictiv
e
m
o
d
ellin
g
.
Fu
tu
r
e
wo
r
k
ca
n
also
lay
f
o
u
n
d
atio
n
to
war
d
s
c
lin
ical
ap
p
licab
ilit
y
an
d
in
ter
p
r
etab
ilit
y
as
well
as
b
y
ad
d
r
ess
in
g
d
ata
g
a
p
s
b
y
e
x
ten
d
in
g
p
u
b
lic
d
ataset
to
i
n
v
o
lv
e
lan
g
u
a
g
es,
ag
e
g
r
o
u
p
s
,
an
d
p
o
p
u
latio
n
.
Me
t
h
o
d
o
lo
g
ies
ad
o
p
tin
g
v
alid
atio
n
s
u
s
in
g
c
r
o
s
s
-
d
em
o
g
r
ap
h
ics,
cr
o
s
s
-
lin
g
u
al,
an
d
cr
o
s
s
-
s
it c
an
o
f
f
er
b
r
o
ad
e
r
g
en
er
aliza
tio
n
to
war
d
s
d
iag
n
o
s
tic
m
o
d
el
o
f
PDD.
T
h
er
ef
o
r
e,
th
e
s
tu
d
y
s
y
n
th
esi
ze
s
b
o
th
th
e
cu
r
r
en
t
an
d
f
u
tu
r
e
s
tate
o
f
v
ar
ied
ap
p
r
o
ac
h
es
(
esp
ec
ially
co
m
p
u
tatio
n
al)
to
war
d
s
in
v
est
ig
atin
g
PDD.
T
h
e
an
s
wer
o
f
R
Q
1
s
u
g
g
est
co
n
tex
tu
ally
to
c
o
n
tin
u
e
im
p
r
o
v
is
in
g
DL
m
o
d
els as it
h
as
h
ig
h
er
s
co
p
e
an
d
p
o
ten
tial.
T
h
e
an
s
wer
o
f
R
Q
2
s
u
g
g
ests
ce
r
tain
s
h
o
r
tco
m
in
g
s
an
d
cr
itical
ch
allen
g
es
th
at
d
em
an
d
atten
t
io
n
f
o
r
ad
d
r
ess
in
g
in
th
e
u
p
c
o
m
in
g
m
o
d
ellin
g
p
er
s
p
ec
tiv
e
,
wh
ile
an
s
wer
s
to
RQ
3
s
tate
th
e
h
ig
h
er
s
co
p
e
an
d
o
p
p
o
r
tu
n
ities
r
esid
in
g
in
v
ar
ied
ev
o
lv
in
g
m
o
d
ellin
g
p
e
r
s
p
e
ctiv
es
.
3
.
4
.
No
v
elt
y
o
f
pro
po
s
ed
s
t
ud
y
T
ab
le
5
s
h
o
wca
s
es
th
e
n
o
v
elty
an
d
co
n
tr
ib
u
tio
n
ass
o
ciate
d
w
ith
th
e
p
r
esen
ted
wo
r
k
.
I
t
is
n
o
ted
th
at
a
p
o
ten
tial
estab
lis
h
m
en
t
if
an
i
n
teg
r
ated
f
r
am
ewo
r
k
co
n
n
ec
ti
n
g
p
u
b
licatio
n
d
y
n
a
m
ics,
d
ata
s
et
ev
o
lu
tio
n
,
a
n
d
m
eth
o
d
o
l
o
g
ical
ad
v
an
ce
s
in
PDD
r
esear
ch
.
T
h
e
o
u
tco
m
e
o
f
th
e
s
tu
d
y
also
ex
p
lo
r
ed
DL
m
eth
o
d
o
l
o
g
ies
to
b
e
lead
in
g
in
c
o
n
tr
ast to
o
th
e
r
AI
-
ce
n
tr
ic
m
o
d
els.
T
h
er
e
is
a
r
e
q
u
ir
em
e
n
t
o
f
eth
i
ca
l
v
alid
atio
n
s
an
d
ex
p
lain
a
b
i
lity
b
y
th
ese
m
o
d
els
th
at
is
n
o
t
f
o
u
n
d
to
b
e
p
o
ten
tially
estab
lis
h
ed
as
g
r
o
u
n
d
wo
r
k
b
y
an
y
r
esear
ch
er
s
till
d
ate
with
an
ap
p
licab
ilit
y
o
n
PDD.
T
h
e
p
r
o
p
o
s
ed
in
v
esti
g
atio
n
h
as
u
n
d
er
s
co
r
ed
h
y
b
r
id
clin
ical
-
AI
in
teg
r
atio
n
as
th
e
u
p
co
m
in
g
f
r
o
n
tier
s
to
war
d
s
en
h
an
cin
g
th
er
a
p
eu
tic
p
er
s
o
n
a
lizatio
n
an
d
d
iag
n
o
s
tic
r
eliab
il
ity
.
Dif
f
er
en
t f
r
o
m
ex
is
tin
g
r
e
v
iew
wo
r
k
to
war
d
s
r
elate
d
to
p
ic,
it
is
n
o
ted
th
at
p
r
o
p
o
s
ed
r
ev
iew
wo
r
k
p
r
esen
ts
a
tem
p
o
r
al
d
im
en
s
io
n
b
y
m
ap
p
in
g
r
esear
c
h
tr
en
d
s
o
v
er
2
0
2
0
-
2
0
2
5
.
T
h
is
p
o
ten
tially
co
n
tr
ib
u
tes n
o
v
elty
attr
ib
u
tes in
tr
o
d
u
ce
d
b
y
p
r
o
p
o
s
ed
r
ev
iew
wo
r
k
.
T
ab
le
5
.
No
v
elty
an
d
co
n
tr
i
b
u
tio
n
o
f
th
e
p
r
o
p
o
s
ed
r
ev
iew
I
t
e
ms
P
r
o
p
o
se
d
r
e
v
i
e
w
Ex
i
s
t
i
n
g
r
e
v
i
e
w
s
C
o
v
e
r
a
g
e
sc
o
p
e
B
o
t
h
A
I
a
n
d
n
o
n
-
A
I
met
h
o
d
s
w
i
t
h
i
l
l
u
st
r
a
t
i
v
e
c
o
m
p
a
r
i
so
n
o
f
t
h
e
i
r
a
c
c
u
r
a
c
y
,
c
o
n
t
r
i
b
u
t
i
o
n
,
a
n
d
sh
o
r
t
c
o
mi
n
g
s.
O
n
l
y
d
e
a
l
s
w
i
t
h
A
I
-
b
a
sed
d
i
a
g
n
o
st
i
c
t
o
o
l
s
o
r
c
l
i
n
i
c
a
l
m
e
t
h
o
d
s
.
Ta
x
o
n
o
m
y
o
f
m
e
t
h
o
d
s
M
u
l
t
i
-
l
e
v
e
l
c
l
a
ss
i
f
i
c
a
t
i
o
n
o
f
A
I
a
n
d
n
o
n
-
AI
t
e
c
h
n
i
q
u
e
s.
La
c
k
s fi
n
e
-
g
r
a
i
n
e
d
t
a
x
o
n
o
m
y
.
Q
u
a
n
t
i
t
a
t
i
v
e
i
n
si
g
ht
I
n
v
o
l
v
e
p
r
a
c
t
i
c
a
l
a
c
c
u
r
a
c
y
r
a
n
g
e
s,
u
p
d
a
t
e
d
d
a
t
a
s
e
t
t
r
e
n
d
s
−
La
c
k
s
n
u
meri
c
a
l
t
r
e
n
d
d
a
t
a
.
−
P
r
i
maril
y
q
u
a
l
i
t
a
t
i
v
e
s
u
mm
a
r
i
e
s
.
D
a
t
a
s
e
t
a
d
o
p
t
i
o
n
−
A
ssesse
s
o
p
e
n
-
a
c
c
e
ss
a
n
d
p
r
i
v
a
t
e
d
a
t
a
set
s
.
−
H
i
g
h
l
i
g
h
t
s
r
e
se
a
r
c
h
b
i
a
s a
n
d
d
a
t
a
se
t
d
o
mi
n
a
n
c
e
N
o
s
y
st
e
ma
t
i
c
d
a
t
a
se
t
a
n
a
l
y
si
s
.
M
a
p
p
i
n
g
t
e
mp
o
r
a
l
t
r
e
n
d
−
F
i
v
e
-
y
e
a
r
p
u
b
l
i
c
a
t
i
o
n
.
−
D
i
f
f
e
r
e
n
t
i
a
t
i
n
g
e
x
p
e
r
i
me
n
t
a
l
v
s
.
s
u
r
v
e
y
p
a
p
e
r
s.
M
o
s
t
r
e
v
i
e
w
s
c
o
v
e
r
s
h
o
r
t
e
r
o
r
u
n
d
e
f
i
n
e
d
p
e
r
i
o
d
s.
A
I
v
a
r
i
a
n
t
c
o
m
p
a
r
i
so
n
−
Ev
a
l
u
a
t
e
s
p
e
r
f
o
r
ma
n
c
e
a
n
d
p
o
p
u
l
a
r
i
t
y
o
f
A
I
v
a
r
i
a
n
t
s
.
U
su
a
l
l
y
l
i
mi
t
e
d
t
o
a
si
n
g
l
e
A
I
f
a
mi
l
y
I
n
t
e
g
r
a
t
i
o
n
p
e
r
sp
e
c
t
i
v
e
F
o
c
u
ses
o
n
c
r
o
ss
-
m
o
d
a
l
a
n
d
h
y
b
r
i
d
i
n
t
e
g
r
a
t
i
o
n
.
R
a
r
e
l
y
a
d
d
r
e
sse
s
m
u
l
t
i
-
mo
d
a
l
f
u
s
i
o
n
a
p
p
r
o
a
c
h
e
s.
C
l
i
n
i
c
a
l
r
e
l
e
v
a
n
c
e
a
n
d
e
x
p
l
a
i
n
a
b
i
l
i
t
y
−
D
i
scu
sses
c
h
a
l
l
e
n
g
e
s
r
e
l
a
t
e
d
t
o
c
l
i
n
i
c
a
l
d
e
p
l
o
y
me
n
t
,
i
n
t
e
r
p
r
e
t
a
b
i
l
i
t
y
,
X
A
I
.
Le
ss
p
r
i
o
r
r
e
v
i
e
w
s
e
n
g
a
g
e
w
i
t
h
c
l
i
n
i
c
a
l
u
sa
b
i
l
i
t
y
o
r
e
x
p
l
a
i
n
a
b
i
l
i
t
y
N
o
v
e
l
c
o
n
t
r
i
b
u
t
i
o
n
−
B
a
l
a
n
c
e
d
,
e
n
d
-
to
-
e
n
d
s
y
n
t
h
e
s
i
s,
d
a
t
a
se
t
s,
p
u
b
l
i
c
a
t
i
o
n
d
y
n
a
mi
c
s,
e
mer
g
i
n
g
d
i
r
e
c
t
i
o
n
s
a
n
d
r
e
sea
r
c
h
g
a
p
s.
L
a
c
k
s
i
n
t
e
g
r
a
t
i
v
e
s
y
n
t
h
e
si
s
a
c
r
o
ss
A
I
a
n
d
n
o
n
-
A
I
d
o
m
a
i
n
s,
d
o
ma
i
n
-
r
e
st
r
i
c
t
e
d
.
4.
CO
NCLU
SI
O
N
T
h
e
s
tu
d
y
o
u
tco
m
e
o
f
th
e
p
r
esen
t
wo
r
k
s
h
o
wca
s
es
in
cr
ea
s
in
g
p
av
e
o
f
ad
o
p
tio
n
o
f
d
ata
-
ce
n
tr
ic
AI
m
eth
o
d
s
th
at
h
av
e
ev
o
l
v
ed
f
r
o
m
co
n
v
en
tio
n
al
n
e
u
r
o
p
h
y
s
ic
al
an
d
b
eh
a
v
io
r
al
m
eth
o
d
s
.
T
h
e
s
tu
d
y
f
in
d
s
th
at
DL
an
d
h
y
b
r
id
a
p
p
r
o
ac
h
es
a
r
e
v
er
y
m
u
c
h
em
e
r
g
in
g
s
o
lu
t
io
n
s
to
war
d
s
p
atter
n
r
ec
o
g
n
it
io
n
an
d
d
ia
g
n
o
s
tic
ac
cu
r
ac
y
.
An
o
th
er
in
te
r
esti
n
g
f
in
d
in
g
o
f
th
e
s
tu
d
y
is
p
o
te
n
tially
h
ig
h
er
a
d
o
p
tio
n
o
f
N
DAR
an
d
AB
I
DE
d
atasets
d
esp
ite
th
e
f
ac
t
th
at
b
o
th
o
f
th
ese
d
atasets
h
av
e
lo
wer
d
eg
r
ee
o
f
d
iv
er
s
ity
a
n
d
h
as
b
iasn
ess
f
ac
to
r
s
.
T
h
is
ev
en
tu
ally
r
em
ai
n
s
an
o
p
en
-
en
d
ch
allen
g
e
.
T
h
e
s
tu
d
y
f
in
d
in
g
s
also
n
o
ted
t
h
at
AI
-
ce
n
tr
ic
m
o
d
ellin
g
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