Transcript Slide 1

Spectrophotometric analysis
of two All-Ceramic materials
Varun Singh Barath
University of Cologne, Germany
Dilemma
Esthetic Dentistry
• Since ancient times – teeth have been an
integral part of the face
• Animal teeth and Ivory– all carved in the
form of human teeth
• Early 16th Century – Mineral teeth
John Greenwood
Esthetic Dentistry
• Metal Ceramic restorations – 4 decades
ago were the “State of Art”
• All-Ceramic restorations – advancements
in last decade have made them popular
– Increase in strength
– Better biocompatiblity
– Excellent optical properties
• PART 1: Spectrophotometric analysis of
two All-Ceramic materials with the effect of
the background shade on the final shade
• PART 2: Proposed Model for Color
Prediction using Kubelka-Munk theory and
Artificial Neural Networks
Spectrophotometric analysis of two AllCeramic materials with the effect of the
background shade on the final shade
Some aspects of Color
• Color is the perception of light by the mind
in response to a stimuli from the eye
• It is a visual sensation
• Different colors have different wavelengths
• Visible part of the spectrum 380 – 750 nm
Some aspects of Color
Color systems
• Numerical representation of Color
• International Commission of Illumination
(Commission Internationale de
l’Eclairage).
• Important colorimetric systems are RGB,
XYZ, CIELAB, CMC, Munsell system, to
name a few
CIELAB system
Courtesy: Handprint media
CIELAB system
• Estd. 1976 (by the International
Commission of Illumination (Commission
Internationale de l’Eclairage))
• L* - vertical, achromatic coordinate
0 (black) to 100 (white);
• a* - horizontal, green/red coordinate,
-80 (green) to +80 (red);
• b* - horizontal, blue/yellow coordinate
-80 (blue) to +80 (yellow);
CIELAB system
Courtesy: Handprint media
CIELAB system
• C - saturation, representing the difference
of a specific color in relation to gray
color of the same lightness
• H° - hue is represented in the ab plane
H=0° corresponds to red color,
H=90° corresponds to yellow,
H=180° corresponds to green,
H=270° corresponds to blue color
Experimental Design
• Aim: to study the effect of background
shade on the final shade of All-Ceramic
Systems (In-Ceram Alumina, Empress2)
• Shades chosen: lighter than the lightest,
darker than the darkest and one from the
middle
• Luting Agents: ZnPO4 , GIC, RLA
• Background: Standard black and white
Armamentarium
• Ceramic samples as clinical units
In-Ceram Alumina, 1,0 mm
In-Ceram Alumina, 1,4 mm
Empress 2, 1,4 mm
Armamentarium
• Cements
Luting agent Shade
Commercial
name
Manufacturer
Zinc Phosphate Neutral
Cement
PhosphaCEM PL
Vivadent Ets.
Lichtenstein
Glass Ionomer
Cement
Universal Ketac-Cem
radiopaque
ESPE Dental AG,
Germany
Composite
Luting agent
A3
ESPE Dental AG,
Germany
Compolute
Aplicap
Armamentarium
• Micrometer (Mitutoyo, Japan)
Armamentarium
• Sample Preparation (Simulating a clinical
All-Ceramic restoration)
Armamentarium
• Spectrophotometer (Dr. Lange GmBH, Berlin, Germany)
Spectral Range: 380 – 720nm
Viewing Geometry: d/8°
Armamentarium
• Standard Black and White Backgrounds
Formula for color difference
• ∆E = [(L w– L b)2 + (a w– a b)2 + (b w– b b)2] ½
• ∆L = L w– L b
• ∆a = a w– a b
• ∆b = b w– bb
Clinically significant color
differences
• ∆E > 3.7 : Very Poor match (Johnston and Kao, 1989)
• ∆E > 2
: Clinically not acceptable (Touati et al, 1993)
• ∆E ≤ 2
: Clinically acceptable (O‘Brien et al, 1990)
• ∆E < 1
: Not appriciable (Kuehni and Marcus, 1990)
Results
Empress2 ∆L
16
14
14
12
12
10
10
8
8
6
6
4
CEMENT
2
0
-2
N=
15
100
CORE
15
300
DLWCBC
4
15
500
Compolut
2
GIC
0
-2
ZnPO
N=
5
5
100
CORE
5
5
5
300
5
5
5
500
5
Empress2 ∆a
5,0
5
4,5
4,0
3
3,5
2
3,0
1
2,5
15
15
DAWCBC
4
100
300
500
0
-1
N=
CORE
15
CEMENT
2,0
Compolut
1,5
1,0
GIC
ZnPO
N= 5 5 5
100
CORE
5 5 5
5 5 5
300
500
Empress2 ∆b
14
12
12
10
8
10
6
8
4
2
0
-2
N=
15
15
100
300
CORE
DBWCBC
6
15
500
CEMENT
4
Compolut
2
GIC
0
ZnPO
N= 5 5 5
100
CORE
5 5 5
5 5 5
300
500
Empress2 ∆E
20
20
10
10
0
N=
DEWCBC
CEMENT
15
15
15
100
300
500
CORE
42
Compolut
GIC
0
N =5 5 5
100
CORE
ZnPO
5 5 5
5 5 5
300
500
Inceram Alumina ∆l 1,40mm
7
6
6
5
5
4
4
3
3
2
2
1
0
DLWCBC
CEMENT
15
15
15
al1
al2
al4
N=
CORE
1
Compolut
0
GIC
-1
ZnPO
N= 5 5 5
al1
CORE
5 5 5
5 5 5
al2
al4
Inceram Alumina ∆a 1,40mm
2,5
2,8
2,6
2,0
2,4
2,2
1,5
2,0
1,8
DAWCBC
CEMENT
1,6
1,4
1,2
1,0
N=
15
15
15
al1
al2
al4
CORE
1,0
Compolut
GIC
,5
ZnPO
N= 5 5 5
al1
CORE
5 5 5
5 5 5
al2
al4
6
8
5
6
4
4
62
3
65
2
0
-2
N=
DBWCBC
DBWB
Inceram Alumina ∆b 1,40mm
15
15
15
al1
al2
al4
CEMENT
2
74
1
Compolut
GIC
0
ZnPO
N= 5 5 5
al1
CORE
CORE
5 5 5
5 5 5
al2
al4
Inceram Alumina ∆E 1,40mm
8
10
7
8
6
5
6
4
3
DEWCBC
4
2
0
N=
15
15
15
al1
al2
al4
CORE
CEMENT
2
Compolut
1
0
GIC
ZnPO
N= 5 5 5
al1
CORE
5 5 5
5 5 5
al2
al4
Inceram Alumina ∆l 1,00mm
16
14
12
12
10
10
8
8
6
DLWCBC
6
4
2
N=
15
15
15
al1
al2
al4
CORE
CEMENT
4
Compolut
2
GIC
0
-2
ZnPO
N= 5 5 5
al1
CORE
5 5 5
5 5 5
al2
al4
Inceram Alumina ∆a 1,00mm
3,5
3,0
3,0
2,5
2,5
2,0
2,0
1,5
1,5
,5
46
0,0
N=
DAWCBC
CEMENT
1,0
15
15
15
al1
al2
al4
CORE
1,0
Compolut
,5
GIC
0,0
ZnPO
N= 5 5 5
al1
CORE
5 5 5
5 5 5
al2
al4
Inceram Alumina ∆b 1,00mm
10
9
8
8
7
6
6
4
DBWCBC
5
4
3
N=
15
15
15
al1
al2
al4
CORE
CEMENT
Compolut
2
GIC
0
ZnPO
N= 5 5 5
al1
CORE
5 5 5
5 5 5
al2
al4
Inceram Alumina ∆E 1,00mm
16
14
14
12
12
10
10
8
8
6
4
N=
DEWCBC
6
15
15
15
al1
al2
al4
CEMENT
4
Compolut
2
0
GIC
ZnPO
N= 5 5 5
al1
CORE
CORE
5 5 5
5 5 5
al2
al4
Correlation: ∆Lwb and ∆Ebcwc
(of translucency with the color change due to luting agent)
• Pearsons correlation (r):
Compolute = 0.13 p = 0.38
GIC
= 0.05 p = 0.76
ZnPO
= 0.82 p = 0.00
0.21 ±0.05 mm
0.24 ±0.04 mm
0.24 ±0.10 mm
Cements: ZnPO, GIC, RLA
60
4
50
3
40
2
30
1
DAWB
DLWB
20
10
0
N=
10
10
10
compolut
GIC
ZnPO
CEMENT
0
-1
N=
10
10
10
compolut
GIC
ZnPO
CEMENT
Cements: ZnPO, GIC, RLA
60
18
16
50
14
12
40
10
8
30
4
DEWB
DBWB
6
2
0
N=
10
10
10
compolut
GIC
ZnPO
CEMENT
20
10
N=
10
10
10
compolut
GIC
ZnPO
CEMENT
Conclusions
• All-Ceramics due to their translucency
have an effect of the luting agents and
background shade (dentine/discolored
tooth/post) on the final shade
• The two All-Ceramics examined showed a
shift in the the ∆a values due to black
background (shift towards red) (reflection curves at
various wavelengths to be investigated)
Conclusions
• As ceramic thickness increases the effect of
luting agent and background decreases
• Depending on the luting agent the
background shade can be partially masked
• Luting agents have an effect on the final
color
Conclusions
• The outcome of the ceramic restorations
cannot be predicted with accuracy
• Not only the color, that is percieved by the
eye is important but also the optical
properties of the materials shoud be
studied for predicting the outcome of the
all ceramic restorations
Future Work
Model for Color Prediction using KubelkaMunk theory and Artificial Neural Networks
for all ceramic restorations
Kubelka-Munk theory
• color mixing model which describes the
reflectance and transmittance of a color
sample with respect to the absorption and
scattering spectra of the material
• mathematical model used to describe the
reflectance
• considers the absorption and scattering in
a colored sample of fixed thickness
Kubelka-Munk theory
• four factors:
–
–
–
–
an absorption spectrum K(λ )
a scattering spectrum S(λ)
the sample thickness X
the reflectance spectrum of the substrate or
backing Rp(λ )
Kubelka-Munk (KM) theory
• Has been used to measure the reflectance
of All-Ceramic materials (Miyagawa and Powers, (1982);
Woolsey, G. D., W. M. Johnston, et al. (1984); Cook and McAree, (1985);
......................................... Davis, B. K., W. M. Johnston, et al. (1994))
• “The data on the absorption/scattering
coefficient ratio (K/S values) at certain
wavelengths are necessary for the creation
of a computer database and as well as for
the computer color prescription” (Paravina R.D,
(1999) )
Artificial Neural Network
(ANN)
• The ANN technology is a computer system
solution with a surprising capacity to learn
from input data
• computer-based algorithms which are
modeled on the structure and behaviour of
neurons in the human brain and can be
trained to recognize and categorize
complex patterns.
Artificial Neural Network
(ANN)
• Neural networks are well suited for data
mining tasks due to their ability to model
complex, multi-dimensional data
• Some applications of ANN.
Stock market prediction
Weather prediciton
Speech recognition
Face recognition.........................
Artificial Neural Network
(ANN)
Threshold Logical Unit
Artificial Neural Network
Feed forward fully
connected back
propagation
algorithm for weight
adjustments
CIELab for ANN ??
• ADVANTAGES:
– Easier access to CIELab data
– Already existing databases
• DISADVANTAGES:
– More experimental work required
– Does not predict the reflectance spectra at
various thickness
Software engineering
Waterfall Model
ColPres (Color Prescription)
•
•
•
•
Development of an algorithm
Development of test Database (MySQL)
Testing the algorithm
Development of a Complete Database
(MySQL)
• Full implementaion of the algorithm (Java)
Clinical Implication of ColPres
ShadeEye-NCC™
Clinical Implication of ColPres
Million dollar Smile
Thank you for your attention.