1. Spectrophotometric analysis of two All-Ceramic materials Varun Singh Barath University of Cologne, Germany

2. Dilemma

3. 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

4. John Greenwood

5. 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

6. 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

7. Spectrophotometric analysis of two All-Ceramic materials with the effect of the background shade on the final shade

8. 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

9. Some aspects of Color

10. 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

11. CIELAB system Courtesy: Handprint media

12. 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);

13. CIELAB system Courtesy: Handprint media

14. 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

16. 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

17. Armamentarium • Ceramic samples as clinical units In-Ceram Alumina, 1,0 mm In-Ceram Alumina, 1,4 mm Empress 2, 1,4 mm

18. Armamentarium • Cements

19. Armamentarium • Micrometer (Mitutoyo, Japan)

20. Armamentarium • Sample Preparation (Simulating a clinical All-Ceramic restoration)

21. Armamentarium • Spectrophotometer (Dr. Lange GmBH, Berlin, Germany) Spectral Range: 380 – 720nm Viewing Geometry: d/8°

22. Armamentarium • Standard Black and White Backgrounds

23. 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

24. 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)

25. Results

26. Empress2 ∆L

27. Empress2 ∆a

28. Empress2 ∆b

29. Empress2 ∆E

30. Inceram Alumina ∆l 1,40mm

31. Inceram Alumina ∆a 1,40mm

32. Inceram Alumina ∆b 1,40mm

33. Inceram Alumina ∆E 1,40mm

34. Inceram Alumina ∆l 1,00mm

35. Inceram Alumina ∆a 1,00mm

36. Inceram Alumina ∆b 1,00mm

37. Inceram Alumina ∆E 1,00mm

38. Correlation:∆Lwb and ∆Ebcwc(of translucency with the color change due to luting agent) • Pearsons correlation (r): Compolute = 0.13 p = 0.38 0.21 ±0.05 mm GIC = 0.05 p = 0.76 0.24 ±0.04 mm ZnPO = 0.82 p = 0.00 0.24 ±0.10 mm

39. Cements: ZnPO, GIC, RLA

40. Cements: ZnPO, GIC, RLA

41. 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)

42. 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

43. 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

44. Future Work Model for Color Prediction using Kubelka-Munk theory and Artificial Neural Networks for all ceramic restorations

45. 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

46. 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 )

47. 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) )

48. 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.

49. 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.........................

50. Artificial Neural Network (ANN) Threshold Logical Unit