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Clinical Specular Microscopy Corneal Endothelial Cell Morphology

Clinical Specular Microscopy Corneal Endothelial Cell Morphology. Bernard E. McCarey, Ph.D. Emory University Eye Center Atlanta, Georgia U.S.A. FDA 2001. Clinical Specular Microscopes Available. Contact Analysis Keller-Konan: SP-580 manual photo digitized

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Clinical Specular Microscopy Corneal Endothelial Cell Morphology

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    1. Clinical Specular Microscopy Corneal Endothelial Cell Morphology Bernard E. McCarey, Ph.D. Emory University Eye Center Atlanta, Georgia U.S.A.

    2. Clinical Specular Microscopes Available

    3. Clinical Specular Microscopes Available

    4. Corneal Endothelial Cell Morphology Cell Area ± S.D. (µm2) Cell Density (cells / mm2) Polymegethism (CV) Pleomorphism (% 6 sided)

    5. Corneal Endothelial Cell Morphology

    6. Corneal Endothelial Cell Density 350,000 cell / cornea at birth, 3000 - 4000 cell / mm2 at middle age, 2500 cell / mm2 at old age, 2000 cells / mm2 minimal acceptable, 1500 cell / mm2 potential corneal edema, 800 cells / mm2

    7. Corneal Endothelial Cell Polymegethism, CV Normal young adult, 0.27 to 0.28 Literature convention uses 27 to 28

    8. Endothelial Cell Morphology Changes Caused by Surgical Trauma Do localized changes effect other zones on the cornea? What is the chronological healing response? Can central Cell Density adequately document the trauma?

    9. Individual Healing Following Intraocular Surgery in the Human

    10. Serial Healing Following ICCE Matsuda, Suda and Manabe; Amer J Ophthalmol 98:313-319, 1984

    11. Serial Healing Following Keratoplasty Matsuda, Suda and Manabe; Amer J Ophthalmol 98:313-319, 1984

    12. Endothelial Cell Changes Caused by Contact Lenses Transient (bleb) Morphology Changes Chronic Morphology Changes Pleomorphism (%H) Polymegethism (CV)

    13. Polymegethism Endothelial Morphology

    14. Corneal Endothelial Cell Density Determination Comparison Method: compare to known “honey comb” pattern Frame Method: count the number of cell within a frame Corner Method: determine cell area from a polygon digitization by locating cell border intersections Center Method: determine cell area from adjacent polygon centers, “center to center”

    15. Frame Method Count all cells within a frame Adjust for cells extending outside of frame Count partial cells as full cells on 2 adjacent frame sides Convert cells counted per partial mm2 to cells / mm2

    16. Frame Method Accuracy Size of Frame: determines number of cells to be counted Decision on Partial Cells Decision on Cell Borders Blue Frame frame = 0.036 mm2 Yellow Frame frame = 0.018 mm2

    17. Center Method * Dot center of contiguous cells Ideally count in a circle In practice count in a rectangle.

    18. Center Method: off centered dots

    19. Center Method: omitted cells

    26. Effect of Number of Cells per Image verses Coefficient of Variation S.D. increases with increasing CV S.D. decreases with increasing number of cells counted S.D. stabilizes with >100 cells counted S.D. reflects an always present data spread Konan software max. count of 200 cells

    27. Center Method * Calculation

    28. Max. Number Countable in Cell Density Field Konan Non-Contact Specular Microscope

    29. Image Location with Konan Specular Microscope

    30. Image Location within a 4 mm Diameter Zone

    31. Image Location within a 3 mm Diameter Zone

    32. Image Location within a 2 mm Diameter Zone

    33. Image Location within a 1 mm Diameter Zone

    34. Image Location at Center

    35. Control Endothelial Cell Density Repeatability Range Control eyes from Medennium Corp. Clinical Trial A single Reading Center 58 subjects from 7 clinical sites in USA Subjects CV= 36 +/- 6 (mean +/- SD) Images captured at baseline and 3 months Repeatability was determined between each time period Paired t-test between baseline and 3 mo. was p=0.727

    36. Control Endothelial Cell Density Repeatability Range

    37. Control Endothelial Cell Density Repeatability Range

    38. Specular Microscopy Clinical Site Issues Clinical Trial Criteria Coordinator Experience Criteria for quality data Specular Microscope Model and software Experience Image Capture Ophthalmic photographer, dedicated to image capturing Ophthalmic technician, diverse patient care Technician, limited training Clinical locations Clinical Site Training

    39. Clinical Site Training All sites should have the same microscope model Automated non-contact specular microscope Preferably one technician per site Require and evaluate practice efforts central images of same individual multiple images to establish individual statistics Re-evaluate practice efforts over time Repeatability of skill is key to good data Individual training visit promotes uniformity of skills

    40. The End

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