# Presentation: 253738

## USE OF THE KONAN NONCON-ROBO SPECULAR MICROSCOPE IN CLINICAL RESEARCH

***Henry F. Edelhauser, Ph.D.***
***Ramzy G. Azar, MPH***
***Emory University Eye Center***
***Atlanta, Georgia***

## Purpose

- Understand variability issues with specular microscopy that may bias results

**Notes:**
Sock wave, mechanical trauma, injury, sound waves...etc

## Objectives

- Provide examples of good and poor photography
- Illustrate variability in specular microscopy photography and analysis
- Illustrate variability within a single image

## What is a Good Image?

- Distinct cells
- Can identify at least 150 cells
- Cells can be grouped in a uniform area
- What may be good for clinical purposes may not be good research

## Things to Consider That Affect Quality of Image

- Dry eye
- Contact lens use
- Wrong Specular Manual Settings
- Keratoconus
- Patient Compliance
- Age
- Training, experience of photographer

## Poor Quality Images

## Poor Quality Images Continued...

## Poor Quality Images Continued...

## Conditions that Potentially Increase Variability

- Guttata (Fuch’s dystrophy)
- Polymegethism/Pleomorphism
- Injury
- Low Cell density (Huge cells)

## Guttata (Fuch’s dystrophy)

## Capturing the Best Image Possible

- Make sure Pt is comfortable
- Instruct Pt to blink
- Instruct Pt not to move and to open eyes wide
- Instruct Pt to focus on the green light
- Be patient
- Use Manual setting to improve quality when cornea is unusually thicker than normal

## Things to Consider When Analyzing Images

- Locate the best and most representative area
- Number of cells
- Quality of Cells
- No shadows
- Disease
- Use area with the fewest distortions
- Blurring
- Washed-out images
- Shadows

## Locating the Best Analysis Area (Sample Images)

## Dotting Cells

- Dot all Cells at the Center
- Remain accurate and consistent throughout
- Dot 150 cells
- Grouping is important

## Where to Group the Analysis?

## What is Wrong With This Analysis?

- Analysis is not representative
- Introducing Bias
- Not likely to repeat
- Not enough cells counted

## Grouping Details

- Easy
- Clear
- No shadows
- Dot 150 cells

- Need Good rep.
- Take more time
- Dot > 150

**Normal**

**Polymegathism**

## Grouping your Analysis

- Correct Grouping
- Concentric
- Even
- Uniform

- Incorrect Grouping
- linear
- uneven
- Winding

## Cell Grouping - Guttata

- Group only in one area

## To Analyze the Cells:

- You need to be able to visualize cells
- Find a pattern
- Identifying
- Cells vs
- Damage vs
- Shadows

## Where an Image is Analyzed Can Create Variability

## Examples of Variability

**CD = 2873**
**SD = 170**
**CV = 48**
**6A = 53**

**CD = 2976**
**SD = 113**
**CV = 33**
**6A = 53**

- _ΔCD = 103_
- _(4%)_

## Examples of Variability Within Readers 

**CD = 2531**
**SD = 139**
**CV = 35**
**6A = 55**

**CD = 2358**
**SD = 222**
**CV = 52**
**6A = 56**

- _ΔCD = 173_
- (7%)

## Examples of Variability Between Readers

- Analysis
- Repeated 4x

- #1 - 2631
- #2 - 2557
- #3 - 2531
- #4 - 2570
- #5 - 2624

**Range 2531 - 2631**

## Consequences of Under or Over Counting

## Endothelial Cell Density

**Notes:**
Sock wave, mechanical trauma, injury, sound waves...etc

## Precision of 36 Robo corneal endothelial specular images of each eye (OD, OS) taken on 18 different days and analyzed with the Robo software

|  | N | Cell Density | Precision |
| --- | --- | --- | --- |
| OD | 18 | 2545 ± 45 cells/mm2 | (1.7%) |
| OS | 18 | 2600 ± 41 cells/mm2 | (1.5%) |

- (From AJO 125:465-471, 1998 LASIK Paper)

## Age Dependent Cell Density Variation Within 3 Different Corneal Regions

**Notes:**
Sock wave, mechanical trauma, injury, sound waves...etc

## Sources of Variability Summary

- Difficult to return to same location      (1 mm =  56 cells/mm2  - 2.0%)
- Poor image quality (minimal # of analyzable cells = 100)
- Technician error (Training/consistency)
- Reader analysis (Training/consistency)
- Equipment calibration/alignment

## Data Flow Chart

- FDA