Presentation of our abstract included in the Cornea Society / EBAA 2010 Fall Educational Symposium. Complete set of notes here: http://www.hailabs.com/2010/10/specular-microscopy-series-effect-of-true-area-sample-size-on-variability-in-endothelial-cell-density/

1. Effect of True Area Sample Size
on Variability in Endothelial Cell Density
(ECD) Results
Jackie Hai and Vivian Xue
HAI Laboratories, Inc.

2. Purpose
 Evaluate the effect of sample area size on
ECD measurement results and variability
 Determine a recommended minimum
sample size by comparing the results of
cell counts performed within larger areas
vs smaller areas

3. Sample Selection Criteria
 True area required for all samples
 Images captured at high resolution
 Low instances of pathology or abnormality
 Random selection with variety of ECD,
polymegathism, pleomorphism within
normal range

4. Untrue Area: excessive concave curvature (circled)

5. Untrue Area: incorrect focal depth (cells out of focus)

6. True Area: flat, focused image with clear cell borderlines

7. True Area: flat, focused image with clear cell borderlines

8. Low Resolution Thumbnails
(160x120 pixels)

9. Sample Area: 39981.10μm2
Cells Counted: 113
Density: 2826
High Resolution Analysis
(640x480 pixels)

10. Sample Area: 44843.25μm2
Cells Counted: 113
Density: 2520
High Resolution Analysis
(640x480 pixels)

11. Sample Selection Criteria
 True area required for all samples
 Images captured at high resolution
 Low instances of pathology or abnormality
 Random selection with variety of ECD,
polymegathism, pleomorphism within
normal range

12. Counting Method
 Variable frame counting method
 Single-field (40,000μm2
– 60,000μm2
)
 Multiple-field (20,000μm2
– 25,000μm2
)

13. Single-field variable frame count (60,000μm2
)
HAI CAS / EB 2.10

14. Multiple-field variable frame count 1 of 3 (23,000μm2
)
HAI CAS / EB 2.10

15. Multiple-field variable frame count 2 of 3 (22,000μm2
)
HAI CAS / EB 2.10

16. Multiple-field variable frame count 3 of 3 (22,500μm2
)
HAI CAS / EB 2.10

17. Results
 ECD ranged from 1700 – 3500 cells/mm2
 Result groupings:
 Class A (large samples): 40,000μm2
- 70,000μm2
 Class B (small samples): 20,000μm2
- 25,000μm2
 Standard Deviation:
 Class A (large samples): 6.27-63.11 (mean 26.3)
 Class B (small samples): 17.22-155.32 (mean 75.0)

19. Results
 Standard Error (S.D. / √N):
 Class A (large samples): 3.14-31.56 (mean 13.2)
 Class B (small samples): 9.94-89.67 (mean 43.3)
 Precision (S.E. / Mean):
 Class A (large samples): 0.0052
 Class B (small samples): 0.0156

21. Results
 In every instance:
Class A Class B
(40,000–70,000μm2
) < (20,000–25,000μm2
)
Mean ECD Mean ECD
 RPD ranged from 3.7% – 13.2%
 Mean RPD of 7.9%

22. Conclusion
 Larger sample sizes (>40,000μm2
) yielded
lower standard error and more precise cell
count results
 Cell counts performed in smaller sample
sizes produce significantly higher ECD
than larger sample sizes

23. Discussion
Rate of Change 1,000,000__
in Cell Density = Sample Area
(cells per mm2
) (in μm2
)

25. Discussion
 Sample size was based on area
 Samples based solely on # of cells yield
smaller sample size in high-density cornea
 In smaller samples, ECD increases at a
higher rate for every extra cell counted

26. Discussion
 Smaller sample sizes compound
possibility for increased margin of error
 Multiple small samples have longer total
perimeter than a single large sample
 Human error factors
 Image quality
 Cell identification
 Tracing of cell boundaries

27. Recommendations
1. Capture images at high resolution and maximum
screen size (640 x 480 pixels).
2. Select the largest sample area possible on the
screen for cell counting.
3. If there are folds or distortions, select the largest
contiguous true area possible.
4. For high resolution images free of distortions, a
minimum selection of 50,000μm2
should always
be possible.