Table 1. .
Frisén Scale |
Sum |
3-D SD-OCT Scan Acquisition* |
SD-OCT Scanner Analysis† |
Subsequent Analyses‡ |
||||||
Include |
Exclude |
Exclude Rate |
Include |
Exclude |
Exclude Rate |
Include |
Exclude |
Exclude Rate |
||
0 | 5 | 5 | 0 | 0% | 5 | 0 | 0% | 5 | 0 | 0% |
1 | 26 | 25 | 1 | 4% | 24 | 2 | 8% | 23 | 3 | 12% |
2 | 33 | 28 | 5 | 15% | 24 | 9 | 27% | 21 | 12 | 36% |
3 | 10 | 8 | 2 | 20% | 5 | 5 | 50% | 5 | 5 | 50% |
4 | 12 | 5 | 7 | 58% | 1 | 11 | 92% | 1 | 11 | 92% |
Total | 86 | 71 | 15 | 17% | 59 | 27 | 31% | 55 | 31 | 36% |
In this study, the 3-D segmentation approach needs a complete 3-D volume acquired within the confines of the z-axis window, so the reason for the 3-D OCT data exclusions was that these scans were incomplete, meaning that some parts of the ILM or RPE were cut off during acquisition of the entire SD-OCT volume, due to operator error.
The SD-OCT scanner algorithm (Zeiss Cirrus) for segmenting the RNFL fails more frequently with greater severity scale of papilledema due to the distortion of tissue boundaries used to find the borders of the RNFL.
In the subsequent analyses, the data were the intersection of * and †, which means valid eyes in this dataset were those in which the propriety Zeiss algorithm for determining RNFL thickness did not fail and those where the SD-OCT volume scans were not truncated during acquisition.