Recently, studies have been undertaken to evaluate the performance of glaucoma detection using Stratus OCT (Carl Zeiss Meditec, Inc.).
47 48 49 In our previous report, we differentiated between normal and glaucomatous eyes in a Taiwan Chinese population based on the summary data reports from Stratus OCT, by using a linear discriminant analysis with forward and backward selection to determine the best combination of parameters for discriminating between glaucomatous and healthy eyes.
49 In the present study, we increased the sample size and applied other automated classifiers to reconfirm OCT in diagnosing glaucoma. Our results showed that it is possible to differentiate between glaucomatous and normal eyes by analyzing the input parameters with Stratus OCT.
MD uses simple measures of descriptive statistics and is not probabilistic in nature. As far as we know, our study is the first to apply MD to differentiate glaucomatous from normal eyes. Taguchi and Rajesh
50 used MD to discriminate between a healthy group and those with liver disease. From their results, the MDs ranged from 0.3784 to 2.3581 in the healthy group and from 7.7274 to 135.6978 in the unhealthy groups. The classification rate in Taguchi et al was 100%. Su and Li
51 compared the classification performance of MD and ANN on liver disease. The correct classification rates were 95.52% and 89.55% by MD and ANN, respectively, in their study. In addition, Samek et al.
52 applied MD pattern recognition to evaluate laser-induced breakdown spectroscopy (LIBS) spectra recorded from teeth. They achieved close to 100% identification, and only one sample was misinterpreted. The accuracy of MD to differentiate glaucoma from normal eyes in our study was 97.66%.
A few studies have been conducted to evaluate the discriminant powers of OCT in diagnosing glaucoma. Sanchez-Galeana et al.
11 determined from OCT summary data reports that the sensitivity and specificity ranged from 76% to 79% and 68% to 81%, respectively, for discriminating between early to moderate glaucomatous and normal eyes. In a study by Greaney et al.,
7 four sectors in order of the most discriminating were identified by stepwise discriminating analysis: temporal to superior (45–75°), inferior (265–295°), temporal (345–15°) and superior to temporal (15–45°); they resulted in an area under the ROC curve 0.88. The area under the ROC curves for the earlier versions of the OCT ranged from 0.79 to 0.94, depending on the parameters and characteristics of the population evaluated.
53 54 55 56 In a study by Medeiros et al.,
47 the inferior quadrant thickness had the highest ROC area (0.92) among the total parameters. Our result also demonstrated that the maximum area under the ROC curve was 0.832 in inferior quadrant thickness. The most plausible reason that individual parameters from Stratus OCT were not good enough is that most patients in our glaucoma group had early glaucoma (mean deviation, −2.7 ± 1.9 dB). However, after the application of automated classifiers, the area under the ROC curve increased to 0.991.
There were some limitations in this cross-sectional study. First, the particular mathematical models that we chose are unlikely to be the only ones that could be applied. Comparisons among other classification methods should be made to yield the best models for improving the discriminant power of OCT. Second, comparisons across studies are very difficult, because of differences in population demographics and the definition and severity of glaucoma and because very few studies have been conducted to investigate RNFL thickness and disc topography together, using automated classifiers method. Third, the sample used in studies is usually a clinic-based population of patients with glaucoma. These patients have been identified on the basis of particular patterns of structural and functional abnormality that meet preconceived notions that bias the outcome of comparison.
57 Besides, the selection bias from OCT images, such as those with peripapillary atrophy or some optic disc shape that could not be analyzed by Stratus software version A 2.0, making those individuals poor candidates for OCT examination. Therefore, the selection bias did exist in this study. Furthermore, the Stratus OCT software in our laboratory was different from that used in other laboratories in Western countries. The A 2.0 version, unlike the A 3.1 version, does not have an internal normative database. Therefore, we were unable to calculate the interval LRs for each parameter. Instead, interval LRs were calculated for the six automated classifiers. The usefulness of a diagnostic test is influenced by the proportion of patients suspected of having the target disorder whose test results have high (>10) or very low (<0.1) LRs, thus greatly affecting the probability of disease.
58 As indicated in
Tables 5 6 and 7 , this proportion was 24% for LDA, 50% for LDA with PCA, 10% for MD, 92% for MD with PCA, 4% for ANN, and 42% for ANN with PCA. The proportion of large effects of MD with PCA were much higher than the effects of other classifiers. A multilevel LR for glaucoma of <0.1 with an MD of <2.0 indicates that MD < 2.0 almost exclusively occurred in the healthy group. A multilevel LR for glaucoma of 71 with an MD > 4.0 indicates that an MD > 4.0 occurs over 70 times more often in patients with glaucoma than in healthy persons. These data suggest that glaucoma subjects rarely have an MD < 2.0 and the healthy subjects almost never have an MD > 4.0
(Table 6) . Selection of other cutoffs may result in different proportions. Larger sample sizes would provide more precise and robust estimations of LRs using smaller intervals of the range of possible test values. However, our results can be used as the basis for further improving the diagnostic accuracy of glaucoma in the Taiwan Chinese population in the near future.
In summary, automated classifiers showed promise for differentiating glaucomatous from normal eyes in the Taiwan Chinese population, by using summary data from Stratus OCT. Although the result was good, clinicians should be cautious when accepting this classification as a reliable indicator of diagnosis of glaucoma and should integrate the Stratus OCT result into the entire clinical picture when diagnosing glaucoma.