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Z.E. Burgansky, G. Wollstein, R.A. Bilonick, H. Ishikawa, L. Kagemann, J.S. Schuman; Detecting Glaucoma With Heidelberg Retina Tomograph 3 (HRT 3) . Invest. Ophthalmol. Vis. Sci. 2006;47(13):3630.
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© ARVO (1962-2015); The Authors (2016-present)
The newly released HRT 3 software uses a larger normative database than the HRT II software for classification of measured parameters. The software also provides stereometric disc measurements without the need of subjectively defining the disc margin. A neural network technique (Glaucoma probability score, GPS) is applied to these measurements for classification. In this study we investigated the capability of HRT 3 software in terms of discriminating glaucoma from healthy subjects in comparison with HRT II.
50 eyes of 50 glaucoma patients and 71 eyes of 71 healthy volunteers were enrolled. All patients underwent fundus photo, visual field testing (SITA), and HRT II scanning within a 6 month interval. HRT II data was analyzed by HRT 3 software without modifying the disc margin. The gold standard diagnosis was established by combining SITA and disc appearance criteria. Areas under the receiver operating characteristic curves (AROCs) were computed for assessment of glaucoma discrimination capabilities of stereometric parameters and classification by Moorefields regression analysis (MRA) with HRT II and HRT 3, and classification by GPS.
The average visual field mean deviation (MD) was –6.03±5.78 dB in the glaucoma group and –0.46±0.96 dB in the healthy group. The best performing stereometric parameters were linear cup/disk ratio (AROC=0.900) for standard HRT 3 and horizontal RNFL curvature (0.910) for HRT 3 GPS. Classification by HRT 3 MRA achieved the best discrimination (AROC 0.934) followed by HRT II MRA (0.927), and GPS (0.862). The differences between the AROCs of HRT II and HRT 3 MRA was not significant (p=0.7, DeLong analysis). HRT 3 MRA AROC was significantly greater than the GPS AROC (p=0.046).
HRT 3 MRA showed the best glaucoma discrimination capability. GPS demonstrated lower discrimination performance despite the fact that it eliminated an operator dependent factor, which is considered to be a significant source of measurement variability.
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