| Autor: |
Kenji Sugisaki, Ryo Asaoka, Toshihiro Inoue, Keiji Yoshikawa, Akiyasu Kanamori, Yoshio Yamazaki, Shinichiro Ishikawa, Hodaka Nemoto, Aiko Iwase, Makoto Araie |
| Zdroj: |
British Journal of Ophthalmology; May2020, Vol. 104 Issue 5, p642-647, 6p, 4 Diagrams, 3 Charts, 1 Graph |
| Abstrakt: |
Aims To predict Humphrey Field Analyzer Central 10-2 Swedish Interactive Threshold Algorithm- Standard test (HFA 10-2) results (Carl Zeiss Meditec, San Leandro, CA) from HFA 24-2 results of the same eyes with advanced glaucoma. Methods Training and testing HFA 24-2 and 10-2 data sets, respectively, consisted of 175 eyes (175 patients) and 44 eyes (44 patients) with open advanced glaucoma (mean deviation of HFA 24-2 ≤-20 dB). Using the training data set, the 68 total deviation (TD) values of the HFA 10-2 test points were predicted from those of the innermost 16 HFA 24-2 test points in the same eye, using image processing or various machine learning methods including bilinear interpolation (IP) as a standard for comparison. The absolute prediction error (PredError) was calculated by applying each method to the testing data set. results The mean (SD) test--retest variability of the HFA 10-2 results in the testing data set was 2.1±1.0 dB, while the IP method yielded a PredError of 5.0±1.7 dB. Among the methods tested, support vector regression (SVR) provided a smallest PredError (4.0±1.5 dB). SVR predicted retinal sensitivity at HFA 10-2 test points in the preserved 'central isle' of advanced glaucoma from HFA 24-2 results of the same eye within an error range of about 25%, while error range was approximately twice of the test--retest variability. Conclusion Applying SVR to HFA 24-2 results allowed us to predict TD values at HFA 10-2 test points of the same eye with advanced glaucoma with an error range of about 25%. [ABSTRACT FROM AUTHOR] |
| Databáze: |
Complementary Index |
| Externí odkaz: |
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