Predicting Humphrey 10-2 visual field from 24-2 visual field in eyes with advanced glaucoma.
Sugisaki Kenji, Asaoka Ryo, Inoue Toshihiro, Yoshikawa Keiji, Kanamori Akiyasu, Yamazaki Yoshio, Ishikawa Shinichiro, Nemoto Hodaka, Iwase Aiko, Araie Makoto
AI Summary
This study found that machine learning (SVR) can predict central 10-2 visual fields from 24-2 fields in advanced glaucoma with about 25% error, aiding central vision assessment.
Abstract
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%.
MeSH Terms
Shields Classification
Key Concepts5
Support vector regression (SVR) provided the smallest absolute prediction error (PredError) of 4.01.5 dB among tested methods when predicting Humphrey Field Analyzer Central 10-2 (HFA 10-2) results from HFA 24-2 results in 44 eyes (44 patients) with open advanced glaucoma.
Support vector regression (SVR) predicted retinal sensitivity at Humphrey Field Analyzer Central 10-2 (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% in 44 eyes (44 patients) with open advanced glaucoma.
The error range for predicting Humphrey Field Analyzer Central 10-2 (HFA 10-2) test points using support vector regression (SVR) from HFA 24-2 results was approximately twice the test-retest variability of the HFA 10-2 results in 44 eyes (44 patients) with open advanced glaucoma.
The mean (SD) test-retest variability of the HFA 10-2 results in 44 eyes (44 patients) with open advanced glaucoma was 2.11.0 dB.
The bilinear interpolation (IP) method yielded a prediction error (PredError) of 5.01.7 dB when predicting Humphrey Field Analyzer Central 10-2 (HFA 10-2) results from HFA 24-2 results in 44 eyes (44 patients) with open advanced glaucoma.
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