期刊
SCIENTIFIC REPORTS
卷 11, 期 1, 页码 -出版社
NATURE RESEARCH
DOI: 10.1038/s41598-020-79494-6
关键词
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资金
- Ministry of Education, Culture, Sports, Science and Technology of Japan [19H01114, 18KK0253, 20K09784]
- Translational Research program
- Japan Agency for Medical Research and Development (AMED)
- Japan Science and Technology Agency
- Suzuken Memorial Foundation
- Mitsui Life Social Welfare Foundation
- Grants-in-Aid for Scientific Research [18KK0253, 20K09784, 19H01114] Funding Source: KAKEN
A convolutional neural network model was trained to predict visual field in glaucoma patients using OCT images and HFA tests. Predictions were made based on OCT data and adjusted with HFA results, resulting in improved accuracy.
We aimed to develop a model to predict visual field (VF) in the central 10 degrees in patients with glaucoma, by training a convolutional neural network (CNN) with optical coherence tomography (OCT) images and adjusting the values with Humphrey Field Analyzer (HFA) 24-2 test. The training dataset included 558 eyes from 312 glaucoma patients and 90 eyes from 46 normal subjects. The testing dataset included 105 eyes from 72 glaucoma patients. All eyes were analyzed by the HFA 10-2 test and OCT; eyes in the testing dataset were additionally analyzed by the HFA 24-2 test. During CNN model training, the total deviation (TD) values of the HFA 10-2 test point were predicted from the combined OCT-measured macular retinal layers' thicknesses. Then, the predicted TD values were corrected using the TD values of the innermost four points from the HFA 24-2 test. Mean absolute error derived from the CNN models ranged between 9.4 and 9.5 B. These values reduced to 5.5 dB on average, when the data were corrected using the HFA 24-2 test. In conclusion, HFA 10-2 test results can be predicted with a OCT images using a trained CNN model with adjustment using HFA 24-2 test.
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