Investigation of visual field employing Bayes and machine learning method
| Project/Area Number |
25861618
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| Research Category |
Grant-in-Aid for Young Scientists (B)
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| Allocation Type | Multi-year Fund |
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| Research Field |
Ophthalmology
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| Research Institution | The University of Tokyo |
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Principal Investigator |
MURATA Hiroshi 東京大学, 医学部附属病院, 助教 (80635748)
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| Co-Investigator(Renkei-kenkyūsha) |
ASAOKA Ryo 東京大学, 医学部附属病院眼科, 臨床講師 (00362202)
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| Project Period (FY) |
2013-04-01 – 2015-03-31
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| Project Status |
Completed (Fiscal Year 2014)
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| Budget Amount *help |
¥3,770,000 (Direct Cost: ¥2,900,000、Indirect Cost: ¥870,000)
Fiscal Year 2014: ¥910,000 (Direct Cost: ¥700,000、Indirect Cost: ¥210,000)
Fiscal Year 2013: ¥2,860,000 (Direct Cost: ¥2,200,000、Indirect Cost: ¥660,000)
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| Keywords | 緑内障 / 視野 / ベイズ統計 / 変分ベイズ / 平均場近似 / ベイズ線形回帰 / ゲイズトラック / 機械学習 |
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| Outline of Final Research Achievements |
Glaucoma is characterized by progressive visual field (VF) damage, which is irreversible, so it is important to predict VF progression in clinical use. In Humphrey Field Analyzer (HFA), which is commonly used in clinical settings, employs simple linear regression for analysis of VF data. However, it ignores the correlation between test points and so on.
We created a novel model for VF progression, and by means of learning the model using a lot of data we had, we were able to predict future VF more precisely than simple linear regression.
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Report
(3 results)
Research Products
(11 results)
