Fast visual field measurement using the variational Bayes linear regression
| Project/Area Number |
17K11418
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| Research Category |
Grant-in-Aid for Scientific Research (C)
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| Allocation Type | Multi-year Fund |
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| Section | 一般 |
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| Research Field |
Ophthalmology
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| Research Institution | The University of Tokyo |
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Principal Investigator |
Asaoka Ryo 東京大学, 医学部附属病院, 助教 (00362202)
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| Project Period (FY) |
2017-04-01 – 2020-03-31
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| Project Status |
Completed (Fiscal Year 2019)
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| Budget Amount *help |
¥4,680,000 (Direct Cost: ¥3,600,000、Indirect Cost: ¥1,080,000)
Fiscal Year 2019: ¥1,170,000 (Direct Cost: ¥900,000、Indirect Cost: ¥270,000)
Fiscal Year 2018: ¥1,430,000 (Direct Cost: ¥1,100,000、Indirect Cost: ¥330,000)
Fiscal Year 2017: ¥2,080,000 (Direct Cost: ¥1,600,000、Indirect Cost: ¥480,000)
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| Keywords | 視野 / 高速視野測定 / 深層学習 / 緑内障 |
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| Outline of Final Research Achievements |
eyes of 45 patients with open angle glaucoma. Visual field measurement was carried out using the conventional SITA standard algorithm and the variational Bayes linear regression model using . As a result, the measurement duration with the latter was 5.4±1.7 minutes which was significantly shorter than that with SITA standard.(p＜0.001)
Pre-training was conducted using 10-2 visual field of 772 eyes. Then, using optical coherence tomography measurements from 86 eyes of 43 normal subjects and 505 eyes of 304 patients with open angle glaucoma, 10-2 visual field was predicted using the convolutional neural network The prediction error with root mean squared error was 6.32 ± 3.76 dB.
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| Academic Significance and Societal Importance of the Research Achievements |
緑内障患者において、variational Bayes linear regressionを用いて視野を推測しつつ測定することで、無駄な視標提示を省き、視野を高速に測定することが可能となった。また１０－２視野を、光干渉断層計による網膜層厚から畳み込みニューラルネットワークを用いて推測することが可能となった。
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Report
(4 results)
Research Products
(35 results)
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[Journal Article] The usefulness of the Deep Learning method of variational autoencoder to reduce measurement noise in glaucomatous visual fields2020
Author(s)
8.Asaoka R (Corresponding author), Murata H, Asano S, Matsuura M, Fujino Y, Miki A, Tanito M, Mizoue S, Mori K, Suzuki K, Yamashita T, Kashiwagi K, Shoji N.
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Journal Title
Related Report
Peer Reviewed / Open Access / Int'l Joint Research
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[Journal Article] The usefulness of data augmentation for visual field trend analyses in glaucoma patients.2020
Author(s)
Asaoka R, Murata H, Matsuura M, Fujino Y, Miki A, Tanito M, Mizoue S, Mori K, Suzuki K, Yamashita T, Kashiwagi K, Nobuyuki Shoji N
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Journal Title
Br J Ophthalmol
Volume: in press
Related Report
Peer Reviewed / Int'l Joint Research
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[Journal Article] Using Deep Learning and transform learning to accurately diagnose early-onset glaucoma from macular optical coherence tomography images.2018
Author(s)
Asaoka R, Murata H, Hirasawa K, Fujino Y, Matsuura M, Miki A, Kanamoto T, Ikeda Y, Mori K, Iwase A, Shoji N, Inoue K, Yamagami J, Araie M.
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Journal Title
American Journal of Ophthalmlogy
Volume: 198
Pages: 136-145
DOI
Related Report
Peer Reviewed / Int'l Joint Research
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