ArticleScientific reports2020
The usefulness of the Deep Learning method of variational autoencoder to reduce measurement noise in glaucomatous visual fields.
Article in Scientific reports, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Prediction of visual field progression in glaucoma: existing methods and artificial intelligence.Japanese journal of ophthalmology · 2023Review
- Association between prenatal alcohol exposure and children's facial shape: a prospective population-based cohort study.Human reproduction (Oxford, England) · 2023Article
- Survey on electronic visual field data transfer practices among Japan Glaucoma Society board members.BMC ophthalmology · 2023Article
- Use of artificial intelligence in forecasting glaucoma progression.Taiwan journal of ophthalmologyReview
- Efficacy of Smoothing Algorithms to Enhance Detection of Visual Field Progression in Glaucoma.Ophthalmology scienceArticle
Corrections and comments
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Authors and funding
13 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The aim of the study was to investigate the usefulness of processing visual field (VF) using a variational autoencoder (VAE). The training data consisted of 82,433 VFs from 16,836 eyes. Testing dataset 1 consisted of test-retest VFs from 104 eyes with open angle glaucoma. Testing dataset 2 was series of 10 VFs from 638 eyes with open angle glaucoma. A VAE model to reconstruct VF was developed using the training dataset. VFs in the testing dataset 1 were then reconstructed using the trained VAE and the mean total deviation (mTD) was calculated (mTD
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