The usefulness of the Deep Learning method of variational autoencoder to reduce measurement noise in glaucomatous visual fields

• Published in: Scientific reports 2020-05, Vol.10 (1), p.7893-7893, Article 7893
• Main Authors: Asaoka, Ryo, Murata, Hiroshi, Asano, Shotaro, Matsuura, Masato, Fujino, Yuri, Miki, Atsuya, Tanito, Masaki, Mizoue, Shiro, Mori, Kazuhiko, Suzuki, Katsuyoshi, Yamashita, Takehiro, Kashiwagi, Kenji, Shoji, Nobuyuki
• Format: Article
• Language: English
• Subjects: 631/114/1314; 692/308/575; Adult; Datasets; Deep Learning; Disease Progression; Eye; Eye - physiopathology; Female; Glaucoma; Glaucoma, Open-Angle - diagnosis; Glaucoma, Open-Angle - physiopathology; Humanities and Social Sciences; Humans; Information processing; Intraocular Pressure - physiology; Linear Models; Male; Middle Aged; multidisciplinary; Noise reduction; Reproducibility of Results; Science; Science (multidisciplinary); Sensitivity and Specificity; Visual field; Visual Field Tests - methods; Visual Field Tests - statistics & numerical data; Visual Fields - physiology
• ISSN: 2045-2322; 2045-2322
• DOI: 10.1038/s41598-020-64869-6

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 VAE ). In testing dataset 2, the mTD value of the tenth VF was predicted using shorter series of VFs. A similar calculation was carried out using a weighted linear regression where the weights were equal to the absolute difference between mTD and mTD VAE . In testing dataset 1, there was a significant relationship between the difference between mTD and mTD VAE from the first VF and the difference between mTD in the first and second VFs. In testing dataset 2, mean squared prediction errors with the weighted mTD trend analysis were significantly smaller than those form the unweighted mTD trend analysis.