Predicting the Glaucomatous Central 10-Degree Visual Field From Optical Coherence Tomography Using Deep Learning and Tensor Regression

Xu, Linchuan; Asaoka, Ryo; Kiwaki, Taichi; Murata, Hiroshi; Fujino, Yuri; Hashimoto, Yukihiko; Asano, Shotaro; Miki, Atsuya; Mori, Kazuhiko; Ikeda, Yoko; Kanamoto, Takashi; Yamagami, Junkichi; Inoue, Kenji; Tanito, Masaki; Yamanishi, Kenji

doi:10.1016/j.ajo.2020.04.037

Cited by 25 publications

(18 citation statements)

References 41 publications

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“…The results of our study suggest that in the cross-sectional prediction of VF (HFA 10-2 test), the largest prediction error is observed with the MLR model (12.1 dB on average; Fig 5). 20,36 This may be because the relationship between structures, such as the thickness of the RNFL and GCC, and function has been known to be nonlinear. 13,37 Conversely, in the SVR model, regression is performed in a latent space (kernel plane); hence, no discrimination exists between linear and nonlinear.…”

Section: Discussionmentioning

confidence: 99%

“…13,44 This tendency was in agreement with our previous studies. 20,36 Sequentially, further improvement is required in the prediction accuracy in other regions.…”

Section: Discussionmentioning

confidence: 99%

“…18 We recently reported the benefits of applying DL to OCT measurements regarding various aspects of VF, such as the diagnosis of glaucoma 19 and the cross-sectional prediction of the HFA 10-2 test. 20 Moreover, we recently proposed the use of deeply regularized latent space linear regression (DLLR), in which the DL component contributes to improving the prediction accuracy of VF (HFA 24-2 test) progression. 21 Because both of these tasks (i.e., the crosssectional prediction of the HFA 10-2 test results and the progression prediction of the HFA 24-2 test results) use DL to obtain knowledge of the transforming information from the OCT domain to the VF domain, it may be advantageous to apply the knowledge learned in one of the tasks to another task.…”

mentioning

confidence: 99%

“…4. Convolutional neural network and tensor regression (CNN-TR): At each test point, the VF (HFA 10-2 test) threshold was predicted by applying CNN-TR20 to the GCC, RNFL, and OSþRPE thicknesses (224 Â 224 pixels with 3 channels).…”

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confidence: 99%

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A Joint Multitask Learning Model for Cross-sectional and Longitudinal Predictions of Visual Field Using OCT

Asaoka

Murata

et al. 2021

Ophthalmology Science

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Purpose: We constructed a multitask learning model (latent space linear regression and deep learning [LSLR-DL]) in which the 2 tasks of cross-sectional predictions (using OCT) of visual field (VF; central 10 ) and longitudinal progression predictions of VF (30 ) were performed jointly via sharing the deep learning (DL) component such that information from both tasks was used in an auxiliary manner (The Association for Computing Machinery's Special Interest Group on Knowledge Discovery and Data Mining [SIGKDD] 2021). The purpose of the current study was to investigate the prediction accuracy preparing an independent validation dataset.Design: Cohort study.Participants: Cross-sectional training and testing data sets included the VF (Humphrey Field Analyzer [HFA] 10-2 test) and an OCT measurement (obtained within 6 months) from 591 eyes of 351 healthy people or patients with open-angle glaucoma (OAG) and from 155 eyes of 131 patients with OAG, respectively. Longitudinal training and testing data sets included 7984 VF results (HFA 24-2 test) from 998 eyes of 592 patients with OAG and 1184 VF results (HFA 24-2 test) from 148 eyes of 84 patients with OAG, respectively. Each eye had 8 VF test results (HFA 24-2 test). The OCT sequences within the observation period were used.Methods: Root mean square error (RMSE) was used to evaluate the accuracy of LSLR-DL for the crosssectional prediction of VF (HFA 10-2 test). For the longitudinal prediction, the final (eighth) VF test (HFA 24-2 test) was predicted using a shorter VF series and relevant OCT images, and the RMSE was calculated. For comparison, RMSE values were calculated by applying the DL component (cross-sectional prediction) and the ordinary pointwise linear regression (longitudinal prediction).Main Outcome Measures: Root mean square error in the cross-sectional and longitudinal predictions.Results: Using LSLR-DL, the mean RMSE in the cross-sectional prediction was 6.4 dB and was between 4.4 dB (VF tests 1 and 2) and 3.7 dB (VF tests 1e7) in the longitudinal prediction, indicating that LSLR-DL significantly outperformed other methods.Conclusions: The results of this study indicate that LSLR-DL is useful for both the cross-sectional prediction of VF (HFA 10-2 test) and the longitudinal progression prediction of VF (HFA 24-2 test). Ophthalmology

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Section: Discussionmentioning

confidence: 99%

“…13,44 This tendency was in agreement with our previous studies. 20,36 Sequentially, further improvement is required in the prediction accuracy in other regions.…”

Section: Discussionmentioning

confidence: 99%

mentioning

confidence: 99%

mentioning

confidence: 99%

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A Joint Multitask Learning Model for Cross-sectional and Longitudinal Predictions of Visual Field Using OCT

Asaoka

Murata

et al. 2021

Ophthalmology Science

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“…These models have shown a high accuracy to extract and use the relevant information obtained from macular volumetric OCT scans and provide a corresponding simulation of central VF in glaucoma patients. [82][83][84] Moreover, Nouri-Mahdavi et al in a recent study showed that VF progression in moderate to advance glaucoma can be partly predicted using combined OCT measurements of peripapillary and macular areas. Of note, they developed and compared separate models using macular or peripapillary measurements and showed that macular models performed better than peripapillary models to detect VF progression.…”

Section: Applications Of Artificial Intelligence (Ai)mentioning

confidence: 99%

Macular Optical Coherence Tomography Imaging in Glaucoma

Kamalipour

Moghimi

2021

JOVR

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The advent of spectral-domain optical coherence tomography has played a transformative role in posterior segment imaging of the eye. Traditionally, images of the optic nerve head and the peripapillary area have been used to evaluate the structural changes associated with glaucoma. Recently, there is growing evidence in the literature supporting the use of macular spectral-domain optical coherence tomography as a complementary tool for clinical evaluation and research purposes in glaucoma.

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VF-HM: Vision Loss Estimation Using Fundus Photograph for High Myopia

Yan,

Liang,

et al. 2023

Lecture Notes in Computer Science

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Predicting the Glaucomatous Central 10-Degree Visual Field From Optical Coherence Tomography Using Deep Learning and Tensor Regression

Cited by 25 publications

References 41 publications

A Joint Multitask Learning Model for Cross-sectional and Longitudinal Predictions of Visual Field Using OCT

A Joint Multitask Learning Model for Cross-sectional and Longitudinal Predictions of Visual Field Using OCT

Macular Optical Coherence Tomography Imaging in Glaucoma

VF-HM: Vision Loss Estimation Using Fundus Photograph for High Myopia

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