Glaucoma Progression Prediction Using Retinal Thickness via Latent Space Linear Regression

Zheng, Yangong; Xu, Linchuan; Kiwaki, Taichi; Wang, Jing; Murata, Hiroshi; Asaoka, Ryo; Yamanishi, Kenji

doi:10.1145/3292500.3330757

Cited by 10 publications

(15 citation statements)

References 24 publications

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“…The definition of similarity between 2 eyes, z i;j , followed the Gaussian kernel, was considered to be reasonable in the field of kernel regression, 30 and worked well as demonstrated by our previous study. 21 By combining the LSLR and DL, the objective function for a test eye indexed by 0 was quantified as follows:…”

Section: Prediction Of Visual Fieldmentioning

confidence: 99%

“…We implemented the LSLR-DL model in Pytorch and applied Adam as the learning algorithm. The values of hyperparameters l 1 and l 2 were determined by grid search, and the value of the hyperparameter S was chosen as 4 because our previous study 21 suggested that a small value would work well.…”

Section: Prediction Of Visual Fieldmentioning

confidence: 99%

“…Deeply regularized latent space linear regression focuses on the coefficient and the intercept of the latent space linear regression of the measurement sets to address the heterogeneity in time. This model outperformed PLR 21,33 because glaucomatous VF damage results from the loss of retinal ganglion cells and VF threshold also fluctuates in both the short 4 and long 5 terms. Moreover, VF measurements are associated with considerable noise, 6,7 which hampers the accurate estimation of the speed of VF progression, 8 whereas OCT measurements are highly reproducible.…”

Section: Prediction Of Visual Fieldmentioning

confidence: 99%

“…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. Additionally, this application is clinically relevant because patients typically undergo measurement in the clinical setting using both the HFA 24-2 test longitudinally and OCT measurements, but the HFA 10-2 test usually is optional.…”

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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: Prediction Of Visual Fieldmentioning

confidence: 99%

Section: Prediction Of Visual Fieldmentioning

confidence: 99%

Section: Prediction Of Visual Fieldmentioning

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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“…For predicting VF from OCT images, convolutional neural network (CNN) models were mainly used in previous studies 10,11 . To manage the image data, 3D CNN models were introduced in several studies 1,12 . However, the structure-function relationships are different for each individual 7 .…”

Section: Introductionmentioning

confidence: 99%

A Multimodal Deep Learning Model for Predicting Future Visual Field in Glaucoma Patients

Pham

Han

Shin

2022

Preprint

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Glaucoma is one of the most common reasons for blindness worldwide, especially in elderly people. Glaucoma can be monitored and treated using visual field (VF) tests. Therefore, predicting the future VF to monitor progression of glaucoma is important. In this paper, we proposed a deep learning model to predict future VF based on previous VF and optical coherence tomography (OCT) images (including thickness map, vertical tomogram, and horizontal tomogram). The image data were analyzed using a ResNet-50 model. Image features and previous VFs were combined, and a long short-term memory (LSTM) network was used to predict future VF. A weighted method was used to detect noisy data. The proposed method was improved when applying weighted loss. The mean absolute error (MAE) was 3.31 ± 1.37, and the root mean square error (RMSE) was 4.58 ± 1.77. The model showed high performance when combining VF data and OCT image data. Furthermore, the model was useful for detecting and re-weighting noisy data.

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Visual Field Prediction with Missing and Noisy Data Based on Distance-Based Loss

Pham

Han

Shin

2022

Lecture Notes in Computer Science

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Glaucoma Progression Prediction Using Retinal Thickness via Latent Space Linear Regression

Cited by 10 publications

References 24 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

A Multimodal Deep Learning Model for Predicting Future Visual Field in Glaucoma Patients

Visual Field Prediction with Missing and Noisy Data Based on Distance-Based Loss

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