Predicting 10-2 Visual Field From Optical Coherence Tomography in Glaucoma Using Deep Learning Corrected With 24-2/30-2 Visual Field

Artificial intelligence in glaucoma: opportunities, challenges, and future directions

Artificial intelligence (AI) has shown excellent diagnostic performance in detecting various complex problems related to many areas of healthcare including ophthalmology. AI diagnostic systems developed from fundus images have become state-of-the-art tools in diagnosing retinal conditions and glaucoma as well as other ocular diseases. However, designing and implementing AI models using large imaging data is challenging. In this study, we review different machine learning (ML) and deep learning (DL) techniques applied to multiple modalities of retinal data, such as fundus images and visual fields for glaucoma detection, progression assessment, staging and so on. We summarize findings and provide several taxonomies to help the reader understand the evolution of conventional and emerging AI models in glaucoma. We discuss opportunities and challenges facing AI application in glaucoma and highlight some key themes from the existing literature that may help to explore future studies. Our goal in this systematic review is to help readers and researchers to understand critical aspects of AI related to glaucoma as well as determine the necessary steps and requirements for the successful development of AI models in glaucoma.

Prediction of Central Visual Field Measures From Macular OCT Volume Scans With Deep Learning

Purpose

Predict central 10° global and local visual field (VF) measurements from macular optical coherence tomography (OCT) volume scans with deep learning (DL).

Methods

This study included 1121 OCT volume scans and 10-2 VFs from 289 eyes (257 patients). Macular scans were used to estimate 10-2 VF mean deviation (MD), threshold sensitivities (TS), and total deviation (TD) values at 68 locations. A three-dimensional (3D) convolutional neural network based on the 3D DenseNet121 architecture was used for prediction. We compared DL predictions to those from baseline linear models. We carried out 10-fold stratified cross-validation to optimize generalizability. The performance of the DL and baseline models was compared based on correlations between ground truth and predicted VF measures and mean absolute error (MAE; ground truth - predicted values).

Results

Average (SD) MD was -9.3 (7.7) dB. Average (SD) correlations between predicted and ground truth MD and MD MAE were 0.74 (0.09) and 3.5 (0.4) dB, respectively. Estimation accuracy deteriorated with worsening MD. Average (SD) Pearson correlations between predicted and ground truth TS and MAEs for DL and baseline model were 0.71 (0.05) and 0.52 (0.05) (P < 0.001) and 6.5 (0.6) and 7.5 (0.5) dB (P < 0.001), respectively. For TD, correlation (SD) and MAE (SD) for DL and baseline models were 0.69 (0.02) and 0.48 (0.05) (P < 0.001) and 6.1 (0.5) and 7.8 (0.5) dB (P < 0.001), respectively.

Conclusions

Macular OCT volume scans can be used to predict global central VF parameters with clinically relevant accuracy.

Translational Relevance

Macular OCT imaging may be used to confirm and supplement central VF findings using deep learning.

Prediction of visual field progression in glaucoma: existing methods and artificial intelligence

Timely treatment is essential in the management of glaucoma. However, subjective assessment of visual field (VF) progression is not recommended, because it can be unreliable. There are two types of artificial intelligence (AI) strong and weak (machine learning). Weak AIs can perform specific tasks. Linear regression is a method of weak AI. Using linear regression in the real-world clinic has enabled analyzing and predicting VF progression. However, caution is still required when interpreting the results, because whenever the number of VF data sets investigated is small, the predictions can be inaccurate. Several other non-ordinal, or modern AI methods have been constructed to improve prediction accuracy, such as clustering and more modern AI methods of Analysis with Non-Stationary Weibull Error Regression and Spatial Enhancement (ANSWERS), Variational Bayes Linear Regression (VBLR), Kalman Filter and sparse modeling (The least absolute shrinkage and selection operator regression: Lasso). It is also possible to improve the prediction performance using retinal thickness measured with optical coherence tomography by using machine learning methods, such as multitask learning.

Deep Learning Estimation of 10-2 Visual Field Map Based on Circumpapillary Retinal Nerve Fiber Layer Thickness Measurements

PURPOSE

To estimate central 10-degree visual field (VF) map from spectral-domain optical coherence tomography (SD-OCT) retinal nerve fiber layer thickness (RNFL) measurements in glaucoma with artificial intelligence.

DESIGN

Artificial intelligence (convolutional neural networks) study.

METHODS

This study included 5352 SD-OCT scans and 10-2 VF pairs from 1365 eyes of 724 healthy patients, patients with suspected glaucoma, and patients with glaucoma. Convolutional neural networks (CNNs) were developed to estimate the 68 individual sensitivity thresholds of 10-2 VF map using all-sectors (CNN_A) and temporal-sectors (CNN_T) RNFL thickness information of the SD-OCT circle scan (768 thickness points). 10-2 indices including pointwise total deviation (TD) values, mean deviation (MD), and pattern standard deviation (PSD) were generated using the CNN-estimated sensitivity thresholds at individual test locations. Linear regression (LR) models with the same input were used for comparison.

RESULTS

The CNN_A model achieved an average pointwise mean absolute error of 4.04 dB (95% confidence interval [CI] 3.76-4.35) and correlation coefficient (r) of 0.59 (95% CI 0.52-0.64) over 10-2 map and the mean absolute error and r of 2.88 dB (95% CI 2.63-3.15) and 0.74 (95% CI 0.67-0.80) for MD, and 2.31 dB (95% CI 2.03-2.61) and 0.59 (95% CI 0.51-0.65) for PSD estimations, respectively, significantly outperforming the LR_A model.

CONCLUSIONS

The proposed CNN_A model improved the estimation of 10-2 VF map based on circumpapillary SD-OCT RNFL thickness measurements. These artificial intelligence methods using SD-OCT structural data show promise to individualize the frequency of central VF assessment in patients with glaucoma and would enable the reallocation of resources from patients at lowest risk to those at highest risk of central VF damage.

Deep learning approaches to predict 10-2 visual field from wide-field swept-source optical coherence tomography en face images in glaucoma

Close monitoring of central visual field (VF) defects with 10-2 VF helps prevent blindness in glaucoma. We aimed to develop a deep learning model to predict 10-2 VF from wide-field swept-source optical coherence tomography (SS-OCT) images. Macular ganglion cell/inner plexiform layer thickness maps with either wide-field en face images (en face model) or retinal nerve fiber layer thickness maps (RNFLT model) were extracted, combined, and preprocessed. Inception-ResNet-V2 was trained to predict 10-2 VF from combined images. Estimation performance was evaluated using mean absolute error (MAE) between actual and predicted threshold values, and the two models were compared with different input data. The training dataset comprised paired 10-2 VF and SS-OCT images of 3,025 eyes of 1,612 participants and the test dataset of 337 eyes of 186 participants. Global prediction errors (MAE_point-wise) were 3.10 and 3.17 dB for the en face and RNFLT models, respectively. The en face model performed better than the RNFLT model in superonasal and inferonasal sectors (P = 0.011 and P = 0.030). Prediction errors were smaller in the inferior versus superior hemifields for both models. The deep learning model effectively predicted 10-2 VF from wide-field SS-OCT images and might help clinicians efficiently individualize the frequency of 10-2 VF in clinical practice.

Association of macular structure, function, and vessel density with foveal threshold in advanced glaucoma

Identifying new biomarkers associated with central visual function impairment is important in advanced glaucoma patients. This retrospective cross-sectional study enrolled 154 eyes from 154 subjects, consisting of 86 patients with advanced open-angle glaucoma (mean deviation of 24-2 visual field [VF] tests < - 15 dB) and 68 healthy controls. Structure, function, and vessel density (VD) parameters were obtained using optical coherence tomography (OCT), 24-2 standard automated perimetry, and OCT angiography, respectively. The relationships of macular thickness, central 5° and 10° VF mean sensitivity (MS), and macular VD parameters with foveal threshold (FT), representing central visual function, were investigated using partial correlation analyses and linear regression analyses, with age adjustment. Superficial and deep layer macular VD, central 5° and 10° VF MS, and best corrected visual acuity (BCVA) correlated significantly with FT after age adjustment (P < 0.05). In multivariate linear regression analyses, FT associated significantly with BCVA (β = - 8.80, P < 0.001), central 5° MS (β = 0.30, P = 0.037), and deep-layer global parafoveal VD (β = 0.37, P = 0.037). Thus, deep-layer parafoveal VD is an independent predictor of FT and may be a potential biomarker for central visual function in advanced glaucoma.

The Development and Clinical Application of Innovative Optical Ophthalmic Imaging Techniques

The field of ophthalmic imaging has grown substantially over the last years. Massive improvements in image processing and computer hardware have allowed the emergence of multiple imaging techniques of the eye that can transform patient care. The purpose of this review is to describe the most recent advances in eye imaging and explain how new technologies and imaging methods can be utilized in a clinical setting. The introduction of optical coherence tomography (OCT) was a revolution in eye imaging and has since become the standard of care for a plethora of conditions. Its most recent iterations, OCT angiography, and visible light OCT, as well as imaging modalities, such as fluorescent lifetime imaging ophthalmoscopy, would allow a more thorough evaluation of patients and provide additional information on disease processes. Toward that goal, the application of adaptive optics (AO) and full-field scanning to a variety of eye imaging techniques has further allowed the histologic study of single cells in the retina and anterior segment. Toward the goal of remote eye care and more accessible eye imaging, methods such as handheld OCT devices and imaging through smartphones, have emerged. Finally, incorporating artificial intelligence (AI) in eye images has the potential to become a new milestone for eye imaging while also contributing in social aspects of eye care.