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

Predicting the Extent of Damage in the Humphrey Field Analyzer 24-2 Visual Fields Using 10-2 Test Results in Patients With Advanced Glaucoma

Purpose

To predict Humphrey Field Analyzer 24-2 test (HFA 24-2) results using 10-2 results.

Methods

A total of 175 advanced glaucoma eyes (175 patients) with HFA 24-2 mean deviation (MD24-2) of < -20 dB were prospectively followed up for five years using HFA 10-2 and 24-2 (twice and once in a year, respectively). Using all the HFA 24-2 and 10-2 test result pairs measured within three months (350 pairs from 85 eyes, training dataset), a formula to predict HFA 24-2 result using HFA 10-2 results was constructed using least absolute shrinkage and selection operator regression (LASSO). Using 90 different eyes (testing dataset), the absolute differences between the actual and LASSO-predicted MD24-2 and that between the slopes calculated using five actual and LASSO-predicted MD24-2 values, were adopted as the prediction error. Similar analyses were performed for the mean total deviation values (mTD) of the superior (or inferior) hemifield [hemi-mTDsup.24-2(-hemi-mTDinf.24-2)].

Results

The prediction error for the LASSO-predicted MD24-2 and its slope were 2.98 (standard deviation [SD] = 1.90) dB and 0.32 (0.33) dB/yr, respectively. The LASSO-predicted hemi-mTDsup.24-2 (hemi-mTDinf.24-2), and its slope were 3.02 (2.89) and 3.76 (2.72) dB, and 0.37 (0.41) and 0.44 (0.38) dB/year, respectively. These prediction errors were within two times SD of repeatability of the simulated stable HFA 24-2 VF parameter series.

Conclusions

HFA 24-2 results could be predicted using the paired HFA 10-2 results with reasonable accuracy using LASSO in patients with advanced glaucoma.

Translational Relevance

It is useful to predict HFA24-2 test from HFA10-2 test, when the former is not available, in advanced glaucoma.

Deep Learning Estimation of 10-2 Visual Field Map Based on Macular Optical Coherence Tomography Angiography Measurements

PURPOSE

To develop deep learning (DL) models estimating the central visual field (VF) from optical coherence tomography angiography (OCTA) vessel density (VD) measurements.

DESIGN

Development and validation of a deep learning model.

METHODS

A total of 1051 10-2 VF OCTA pairs from healthy, glaucoma suspects, and glaucoma eyes were included. DL models were trained on en face macula VD images from OCTA to estimate 10-2 mean deviation (MD), pattern standard deviation (PSD), 68 total deviation (TD) and pattern deviation (PD) values and compared with a linear regression (LR) model with the same input. Accuracy of the models was evaluated by calculating the average mean absolute error (MAE) and the R2 (squared Pearson correlation coefficients) of the estimated and actual VF values.

RESULTS

DL models predicting 10-2 MD achieved R2 of 0.85 (95% confidence interval [CI], 74-0.92) for 10-2 MD and MAEs of 1.76 dB (95% CI, 1.39-2.17 dB) for MD. This was significantly better than mean linear estimates for 10-2 MD. The DL model outperformed the LR model for the estimation of pointwise TD values with an average MAE of 2.48 dB (95% CI, 1.99-3.02) and R2 of 0.69 (95% CI, 0.57-0.76) over all test points. The DL model outperformed the LR model for the estimation of all sectors.

CONCLUSIONS

DL models enable the estimation of VF loss from OCTA images with high accuracy. Applying DL to the OCTA images may enhance clinical decision making. It also may improve individualized patient care and risk stratification of patients who are at risk for central VF damage.

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.

The application of artificial intelligence in glaucoma diagnosis and prediction

Artificial intelligence is a multidisciplinary and collaborative science, the ability of deep learning for image feature extraction and processing gives it a unique advantage in dealing with problems in ophthalmology. The deep learning system can assist ophthalmologists in diagnosing characteristic fundus lesions in glaucoma, such as retinal nerve fiber layer defects, optic nerve head damage, optic disc hemorrhage, etc. Early detection of these lesions can help delay structural damage, protect visual function, and reduce visual field damage. The development of deep learning led to the emergence of deep convolutional neural networks, which are pushing the integration of artificial intelligence with testing devices such as visual field meters, fundus imaging and optical coherence tomography to drive more rapid advances in clinical glaucoma diagnosis and prediction techniques. This article details advances in artificial intelligence combined with visual field, fundus photography, and optical coherence tomography in the field of glaucoma diagnosis and prediction, some of which are familiar and some not widely known. Then it further explores the challenges at this stage and the prospects for future clinical applications. In the future, the deep cooperation between artificial intelligence and medical technology will make the datasets and clinical application rules more standardized, and glaucoma diagnosis and prediction tools will be simplified in a single direction, which will benefit multiple ethnic groups.

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.

The number of examinations required for the accurate prediction of the progression of the central 10-degree visual field test in glaucoma

The purpose of the study was to investigate the number of examinations required to precisely predict the future central 10-degree visual field (VF) test and to evaluate the effect of fitting non-linear models, including quadratic regression, exponential regression, logistic regression, and M-estimator robust regression model, for eyes with glaucoma. 180 eyes from 133 open angle glaucoma patients with a minimum of 13 Humphrey Field Analyzer 10-2 SITA standard VF tests were analyzed in this study. Using trend analysis with ordinary least squares linear regression (OLSLR), the first, second, and third future VFs were predicted in a point-wise (PW) manner using a varied number of prior VF sequences, and mean absolute errors (MAE) were calculated. The number of VFs needed to reach the minimum 95% confidence interval (CI) of the MAE of the OLSLR was investigated. We also examined the effect of applying other non-linear models. When predicting the first, second, and third future VFs using OLSLR, the minimum MAE was obtained using VF1-12 (2.15 ± 0.98 dB), VF1-11 (2.33 ± 1.10 dB), and VF1-10 (2.63 ± 1.36 dB), respectively. To reach the 95% CI of these MAEs, 10, 10, and 8 VFs were needed for the first, second and third future VF predictions, respectively. No improvement was observed by applying non-linear regression models. As a conclusion, approximately 8-10 VFs were needed to achieve an accurate prediction of PW VF sensitivity of the 10-degree central VF.

Identifying the Retinal Layers Linked to Human Contrast Sensitivity Via Deep Learning

Purpose

Luminance contrast is the fundamental building block of human spatial vision. Therefore contrast sensitivity, the reciprocal of contrast threshold required for target detection, has been a barometer of human visual function. Although retinal ganglion cells (RGCs) are known to be involved in contrast coding, it still remains unknown whether the retinal layers containing RGCs are linked to a person's contrast sensitivity (e.g., Pelli-Robson contrast sensitivity) and, if so, to what extent the retinal layers are related to behavioral contrast sensitivity. Thus the current study aims to identify the retinal layers and features critical for predicting a person's contrast sensitivity via deep learning.

Methods

Data were collected from 225 subjects including individuals with either glaucoma, age-related macular degeneration, or normal vision. A deep convolutional neural network trained to predict a person's Pelli-Robson contrast sensitivity from structural retinal images measured with optical coherence tomography was used. Then, activation maps that represent the critical features learned by the network for the output prediction were computed.

Results

The thickness of both ganglion cell and inner plexiform layers, reflecting RGC counts, were found to be significantly correlated with contrast sensitivity (r = 0.26 ∼ 0.58,Ps < 0.001 for different eccentricities). Importantly, the results showed that retinal layers containing RGCs were the critical features the network uses to predict a person's contrast sensitivity (an average R² = 0.36 ± 0.10).

Conclusions

The findings confirmed the structure and function relationship for contrast sensitivity while highlighting the role of RGC density for human contrast sensitivity.

[Artificial intelligence and glaucoma: A literature review]

In recent years, research in artificial intelligence (AI) has experienced an unprecedented surge in the field of ophthalmology, in particular glaucoma. The diagnosis and follow-up of glaucoma is complex and relies on a body of clinical evidence and ancillary tests. This large amount of information from structural and functional testing of the optic nerve and macula makes glaucoma a particularly appropriate field for the application of AI. In this paper, we will review work using AI in the field of glaucoma, whether for screening, diagnosis or detection of progression. Many AI strategies have shown promising results for glaucoma detection using fundus photography, optical coherence tomography, or automated perimetry. The combination of these imaging modalities increases the performance of AI algorithms, with results comparable to those of humans. We will discuss potential applications as well as obstacles and limitations to the deployment and validation of such models. While there is no doubt that AI has the potential to revolutionize glaucoma management and screening, research in the coming years will need to address unavoidable questions regarding the clinical significance of such results and the explicability of the predictions.

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

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 cross-sectional 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 1–7) 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).

Deep Learning Estimation of 10-2 and 24-2 Visual Field Metrics Based on Thickness Maps from Macula OCT

PURPOSE

To develop deep learning (DL) systems estimating visual function from macula-centered spectral-domain (SD) OCT images.

DESIGN

Evaluation of a diagnostic technology.

PARTICIPANTS

A total of 2408 10-2 visual field (VF) SD OCT pairs and 2999 24-2 VF SD OCT pairs collected from 645 healthy and glaucoma subjects (1222 eyes).

METHODS

Deep learning models were trained on thickness maps from Spectralis macula SD OCT to estimate 10-2 and 24-2 VF mean deviation (MD) and pattern standard deviation (PSD). Individual and combined DL models were trained using thickness data from 6 layers (retinal nerve fiber layer [RNFL], ganglion cell layer [GCL], inner plexiform layer [IPL], ganglion cell-IPL [GCIPL], ganglion cell complex [GCC] and retina). Linear regression of mean layer thicknesses were used for comparison.

MAIN OUTCOME MEASURES

Deep learning models were evaluated using R² and mean absolute error (MAE) compared with 10-2 and 24-2 VF measurements.

RESULTS

Combined DL models estimating 10-2 achieved R² of 0.82 (95% confidence interval [CI], 0.68-0.89) for MD and 0.69 (95% CI, 0.55-0.81) for PSD and MAEs of 1.9 dB (95% CI, 1.6-2.4 dB) for MD and 1.5 dB (95% CI, 1.2-1.9 dB) for PSD. This was significantly better than mean thickness estimates for 10-2 MD (0.61 [95% CI, 0.47-0.71] and 3.0 dB [95% CI, 2.5-3.5 dB]) and 10-2 PSD (0.46 [95% CI, 0.31-0.60] and 2.3 dB [95% CI, 1.8-2.7 dB]). Combined DL models estimating 24-2 achieved R² of 0.79 (95% CI, 0.72-0.84) for MD and 0.68 (95% CI, 0.53-0.79) for PSD and MAEs of 2.1 dB (95% CI, 1.8-2.5 dB) for MD and 1.5 dB (95% CI, 1.3-1.9 dB) for PSD. This was significantly better than mean thickness estimates for 24-2 MD (0.41 [95% CI, 0.26-0.57] and 3.4 dB [95% CI, 2.7-4.5 dB]) and 24-2 PSD (0.38 [95% CI, 0.20-0.57] and 2.4 dB [95% CI, 2.0-2.8 dB]). The GCIPL (R² = 0.79) and GCC (R² = 0.75) had the highest performance estimating 10-2 and 24-2 MD, respectively.

CONCLUSIONS

Deep learning models improved estimates of functional loss from SD OCT imaging. Accurate estimates can help clinicians to individualize VF testing to patients.

Three "Red Lines" for Pattern Recognition-Based Differential Diagnosis Using Optical Coherence Tomography in Clinical Practice

BACKGROUND

Optical coherence tomography (OCT) devices for imaging of the eye are broadly available. The test is noninvasive, rapid, and well-tolerated by patients. This creates a large number of OCT images and patient referrals. Interpretation of OCT findings at the interface between neurological and ophthalmologic conditions has become a key skill in the neuro-ophthalmology service. Similar to the interpretation of visual fields, recogntion of the vertical and horizontal medians are helpful. A third "red line" is added, which will be reviewed here.

EVIDENCE

Levels 1a to 5 evidence.

ACQUISITION

Literature research.

RESULTS

There is level 1a evidence that neurodegeneration of the brain is associated with inner retinal layer atrophy. Predominantly, this is driven by retrograde (trans-synaptic) axonal degeneration from the brain to the eye. This process typically stops at the level of the inner nuclear layer (INL). Anterograde (Wallerian) axonal degeneration from the eye to the brain can trespass the INL. The geography of atrophy and swelling of individual macular retinal layers distinguishes prechiasmal from postchiasmal pathology. The emerging patterns are a front-back "red line" at the INL; a vertical "red line" through the macula for chiasmal/postchiasmal pathology; and a horizontal "red line" through the macular for pathology pointing to the optic disc. This is summarized by illustrative case vignettes.

CONCLUSIONS

The interpretation of patterns of individual retinal layer atrophy (3 "red lines") needs to be combined with recognition of localized layer thickening (edema, structural) at the macula. Certain macular patterns point to pathology at the level of the optic disc. This requires revision of the optic disc OCT and will guide need for further investigations. The 3 "red lines" proposed here may be found useful in clinical practice and the related mnemonics ("half moon," "sunset," "rainbow") for teaching.

Macular Optical Coherence Tomography Imaging in Glaucoma

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. Containing more than 50% of retinal ganglion cells in a multilayered pattern, macula is shown to be affected even at the earliest stages of glaucomatous structural damage. Risk assessment for glaucoma progression, earlier detection of glaucomatous structural damage, monitoring of glaucoma especially in advanced cases, and glaucoma evaluation in certain ocular conditions including eyes with high myopia, positive history of disc hemorrhage, and certain optic disc phenotypes are specific domains where macular imaging yields complementary information compared to optic nerve head and peripapillary evaluation using optical coherence tomography. Moreover, the development of artificial intelligence models in data analysis has enabled a tremendous opportunity to create an integrated representation of structural and functional alterations observed in glaucoma. In this study, we aimed at providing a brief review of the main clinical applications and future potential utility of macular spectral-domain optical coherence tomography in glaucoma.

Artificial intelligence and complex statistical modeling in glaucoma diagnosis and management

PURPOSE OF REVIEW

The field of artificial intelligence has grown exponentially in recent years with new technology, methods, and applications emerging at a rapid rate. Many of these advancements have been used to improve the diagnosis and management of glaucoma. We aim to provide an overview of recent publications regarding the use of artificial intelligence to enhance the detection and treatment of glaucoma.

RECENT FINDINGS

Machine learning classifiers and deep learning algorithms have been developed to autonomously detect early structural and functional changes of glaucoma using different imaging and testing modalities such as fundus photography, optical coherence tomography, and standard automated perimetry. Artificial intelligence has also been used to further delineate structure-function correlation in glaucoma. Additional 'structure-structure' predictions have been successfully estimated. Other machine learning techniques utilizing complex statistical modeling have been used to detect glaucoma progression, as well as to predict future progression. Although not yet approved for clinical use, these artificial intelligence techniques have the potential to significantly improve glaucoma diagnosis and management.

SUMMARY

Rapidly emerging artificial intelligence algorithms have been used for the detection and management of glaucoma. These algorithms may aid the clinician in caring for patients with this complex disease. Further validation is required prior to employing these techniques widely in clinical practice.

Artificial intelligence and deep learning in glaucoma: Current state and future prospects

Over the past few years, there has been an unprecedented and tremendous excitement for artificial intelligence (AI) research in the field of Ophthalmology; this has naturally been translated to glaucoma-a progressive optic neuropathy characterized by retinal ganglion cell axon loss and associated visual field defects. In this review, we aim to discuss how AI may have a unique opportunity to tackle the many challenges faced in the glaucoma clinic. This is because glaucoma remains poorly understood with difficulties in providing early diagnosis and prognosis accurately and in a timely fashion. In the short term, AI could also become a game changer by paving the way for the first cost-effective glaucoma screening campaigns. While there are undeniable technical and clinical challenges ahead, and more so than for other ophthalmic disorders whereby AI is already booming, we strongly believe that glaucoma specialists should embrace AI as a companion to their practice. Finally, this review will also remind ourselves that glaucoma is a complex group of disorders with a multitude of physiological manifestations that cannot yet be observed clinically. AI in glaucoma is here to stay, but it will not be the only tool to solve glaucoma.