Longitudinal Higher-Order OCT Assessment of Quantitative Fluid Dynamics and the Total Retinal Fluid Index in Neovascular AMD

Ellipsoid Zone Integrity and Visual Function in Dry Age-Related Macular Degeneration

In this longitudinal retrospective image analysis, conducted on patients diagnosed with dry age-related macular degeneration (AMD) and 5 years of follow-up imaging data, the study aimed to investigate the relationship between ellipsoid zone (EZ) integrity on spectral domain optical coherence tomography (SD-OCT) and visual acuity (VA). Using a machine learning-enabled feature extraction tool, quantitative EZ parameters were derived from SD-OCT images. The analysis revealed significant correlations between EZ integrity metrics and VA. Eyes with excellent VA (≥20/25 Snellen) exhibited higher EZ integrity, including less EZ attenuation, thicker ellipsoid zone-retinal pigment epithelium (EZ-RPE) thickness, and higher EZ intensity, in contrast to eyes with worse VA (≤20/40 Snellen). Additionally, eyes with geographic atrophy (GA) in the foveal region displayed compromised EZ integrity compared to those without GA. Notably, baseline EZ integrity metrics were predictive of future VA loss. These findings suggest that quantitative SD-OCT measurements of EZ integrity could potentially detect early changes in dry AMD and serve as valuable indicators for predicting future functional outcomes. Furthermore, these measurements hold promise for use in clinical trial screenings, offering insights into the progression of the disease and its impact on visual acuity. This study underscores the importance of EZ integrity assessment in understanding and managing dry AMD.

Texture-Based Radiomic SD-OCT Features Associated With Response to Anti-VEGF Therapy in a Phase III Neovascular AMD Clinical Trial

Purpose

The goal of this study was to evaluate the role of texture-based baseline radiomic features (Fr) and dynamic radiomics alterations (delta, FΔr) within multiple targeted compartments on optical coherence tomography (OCT) scans to predict response to anti-vascular endothelial growth factor (VEGF) therapy in neovascular age-related macular degeneration (nAMD).

Methods

HAWK is a phase 3 clinical trial data set of active nAMD patients (N = 1082) comparing brolucizumab and aflibercept. This analysis included patients receiving 6 mg brolucizumab or 2 mg aflibercept and categorized as complete responders (n = 280) and incomplete responders (n = 239) based on whether or not the eyes achieved/maintained fluid resolution on OCT. A total of 481 Fr were extracted from each of the fluid, subretinal hyperreflective material (SHRM), retinal tissue, and sub-retinal pigment epithelium (RPE) compartments. Most discriminating eight baseline features, selected by the minimum redundancy, maximum relevance feature selection, were evaluated using a quadratic discriminant analysis (QDA) classifier on the training set (Str, n = 363) to differentiate between the two patient groups. Classifier performance was subsequently validated on independent test set (St, n = 156).

Results

In total, 519 participants were included in this analysis from the HAWK phase 3 study. There were 280 complete responders and 219 incomplete responders. Compartmental analysis of radiomics featured identified the sub-RPE and SHRM compartments as the most distinguishing between the two response groups. The QDA classifier yielded areas under the curve of 0.78, 0.79, and 0.84, respectively, using Fr, FΔr, and combined Fr, FΔr, and Fc on St.

Conclusions

Utilizing compartmental static and dynamic radiomics features, unique differences were identified between eyes that respond differently to anti-VEGF therapy in a large phase 3 trial that may provide important predictive value.

Translational Relevance

Imaging biomarkers, such as radiomics features identified in this analysis, for predicting treatment response are needed to enhanced precision medicine in the management of nAMD.

Deep Learning Algorithm Detects Presence of Disorganization of Retinal Inner Layers (DRIL)-An Early Imaging Biomarker in Diabetic Retinopathy

Purpose

To develop and train a deep learning-based algorithm for detecting disorganization of retinal inner layers (DRIL) on optical coherence tomography (OCT) to screen a cohort of patients with diabetic retinopathy (DR).

Methods

In this cross-sectional study, subjects over age 18, with ICD-9/10 diagnoses of type 2 diabetes with and without retinopathy and Cirrus HD-OCT imaging performed between January 2009 to September 2019 were included in this study. After inclusion and exclusion criteria were applied, a final total of 664 patients (5992 B-scans from 1201 eyes) were included for analysis. Five-line horizontal raster scans from Cirrus HD-OCT were obtained from the shared electronic health record. Two trained graders evaluated scans for presence of DRIL. A third physician grader arbitrated any disagreements. Of 5992 B-scans analyzed, 1397 scans (∼30%) demonstrated presence of DRIL. Graded scans were used to label training data for the convolution neural network (CNN) development and training.

Results

On a single CPU system, the best performing CNN training took ∼35 mins. Labeled data were divided 90:10 for internal training/validation and external testing purpose. With this training, our deep learning network was able to predict the presence of DRIL in new OCT scans with a high accuracy of 88.3%, specificity of 90.0%, sensitivity of 82.9%, and Matthews correlation coefficient of 0.7.

Conclusions

The present study demonstrates that a deep learning-based OCT classification algorithm can be used for rapid automated identification of DRIL. This developed tool can assist in screening for DRIL in both research and clinical decision-making settings.

Translational Relevance

A deep learning algorithm can detect disorganization of retinal inner layers in OCT scans.

Longitudinal analysis of aqueous humour cytokine expression and OCT-based imaging biomarkers in retinal vein occlusions treated with anti-vascular endothelial growth factor therapy in the IMAGINE study

BACKGROUND/OBJECTIVES

Retinal vein occlusion (RVO) is the second most common retinal vascular disorder. Despite promising advances with anti-VEGF therapy, select patients are unresponsive to therapy. A precision medicine-based approach for therapeutic decision-making based on underlying biomarkers may facilitate treatment based on the underlying pathway. This study aims to identify the baseline and longitudinal cytokine profiles of RVO-related macular oedema and correlating these expression profiles with higher order OCT features using a novel retinal segmentation and feature extraction platform.

SUBJECTS/METHODS

The IMAGINE study is a post-hoc assessment of aqueous humour cytokines with correlation to higher level analysis of imaging studies. OCT scans underwent machine learning enhanced segmentation of the internal limiting membrane (ILM), ellipsoid zone (EZ) and retinal pigment epithelium (RPE), as well as evaluating volumetric fluid metrics. Samples of aqueous humour were obtained at baseline, as well as months 4 and 9 prior to treatment. These samples were analysed for the expression of multiple cytokines. Patients were divided into Responders and Non-Responders based on OCT profiles. Additionally, patients were categorised as a Rebounder if their CST increased by 50% after initial improvement.

RESULTS

Twenty-six eyes were included. The OCT-based response schema identified 21 Responders (81%) and 5 Non-Responders (19%). VEGF levels directly correlated with intraretinal fluid volume and angiogenin was inversely correlated with fluid indices. Multiple cytokines, including ANGPTL4, were directly correlated with ellipsoid zone disruption. The baseline VEGF levels were significantly higher in all responders compared to Non-Responders (p = 0.02). Rebounders tended to have significantly decreased levels of angiogenin and TIMP-1 (p = 0.019, p = 0.015).

CONCLUSIONS

Cytokine expression was linked to specific OCT features and treatment response in RVO. Identification of an imaging phenotype that could serve as a surrogate for underlying active disease pathways could enhance treatment decision-making and precision medicine.

Automated Identification and Segmentation of Ellipsoid Zone At-Risk Using Deep Learning on SD-OCT for Predicting Progression in Dry AMD

BACKGROUND

The development and testing of a deep learning (DL)-based approach for detection and measurement of regions of Ellipsoid Zone (EZ) At-Risk to study progression in nonexudative age-related macular degeneration (AMD).

METHODS

Used in DL model training and testing were 341 subjects with nonexudative AMD with or without geographic atrophy (GA). An independent dataset of 120 subjects were used for testing model performance for prediction of GA progression. Accuracy, specificity, sensitivity, and intraclass correlation coefficient (ICC) for DL-based EZ At-Risk percentage area measurement was calculated. Random forest-based feature ranking of EZ At-Risk was compared to previously validated quantitative OCT-based biomarkers.

RESULTS

The model achieved a detection accuracy of 99% (sensitivity = 99%; specificity = 100%) for EZ At-Risk. Automatic EZ At-Risk measurement achieved an accuracy of 90% (sensitivity = 90%; specificity = 84%) and the ICC compared to ground truth was high (0.83). In the independent dataset, higher baseline mean EZ At-Risk correlated with higher progression to GA at year 5 (p < 0.001). EZ At-Risk was a top ranked feature in the random forest assessment for GA prediction.

CONCLUSIONS

This report describes a novel high performance DL-based model for the detection and measurement of EZ At-Risk. This biomarker showed promising results in predicting progression in nonexudative AMD patients.

Longitudinal Quantitative Ultrawide-field Fluorescein Angiography Dynamics in the RUBY Diabetic Macular Edema Study

OBJECTIVE

The purpose of this study was to evaluate the longitudinal change in quantitative ultrawide-field angiographic (UWFA) parameters and correlate them with functional outcomes and spectral domain-OCT metrics.

DESIGN

This study is a post hoc analysis of the phase II RUBY study: a prospective, randomized trial of patients with diabetic macular edema (DME) treated with either intravitreal aflibercept injection (IAI) or combined IAI/nesvacumab (antiangiopoietin 2 mAb).

SUBJECTS

Subjects with DME that underwent UWFA across all treatment groups (n = 44).

METHODS

A machine learning-enabled feature extraction system generated panretinal quantitative UWFA metrics, including leakage, ischemia, and microaneurysm (MA) burden. Zonal assessments were performed corresponding to the macula, midperiphery, and far periphery.

MAIN OUTCOME MEASURES

Changes in ischemic area and index (proportion of nonperfusion in analyzable retina), leakage area and index (proportion of leakage in analyzable retina), and MA count at baseline, week 12, week 24, and week 36 were analyzed. Spectral-domain-OCT quantitative metrics, such as central subfield thickness, ellipsoid zone (EZ) integrity parameters, intraretinal fluid (IRF) volume, and subretinal fluid (SRF) volume were extracted via a machine learning-enhanced OCT feature extraction platform and analyzed. Additionally, the effect of these changes on best-corrected visual acuity (BCVA) was evaluated.

RESULTS

Mean panretinal leakage index, zonal leakage area, and panretinal MA count improved significantly between baseline and week 36. Panretinal ischemic index decreased between baseline and week 36, with some aspects showing significant improvement. Mean BCVA significantly improved from baseline to week 36. There was a significant inverse correlation between change in BCVA and change in macular leakage area. A direct correlation was observed between both baseline macular leakage area and panretinal leakage index with IRF volume, SRF volume, and EZ disruption on OCT.

CONCLUSIONS

Assessment of UWFA parameters demonstrates a significant improvement in panretinal leakage index, leakage area, and MA burden in eyes treated with IAI with or without nesvacumab. A numeric reduction in panretinal ischemic index and area was noted. The analysis also shows the critical association of leakage with visual and OCT features. This highlights the potential role of UWFA in disease burden assessment, with leakage parameters serving as a primary end point.

FINANCIAL DISCLOSURE(S)

Proprietary or commercial disclosure may be found after the references.

Machine Learning-Based Automated Detection and Quantification of Geographic Atrophy and Hypertransmission Defects Using Spectral Domain Optical Coherence Tomography

The current study describes the development and assessment of innovative, machine learning (ML)-based approaches for automated detection and pixel-accurate measurements of regions with geographic atrophy (GA) in late-stage age-related macular degeneration (AMD) using optical coherence tomography systems. 900 OCT volumes, 100266 B-scans, and en face OCT images from 341 non-exudative AMD patients with or without GA were included in this study from both Cirrus (Zeiss) and Spectralis (Heidelberg) OCT systems. B-scan and en face level ground truth GA masks were created on OCT B-scan where the segmented ellipsoid zone (EZ) line, retinal pigment epithelium (RPE) line, and bruchs membrane (BM) line overlapped. Two deep learning-based approaches, B-scan level and en face level, were trained. The OCT B-scan model had detection accuracy of 91% and GA area measurement accuracy of 94%. The en face OCT model had detection accuracy of 82% and GA area measurement accuracy of 96% with primary target of hypertransmission on en face OCT. Accuracy was good for both devices tested (92-97%). Automated lesion size stratification for CAM cRORA definition of 250um minimum lesion size was feasible. High-performance models for automatic detection and segmentation of GA area were achieved using OCT systems and deep learning. The automatic measurements showed high correlation with the ground truth. The en face model excelled at identification of hypertransmission defects. The models performance generalized well across device types tested. Future development will include integration of both models to enhance feature detection across GA lesions as well as isolating hypertransmission defects without GA for pre-GA biomarker extraction.

OCT-Derived Radiomic Features Predict Anti-VEGF Response and Durability in Neovascular Age-Related Macular Degeneration

PURPOSE

No established biomarkers currently exist for therapeutic efficacy and durability of anti-VEGF therapy in neovascular age-related macular degeneration (nAMD). This study evaluated radiomic-based quantitative OCT biomarkers that may be predictive of anti-VEGF treatment response and durability.

DESIGN

Assessment of baseline biomarkers using machine learning (ML) classifiers to predict tolerance to anti-VEGF therapy.

PARTICIPANTS

Eighty-one participants with treatment-naïve nAMD from the OSPREY study, including 15 super responders (patients who achieved and maintained retinal fluid resolution) and 66 non-super responders (patients who did not achieve or maintain retinal fluid resolution).

METHODS

A total of 962 texture-based radiomic features were extracted from fluid, subretinal hyperreflective material (SHRM), and different retinal tissue compartments of OCT scans. The top 8 features, chosen by the minimum redundancy maximum relevance feature selection method, were evaluated using 4 ML classifiers in a cross-validated approach to distinguish between the 2 patient groups. Longitudinal assessment of changes in different texture-based radiomic descriptors (delta-texture features) between baseline and month 3 also was performed to evaluate their association with treatment response. Additionally, 8 baseline clinical parameters and a combination of baseline OCT, delta-texture features, and the clinical parameters were evaluated in a cross-validated approach in terms of association with therapeutic response.

MAIN OUTCOME MEASURES

The cross-validated area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, and specificity were calculated to validate the classifier performance.

RESULTS

The cross-validated AUC by the quadratic discriminant analysis classifier was 0.75 ± 0.09 using texture-based baseline OCT features. The delta-texture features within different OCT compartments between baseline and month 3 yielded an AUC of 0.78 ± 0.08. The baseline clinical parameters sub-retinal pigment epithelium volume and intraretinal fluid volume yielded an AUC of 0.62 ± 0.07. When all the baseline, delta, and clinical features were combined, a statistically significant improvement in the classifier performance (AUC, 0.81 ± 0.07) was obtained.

CONCLUSIONS

Radiomic-based quantitative assessment of OCT images was shown to distinguish between super responders and non-super responders to anti-VEGF therapy in nAMD. The baseline fluid and SHRM delta-texture features were found to be most discriminating across groups.

Bacillary Detachment in Neovascular Age-Related Macular Degeneration: Incidence, Clinical Features, and Response to Anti-VEGF Therapy

OBJECTIVE

To evaluate the incidence of bacillary layer detachment among patients with neovascular age-related macular degeneration (nAMD) and their response to anti-VEGF therapy.

DESIGN

Post hoc analysis of the OSPREY clinical trial, a prospective, double-masked, phase II study comparing 6-mg brolucizumab with 2-mg aflibercept over 56 weeks.

PARTICIPANTS

Participants with treatment-naive nAMD at the initiation of the trial were included in the analysis (n = 81).

METHODS

Spectral-domain OCT (SD-OCT) scans were obtained at 4-week intervals throughout the OSPREY study and were segmented automatically using a proprietary, machine learning-enabled higher-order feature-extraction platform.

MAIN OUTCOME MEASURES

The presence of bacillary detachment, and in these eyes the effect of anti-VEGF therapy on change from baseline in visual acuity (VA), central subfield thickness (CST), retinal fluid volumes, subretinal hyper-reflective material (SHRM) volume, subretinal pigment epithelium (sub-RPE) fluid volume, and ellipsoid zone (EZ) integrity at week 56.

RESULTS

Bacillary detachment was identified in 7.4% (6 of 81) eyes, which had higher fluid volumes, increased CST, EZ attenuation, and increased sub-RPE volume at baseline compared with eyes without bacillary detachment. Anti-VEGF treatment resulted in the resolution of bacillary detachment in 100% of the eyes. In eyes with bacillary detachment at baseline, the anti-VEGF treatment decreased CST, fluid burden, and SHRM volumes throughout the treatment course; however, there was no significant change from baseline in VA, sub-RPE volume, or EZ integrity throughout the 56-week course of anti-VEGF treatment.

CONCLUSIONS

Bacillary detachment is an OCT signature that is identifiable in a notable proportion of nAMD eyes. Anti-VEGF therapy resulted in 100% resolution of bacillary detachment and significant decreases in CST and SHRM volume; however, improvements in VA may have been limited by persistent EZ attenuation.

Impact of Baseline Quantitative OCT Features on Response to Risuteganib for the Treatment of Dry Age-Related Macular Degeneration: The Importance of Outer Retinal Integrity

OBJECTIVE

The purpose of the study was to perform a post hoc analysis to explore the effect of baseline anatomic characteristics identified on OCT on best-corrected visual acuity (BCVA) responses to risuteganib from the completed phase II study in subjects with dry age-related macular degeneration (AMD).

DESIGN

Post hoc analysis of a randomized, double-masked, placebo-controlled, phase II study.

SUBJECTS

Eyes with intermediate dry AMD with BCVA between 20/40 and 20/200. Patients with concurrent vision-influencing or macula-obscuring ocular pathologies were excluded.

METHODS

Patients were randomized to receive a 1-mg intravitreal risuteganib injection or a sham injection at baseline. A second 1-mg intravitreal injection of risuteganib was given at week 16 to those in the treatment arm. Two independent, masked reading centers evaluated the baseline anatomic characteristics on OCT to explore features associated with positive responses to risuteganib.

MAIN OUTCOME MEASURES

Treatment response was defined as a gain of ≥ 8 letters in BCVA from baseline to week 28 in the treatment arm, compared with baseline to week 12 in the sham group. Anatomic parameters, measured by retinal segmentation platforms, including measures of retinal thickness were compared between the responders and nonresponders to risuteganib.

RESULTS

Thirty-nine patients completed the study and underwent analysis. In the treatment arm, 48% of eyes demonstrated treatment responses, compared with 7% in the sham group. In the quantitative anatomic assessment, enhanced ellipsoid integrity, greater outer retinal thickness, and decreased geographic atrophy were associated with increased BCVA gains to risuteganib.

CONCLUSIONS

This post hoc analysis demonstrated that baseline OCT features may help determine the likelihood of a functional response to risuteganib. The characterization of higher-order OCT features may provide important information regarding biomarkers for treatment response and could facilitate optimized clinical trial enrollment and enrichment.

The Impact of Early Residual Fluid After Anti-Vascular Endothelial Growth Factor Initiation in Patients With Neovascular Age-Related Macular Degeneration: A Meta-Analysis Review

Fluid in neovascular age-related macular degeneration is often used to assess patient response to anti-vascular endothelial growth factor therapy. Various studies theorize that early residual fluid (ERF), noted as persistence of intraretinal fluid and subretinal fluid after the anti-vascular endothelial growth factor loading phase (LP), may be predictive of visual outcomes. This meta-analysis examined the existing literature on the relationship between ERF and long-term visual acuity (VA) and found that those who were fluid-free after the LP tended to have the highest VA gains overall. Early intraretinal fluid appeared to be associated with reduced VA gains, whereas the impact of early sub-retinal fluid was more debated. For those with ERF, monthly or more frequent dosing regimens following the LP appeared most optimal for VA. As most studies in this review were post hoc analyses, this highlights the need for real-world studies investigating ERF and its effect on visual outcomes in neovascular age-related macular degeneration. [Ophthalmic Surg Lasers Imaging Retina 2022;53:506-513.].

The Association of Fluid Volatility With Subretinal Hyperreflective Material and Ellipsoid Zone Integrity in Neovascular AMD

Purpose

To evaluate the association of fluid volatility with ellipsoid zone (EZ) integrity and subretinal hyperreflective material (SHRM) volume during anti–vascular endothelial growth factor (VEGF) therapy in neovascular age-related macular degeneration (nAMD).

Methods

This study was a post hoc analysis of the OSPREY study. Retinal volatility was quantified as the standard deviation across weeks 12 to 56 for six optical coherence tomography (OCT) metrics: central subfield thickness (CST), total fluid (TF) volume, subretinal fluid (SRF) volume, intraretinal fluid (IRF) volume, macular total retinal fluid index (TRFI), and central macular TRFI. Eyes with volatility ≤ 25th or ≥ 75th percentile values were compared.

Results

Eyes with low volatility in several exudative metrics showed greater change from baseline in SHRM volume at week 12 than eyes with high volatility. During the maintenance phase (weeks 12–56), eyes exhibiting high SRF volatility demonstrated increased SHRM volume compared to eyes with low SRF volatility (P = 0.027). Eyes exhibiting high volatility in CST, TF, and SRF demonstrated less improvement in EZ total attenuation (P < 0.001, P = 0.033, and P = 0.043, respectively) than eyes with low volatility. Early exudative instability (i.e., between weeks 4–8 or weeks 8–12) in multiple parameters (i.e., CST, TF, IRF, macular TRFI, or central macular TRFI) was associated with greater volatility during the maintenance phase (P < 0.05).

Conclusions

Greater volatility in exudative OCT metrics, particularly SRF volatility, was associated with a greater increase in SHRM and less improvement in EZ integrity, suggesting that volatility is detrimental to multiple anatomic features in nAMD. Early exudative instability during the loading phase of treatment was associated with longer-term volatility in exudation.

Fluorescence Sensing Technologies for Ophthalmic Diagnosis

Personalized and point-of-care (POC) diagnoses are critical for ocular physiology and disease diagnosis. Real-time monitoring and continuous sampling abilities of tear fluid and user-friendliness have become the key characteristics for the applied ophthalmic techniques. Fluorescence technologies, as one of the most popular methods that can fulfill the requirements of clinical ophthalmic applications for optical sensing, have been raised and applied for tear sensing and diagnostic platforms in recent decades. Wearable sensors in this case have been increasingly developed for ocular diagnosis. Contact lenses, as one of the commercialized and popular tools for ocular dysfunction, have been developed as a platform for fluorescence sensing in tears diagnostics and real-time monitoring. Numbers of biochemical analytes have been examined through developed fluorescent contact lens sensors, including pH values, electrolytes, glucose, and enzymes. These sensors have been proven for monitoring ocular conditions, enhancing and detecting medical treatments, and tracking efficiency of related ophthalmic surgeries at POC settings. This review summarizes the applied ophthalmic fluorescence sensing technologies in tears for ocular diagnosis and monitoring. In addition, the cooperation of fabricated fluorescent sensor with mobile phone readout devices for diagnosing ocular diseases with specific biomarkers continuously is also discussed. Further perspectives for the developments and applications of fluorescent ocular sensing and diagnosing technologies are also provided.

Semi-Automated Quantification of Retinal and Choroidal Biomarkers in Retinal Vascular Diseases: Agreement of Spectral-Domain Optical Coherence Tomography with and without Enhanced Depth Imaging Mode

Background: We compared with and without enhanced depth imaging mode (EDI) in semi-automated quantification of retinal and choroidal biomarkers in optical coherence tomography (OCT) in patients with diabetic retinopathy (DR) or retinal vein occlusion (RVO) complicated by macular edema. We chose to study three OCT biomarkers: the numbers of hyperreflective foci (HF), the ellipsoid zone reflectivity ratio (EZR) and the choroidal vascularity index (CVI), all known to be correlated with visual acuity changes or treatment outcomes. Methods: In a single examination, one eye of each patient (n = 60; diabetic retinopathy: n = 27, retinal vein occlusion: n = 33) underwent macular 870 nm spectral domain-OCT (SD-OCT) B-scans without and with EDI mode. Semi-automated quantification of HF, EZR and CVI was applied according to preexisting published protocols. Paired Student’s t-test or Wilcoxon rank-sum test was used to test for differences in subgroups. Intraclass correlation coefficient (ICC) and Bland–Altman plots were applied to describe the agreement between quantification in EDI and conventional OCT mode. The effect of macular edema on semi-automated quantification was evaluated. Results: For the entire cohort, quantification of all three biomarkers was not significantly different in SD-OCT scans with and without EDI mode (p > 0.05). ICC was 0.78, 0.90 and 0.80 for HF, EZR and CVI. The presence of macular edema led to significant differences in the quantification of hyperreflective foci (without EDI: 80.00 ± 33.70, with EDI: 92.08 ± 38.11; mean difference: 12.09, p = 0.03), but not in the quantification of EZR and CVI (p > 0.05). Conclusion: Quantification of EZR and CVI was comparable whether or not EDI mode was used. In conclusion, both retinal and choroidal biomarkers can be quantified from one single 870 nm SD-OCT EDI image.

Quantitative Imaging Biomarkers in Age-Related Macular Degeneration and Diabetic Eye Disease: A Step Closer to Precision Medicine

The management of retinal diseases relies heavily on digital imaging data, including optical coherence tomography (OCT) and fluorescein angiography (FA). Targeted feature extraction and the objective quantification of features provide important opportunities in biomarker discovery, disease burden assessment, and predicting treatment response. Additional important advantages include increased objectivity in interpretation, longitudinal tracking, and ability to incorporate computational models to create automated diagnostic and clinical decision support systems. Advances in computational technology, including deep learning and radiomics, open new doors for developing an imaging phenotype that may provide in-depth personalized disease characterization and enhance opportunities in precision medicine. In this review, we summarize current quantitative and radiomic imaging biomarkers described in the literature for age-related macular degeneration and diabetic eye disease using imaging modalities such as OCT, FA, and OCT angiography (OCTA). Various approaches used to identify and extract these biomarkers that utilize artificial intelligence and deep learning are also summarized in this review. These quantifiable biomarkers and automated approaches have unleashed new frontiers of personalized medicine where treatments are tailored, based on patient-specific longitudinally trackable biomarkers, and response monitoring can be achieved with a high degree of accuracy.

Ophthalmic sensing technologies for ocular disease diagnostics

Point-of-care diagnosis and personalized treatments are critical in ocular physiology and disease. Continuous sampling of tear fluid for ocular diagnosis is a need for further exploration. Several techniques have been developed for possible ophthalmological applications, from traditional spectroscopies to wearable sensors. Contact lenses are commonly used devices for vision correction, as well as for other therapeutic and cosmetic purposes. They are increasingly being developed into ocular sensors, being used to sense and monitor biochemical analytes in tear fluid, ocular surface temperature, intraocular pressure, and pH value. These sensors have had success in detecting ocular conditions, optimizing pharmaceutical treatments, and tracking treatment efficacy in point-of-care settings. However, there is a paucity of new and effective instrumentation reported in ophthalmology. Hence, this review will summarize the applied ophthalmic technologies for ocular diagnostics and tear monitoring, including both conventional and biosensing technologies. Besides applications of smart readout devices for continuous monitoring, targeted biomarkers are also discussed for the convenience of diagnosis of various ocular diseases. A further discussion is also provided for future aspects and market requirements related to the commercialization of novel types of contact lens sensors.