Techniques for imaging the choroid and choroidal blood flow in vivo.

The choroid, which is a highly vascularized layer between the retina and sclera, is essential for supplying oxygen and nutrients to the outer retina. Choroidal vascular dysfunction has been implicated in numerous ocular diseases, including age-related macular degeneration, central serous chorioretinopathy, polypoidal choroidal vasculopathy, and myopia. Traditionally, the in vivo assessment of choroidal blood flow relies on techniques such as laser Doppler flowmetry, laser speckle flowgraphy, pneumotonometry, laser interferometry, and ultrasonic color Doppler imaging. While the aforementioned methods have provided valuable insights into choroidal blood flow regulation, their clinical applications have been limited. Recent advancements in optical coherence tomography and optical coherence tomography angiography have expanded our understanding of the choroid, allowing detailed visualization of the larger choroidal vessels and choriocapillaris, respectively. This review provides an overview of the available techniques that can investigate the choroid and its blood flow in vivo. Future research should combine these techniques to comprehensively image the entire choroidal microcirculation and develop robust methods to quantify choroidal blood flow. The potential findings will provide a better picture of choroidal hemodynamics and its effect on ocular health and disease.

Association of Antihypertensive Medication with Retinal Nerve Fiber Layer and Ganglion Cell-Inner Plexiform Layer Thickness.

PURPOSE: To evaluate the association between different classes of antihypertensive medication with retinal nerve fiber layer (RNFL) and ganglion cell-inner plexiform layer (GC-IPL) thickness in a nonglaucomatous multiethnic Asian population. DESIGN: Population-based, cross-sectional study. PARTICIPANTS: A total of 9144 eyes for RNFL analysis (2668 Malays, 3554 Indians, and 2922 Chinese) and 8549 eyes for GC-IPL analysis (2460 Malays, 3230 Indians, and 2859 Chinese) aged 44 to 86 years. METHODS: Participants underwent standardized systemic and ocular examinations and interviewer-administered questionnaires for collection of data on medication and other variables. Intraocular pressure (IOP) readings were obtained by Goldmann applanation tonometry before pupil dilation for fundoscopy and OCT imaging. Blood pressure (BP) was measured with an automatic BP monitor. Mean arterial pressure (MAP) was defined as diastolic BP plus 1/3 (systolic BP - diastolic BP). Regression models were used to investigate the association of antihypertensive medication with OCT measurements of RNFL and GC-IPL. MAIN OUTCOME MEASURES: Average and sectoral RNFL and GC-IPL thickness. RESULTS: After adjusting for age, gender, ethnicity, MAP, IOP, body mass index (BMI), and presence of diabetes, we found that participants taking any type of antihypertensive medication (ß = -0.83; 95% confidence interval [CI], -1.46 to -0.02; P = 0.01), specifically angiotensin-converting enzyme inhibitors (ACEIs) (ß = -1.66; 95% CI, -2.57 to -0.75; P < 0.001) or diuretics (ß = -1.38; 95% CI, -2.59 to -0.17; P < 0.05), had thinner average RNFL in comparison with participants who were not receiving antihypertensive treatment. Use of a greater number of antihypertensive medications was significantly associated with thinner average RNFL (P for trend = 0.001). This association was most evident in the inferior RNFL quadrant in participants using ACEIs (ß = -2.44; 95% CI, -3.99 to -0.89; P = 0.002) or diuretics (ß = -2.76; 95% CI, -4.76 to -0.76; P = 0.007). A similar trend was noted in our analysis of macular GC-IPL thickness. CONCLUSIONS: Use of 2 or more antihypertensive medications, ACEI, and diuretics were associated with a loss of structural markers of retinal ganglion cell health in a multiethnic Asian population.

Techniques and Applications in Skin OCT Analysis.

The skin is the largest organ of our body. Skin disease abnormalities which occur within the skin layers are difficult to examine visually and often require biopsies to make a confirmation on a suspected condition. Such invasive methods are not well-accepted by children and women due to the possibility of scarring. Optical coherence tomography (OCT) is a non-invasive technique enabling in vivo examination of sub-surface skin tissue without the need for excision of tissue. However, one of the challenges in OCT imaging is the interpretation and analysis of OCT images. In this review, we discuss the various methodologies in skin layer segmentation and how it could potentially improve the management of skin diseases. We also present a review of works which use advanced machine learning techniques to achieve layers segmentation and detection of skin diseases. Lastly, current challenges in analysis and applications are also discussed.

A survey on computer aided diagnosis for ocular diseases.

BACKGROUND: Computer Aided Diagnosis (CAD), which can automate the detection process for ocular diseases, has attracted extensive attention from clinicians and researchers alike. It not only alleviates the burden on the clinicians by providing objective opinion with valuable insights, but also offers early detection and easy access for patients. METHOD: We review ocular CAD methodologies for various data types. For each data type, we investigate the databases and the algorithms to detect different ocular diseases. Their advantages and shortcomings are analyzed and discussed. RESULT: We have studied three types of data (i.e., clinical, genetic and imaging) that have been commonly used in existing methods for CAD. The recent developments in methods used in CAD of ocular diseases (such as Diabetic Retinopathy, Glaucoma, Age-related Macular Degeneration and Pathological Myopia) are investigated and summarized comprehensively. CONCLUSION: While CAD for ocular diseases has shown considerable progress over the past years, the clinical importance of fully automatic CAD systems which are able to embed clinical knowledge and integrate heterogeneous data sources still show great potential for future breakthrough.

Evaluation of thickness of individual macular retinal layers in diabetic eyes from optical coherence tomography.

The effect of diabetes mellitus (DM) on individual retinal layers remains incompletely understood. We evaluated the intra-retinal layer thickness alterations in 71 DM eyes with no diabetic retinopathy (DR), 90 with mild DR, and 63 with moderate DR without macular edema, using spectral-domain optical coherence tomography (SD-OCT) and the Iowa Reference Algorithm for automated retinal layer segmentation. The average thickness of 10 intra-retinal layers was then corrected for ocular magnification using axial length measurements, and pairwise comparisons were made using multivariable linear regression models adjusted for gender and race. In DM no DR eyes, significant thinning was evident in the ganglion cell layer (GCL; p < 0.001), inner nuclear layer (INL; p = 0.001), and retinal pigment epithelium (RPE; p = 0.014) compared to normal eyes. Additionally, mild DR eyes exhibited a thinner inner plexiform layer (IPL; p = 0.008) than DM no DR eyes. Conversely, moderate DR eyes displayed thickening in the INL, outer nuclear layer, IPL, and retinal nerve fiber layer (all p ≤ 0.002), with notably worse vision. These findings highlight distinctive patterns: early diabetic eyes experience thinning in specific retinal layers, while moderate DR eyes exhibit thickening of certain layers and slightly compromised visual acuity, despite the absence of macular edema. Understanding these structural changes is crucial for comprehending diabetic eye complications.

Segregation of neuronal and vascular retinal damage in patients with hypertension and diabetes.

This study aimed to examine the impact of diabetes and hypertension on retinal nerve fiber layer (RNFL) thickness components. Optical coherence tomography (OCT) measurements do not consider blood vessel contribution, which this study addressed. We hypothesized that diabetes and/or hypertension would lead to thinner RNFL versus controls due to the vascular component. OCT angiography was used to measure the RNFL in 121 controls, 50 diabetes patients, 371 hypertension patients, and 177 diabetes patients with hypertension. A novel technique separated the RNFL thickness into original (vascular component) and corrected (no vascular component) measurements. Diabetes-only (98 ± 1.7 µm; p = 0.002) and diabetes with hypertension (99 ± 0.8 µm; p = 0.001) patients had thinner original RNFL versus controls (102 ± 0.8 µm). No difference was seen between hypertension-only patients (101 ± 0.5 µm; p = 0.083) and controls. After removing the blood vessel component, diabetes/hypertension groups had thinner corrected RNFL versus controls (p = 0.024). Discrepancies in diabetes/hypertension patients were due to thicker retinal blood vessels within the RNFL thickness (p = 0.002). Our findings suggest that diabetes and/or hypertension independently contribute to neurodegenerative thinning of the RNFL, even in the absence of retinopathy. The differentiation of neuronal and vascular components in RNFL thickness measurements provided by the novel technique highlights the importance of considering vascular changes in individuals with these conditions.

Defining the structure-function relationship of specific lesions in early and advanced age-related macular degeneration.

The objective of this study is to define structure-function relationships of pathological lesions related to age-related macular degeneration (AMD) using microperimetry and multimodal retinal imaging. We conducted a cross-sectional study of 87 patients with AMD (30 eyes with early and intermediate AMD and 110 eyes with advanced AMD), compared to 33 normal controls (66 eyes) recruited from a single tertiary center. All participants had enface and cross-sectional optical coherence tomography (Heidelberg HRA-2), OCT angiography, color and infra-red (IR) fundus and microperimetry (MP) (Nidek MP-3) performed. Multimodal images were graded for specific AMD pathological lesions. A custom marking tool was used to demarcate lesion boundaries on corresponding enface IR images, and subsequently superimposed onto MP color fundus photographs with retinal sensitivity points (RSP). The resulting overlay was used to correlate pathological structural changes to zonal functional changes. Mean age of patients with early/intermediate AMD, advanced AMD and controls were 73(SD = 8.2), 70.8(SD = 8), and 65.4(SD = 7.7) years respectively. Mean retinal sensitivity (MRS) of both early/intermediate (23.1 dB; SD = 5.5) and advanced AMD (18.1 dB; SD = 7.8) eyes were significantly worse than controls (27.8 dB, SD = 4.3) (p < 0.01). Advanced AMD eyes had significantly more unstable fixation (70%; SD = 63.6), larger mean fixation area (3.9 mm2; SD = 3.0), and focal fixation point further away from the fovea (0.7 mm; SD = 0.8), than controls (29%; SD = 43.9; 2.6 mm2; SD = 1.9; 0.4 mm; SD = 0.3) (p ≤ 0.01). Notably, 22 fellow eyes of AMD eyes (25.7 dB; SD = 3.0), with no AMD lesions, still had lower MRS than controls (p = 0.04). For specific AMD-related lesions, end-stage changes such as fibrosis (5.5 dB, SD = 5.4 dB) and atrophy (6.2 dB, SD = 7.0 dB) had the lowest MRS; while drusen and pigment epithelial detachment (17.7 dB, SD = 8.0 dB) had the highest MRS. Peri-lesional areas (20.2 dB, SD = 7.6 dB) and surrounding structurally normal areas (22.2 dB, SD = 6.9 dB) of the retina with no AMD lesions still had lower MRS compared to controls (27.8 dB, SD = 4.3 dB) (p < 0.01). Our detailed topographic structure-function correlation identified specific AMD pathological changes associated with a poorer visual function. This can provide an added value to the assessment of visual function to optimize treatment outcomes to existing and potentially future novel therapies.

Ocular microvascular complications in diabetic retinopathy: insights from machine learning.

INTRODUCTION: Diabetic retinopathy (DR) is a leading cause of preventable blindness among working-age adults, primarily driven by ocular microvascular complications from chronic hyperglycemia. Comprehending the complex relationship between microvascular changes in the eye and disease progression poses challenges, traditional methods assuming linear or logistical relationships may not adequately capture the intricate interactions between these changes and disease advances. Hence, the aim of this study was to evaluate the microvascular involvement of diabetes mellitus (DM) and non-proliferative DR with the implementation of non-parametric machine learning methods. RESEARCH DESIGN AND METHODS: We conducted a retrospective cohort study that included optical coherence tomography angiography (OCTA) images collected from a healthy group (196 eyes), a DM no DR group (120 eyes), a mild DR group (71 eyes), and a moderate DR group (66 eyes). We implemented a non-parametric machine learning method for four classification tasks that used parameters extracted from the OCTA images as predictors: DM no DR versus healthy, mild DR versus DM no DR, moderate DR versus mild DR, and any DR versus no DR. SHapley Additive exPlanations values were used to determine the importance of these parameters in the classification. RESULTS: We found large choriocapillaris flow deficits were the most important for healthy versus DM no DR, and became less important in eyes with mild or moderate DR. The superficial microvasculature was important for the healthy versus DM no DR and mild DR versus moderate DR tasks, but not for the DM no DR versus mild DR task-the stage when deep microvasculature plays an important role. Foveal avascular zone metric was in general less affected, but its involvement increased with worsening DR. CONCLUSIONS: The findings from this study provide valuable insights into the microvascular involvement of DM and DR, facilitating the development of early detection methods and intervention strategies.

Enhancing the structure-function relationship in glaucoma using anatomical compensation of retinal nerve fibre layer.

BACKGROUND/AIMS: To investigate whether compensating retinal nerve fibre layer (RNFL) thickness measurements for demographic and anatomical ocular factors can strengthen the structure-function relationship in patients with glaucoma. METHODS: 600 eyes from 412 patients with glaucoma (mean deviation of the visual field (MD VF) -6.53±5.55 dB) were included in this cross-sectional study. Participants underwent standard automated perimetry and spectral-domain optical coherence tomography imaging (Cirrus; Carl Zeiss Meditec). Compensated RNFL thickness was computed considering age, refractive error, optic disc parameters and retinal vessel density. The relationship between MD VF and RNFL thickness measurements, with or without demographic and anatomical compensation, was evaluated sectorally and focally. RESULTS: The superior arcuate sector exhibited the highest correlation between measured RNFL and MD VF, with a correlation of 0.49 (95% CI 0.37 to 0.59). Applying the compensated RNFL data increased the correlation substantially to 0.62 (95% CI 0.52 to 0.70; p<0.001). Only 61% of the VF locations showed a significant relationship (Spearman's correlation of at least 0.30) between structural and functional aspects using measured RNFL data, and this increased to 78% with compensated RNFL measurements. In the 10°-20° VF region, the slope below the breakpoint for compensated RNFL thickness demonstrated a more robust correlation (slope=1.66±0.18 µm/dB; p<0.001) than measured RNFL (slope=0.27±0.67 µm/dB; p=0.688). CONCLUSION: Compensated RNFL data improve the correlation between RNFL measurements and VF parameters. This indicates that creating structure-to-function maps that consider anatomical variances may aid in identifying localised structural and functional loss in glaucoma.

Retinal Nerve Fiber Layer Damage Assessment in Glaucomatous Eyes Using Retinal Retardance Measured by Polarization-Sensitive Optical Coherence Tomography.

Purpose: To assess the diagnostic performance and structure-function association of retinal retardance (RR), a customized metric measured by a prototype polarization-sensitive optical coherence tomography (PS-OCT), across various stages of glaucoma. Methods: This cross-sectional pilot study analyzed 170 eyes from 49 healthy individuals and 68 patients with glaucoma. The patients underwent PS-OCT imaging and conventional spectral-domain optical coherence tomography (SD-OCT), as well as visual field (VF) tests. Parameters including RR and retinal nerve fiber layer thickness (RNFLT) were extracted from identical circumpapillary regions of the fundus. Glaucomatous eyes were categorized into early, moderate, or severe stages based on VF mean deviation (MD). The diagnostic performance of RR and RNFLT in discriminating glaucoma from controls was assessed using receiver operating characteristic (ROC) curves. Correlations among VF-MD, RR, and RNFLT were evaluated and compared within different groups of disease severity. Results: The diagnostic performance of both RR and RNFLT was comparable for glaucoma detection (RR AUC = 0.98, RNFLT AUC = 0.97; P = 0.553). RR showed better structure-function association with VF-MD than RNFLT (RR VF-MD = 0.68, RNFLT VF-MD = 0.58; z = 1.99; P = 0.047) in glaucoma cases, especially in severe glaucoma, where the correlation between VF-MD and RR (r = 0.73) was significantly stronger than with RNFLT (r = 0.43, z = 1.96, P = 0.050). In eyes with early and moderate glaucoma, the structure-function association was similar when using RNFLT and RR. Conclusions: RR and RNFLT have similar performance in glaucoma diagnosis. However, in patients with glaucoma especially severe glaucoma, RR showed a stronger correlation with VF test results. Further research is needed to validate RR as an indicator for severe glaucoma evaluation and to explore the benefits of using PS-OCT in clinical practice. Translational Relevance: We demonstrated that PS-OCT has the potential to evaluate the status of RNFL structural damage in eyes with severe glaucoma, which is currently challenging in clinics.

Anatomical compensation of retinal nerve fiber layer improves the detection of glaucoma between ethnicities.

The study aimed to evaluate the impact of compensating retinal nerve fiber layer (RNFL) thickness for demographic and anatomical factors on glaucoma detection in Chinese and Indian adults. A population-based study included 1995 healthy participants (1076 Chinese and 919 Indians) to construct a multivariable linear regression compensation model. This model was applied to 357 Chinese glaucoma patients, 357 healthy Chinese, and 357 healthy Indians using Cirrus spectral-domain optical coherence tomography (OCT). The compensated RNFL thickness considered age, refractive error, optic disc parameters, and retinal vessel density. Results showed that although the average RNFL thickness was significantly higher in Chinese participants compared to Indians, the compensation model reduced this difference to nonsignificance. Moreover, the compensation model significantly improved the area under the receiver operating characteristic curve (0.90 vs. 0.78; p<0.001), sensitivity (75% vs. 51%), and specificity (67% vs. 32%) in distinguishing Chinese glaucoma patients from healthy Indian individuals. The compensation model significantly enhanced the diagnostic accuracy of RNFL thickness in distinguishing glaucoma in the Chinese ethnic group compared to the OCT instrument's default values. These results suggest that modifying RNFL measurements based on individual characteristics can yield substantial benefits for glaucoma detection across ethnicities.

Optical coherence tomography choroidal enhancement using generative deep learning.

Spectral-domain optical coherence tomography (SDOCT) is the gold standard of imaging the eye in clinics. Penetration depth with such devices is, however, limited and visualization of the choroid, which is essential for diagnosing chorioretinal disease, remains limited. Whereas swept-source OCT (SSOCT) devices allow for visualization of the choroid these instruments are expensive and availability in praxis is limited. We present an artificial intelligence (AI)-based solution to enhance the visualization of the choroid in OCT scans and allow for quantitative measurements of choroidal metrics using generative deep learning (DL). Synthetically enhanced SDOCT B-scans with improved choroidal visibility were generated, leveraging matching images to learn deep anatomical features during the training. Using a single-center tertiary eye care institution cohort comprising a total of 362 SDOCT-SSOCT paired subjects, we trained our model with 150,784 images from 410 healthy, 192 glaucoma, and 133 diabetic retinopathy eyes. An independent external test dataset of 37,376 images from 146 eyes was deployed to assess the authenticity and quality of the synthetically enhanced SDOCT images. Experts' ability to differentiate real versus synthetic images was poor (47.5% accuracy). Measurements of choroidal thickness, area, volume, and vascularity index, from the reference SSOCT and synthetically enhanced SDOCT, showed high Pearson's correlations of 0.97 [95% CI: 0.96-0.98], 0.97 [0.95-0.98], 0.95 [0.92-0.98], and 0.87 [0.83-0.91], with intra-class correlation values of 0.99 [0.98-0.99], 0.98 [0.98-0.99], and 0.95 [0.96-0.98], 0.93 [0.91-0.95], respectively. Thus, our DL generative model successfully generated realistic enhanced SDOCT data that is indistinguishable from SSOCT images providing improved visualization of the choroid. This technology enabled accurate measurements of choroidal metrics previously limited by the imaging depth constraints of SDOCT. The findings open new possibilities for utilizing affordable SDOCT devices in studying the choroid in both healthy and pathological conditions.

Comparison of manual and artificial intelligence-automated choroidal thickness segmentation of optical coherence tomography imaging in myopic adults.

BACKGROUND: Myopia affects 1.4 billion individuals worldwide. Notably, there is increasing evidence that choroidal thickness plays an important role in myopia and risk of developing myopia-related conditions. With the advancements in artificial intelligence (AI), choroidal thickness segmentation can now be automated, offering inherent advantages such as better repeatability, reduced grader variability, and less reliance for manpower. Hence, we aimed to evaluate the agreement between AI-automated and manual segmented measurements of subfoveal choroidal thickness (SFCT) using two swept-source optical coherence tomography (OCT) systems. METHODS: Subjects aged ≥ 16 years, with myopia of ≥ 0.50 diopters in both eyes, were recruited from the Prospective Myopia Cohort Study in Singapore (PROMYSE). OCT scans were acquired using Triton DRI-OCT and PLEX Elite 9000. OCT images were segmented both automatically with an established SA-Net architecture and manually using a standard technique with adjudication by two independent graders. SFCT was subsequently determined based on the segmentation. The Bland-Altman plot and intraclass correlation coefficient (ICC) were used to evaluate the agreement. RESULTS: A total of 229 subjects (456 eyes) with mean [± standard deviation (SD)] age of 34.1 (10.4) years were included. The overall SFCT (mean ± SD) based on manual segmentation was 216.9 ± 82.7 µm with Triton DRI-OCT and 239.3 ± 84.3 µm with PLEX Elite 9000. ICC values demonstrated excellent agreement between AI-automated and manual segmented SFCT measurements (PLEX Elite 9000: ICC = 0.937, 95% CI: 0.922 to 0.949, P < 0.001; Triton DRI-OCT: ICC = 0.887, 95% CI: 0.608 to 0.950, P < 0.001). For PLEX Elite 9000, manual segmented measurements were generally thicker when compared to AI-automated segmented measurements, with a fixed bias of 6.3 µm (95% CI: 3.8 to 8.9, P < 0.001) and proportional bias of 0.120 (P < 0.001). On the other hand, manual segmented measurements were comparatively thinner than AI-automated segmented measurements for Triton DRI-OCT, with a fixed bias of - 26.7 µm (95% CI: - 29.7 to - 23.7, P < 0.001) and proportional bias of - 0.090 (P < 0.001). CONCLUSION: We observed an excellent agreement in choroidal segmentation measurements when comparing manual with AI-automated techniques, using images from two SS-OCT systems. Given its edge over manual segmentation, automated segmentation may potentially emerge as the primary method of choroidal thickness measurement in the future.

Validity of a new optic disc grading software for use in clinical and epidemiological research.

BACKGROUND: To determine the reliability and agreement of a new optic disc grading software program for use in clinical, epidemiological research. DESIGN: Reliability and agreement study. SAMPLES: 328 monoscopic and 85 stereoscopic optic disc images. METHODS: Optic disc parameters were measured using a new optic disc grading software (Singapore Optic Disc Assessment) that is based on polynomial curve-fitting algorithm. Two graders independently graded 328 monoscopic images to determine intergrader reliability. One grader regraded the images after 1 month to determine intragrader reliability. In addition, 85 stereo optic disc images were separately selected, and vertical cup-to-disc ratios were measured using both the new software and standardized Wisconsin manual stereo-grading method by the same grader 1 month apart. Intraclass correlation coefficient (ICC) and Bland-Altman plot analyses were performed. MAIN OUTCOME MEASURES: Optic disc parameters. RESULTS: The intragrader and intergrader reliability for optic disc measurements using Singapore Optic Disc Assessment was high (ICC ranging from 0.82 to 0.94). The mean differences (95% limits of agreement) for intergrader vertical cup-to-disc ratio measurements were 0.00 (-0.12 to 0.13) and 0.03 (-0.15 to 0.09), respectively. The vertical cup-to-disc ratio agreement between the software and Wisconsin grading method was extremely close (ICC = 0.94). The mean difference (95% limits of agreement) of vertical cup-to-disc ratio measurement between the two methods was 0.03 (-0.09 to 0.16). CONCLUSIONS: Intragrader and intergrader reliability using Singapore Optic Disc Assessment was excellent. This software was highly comparable with standardized stereo-grading method. Singapore Optic Disc Assessment is useful for grading digital optic disc images in clinical, population-based studies.

Predicting glaucoma progression using deep learning framework guided by generative algorithm.

Glaucoma is a slowly progressing optic neuropathy that may eventually lead to blindness. To help patients receive customized treatment, predicting how quickly the disease will progress is important. Structural assessment using optical coherence tomography (OCT) can be used to visualize glaucomatous optic nerve and retinal damage, while functional visual field (VF) tests can be used to measure the extent of vision loss. However, VF testing is patient-dependent and highly inconsistent, making it difficult to track glaucoma progression. In this work, we developed a multimodal deep learning model comprising a convolutional neural network (CNN) and a long short-term memory (LSTM) network, for glaucoma progression prediction. We used OCT images, VF values, demographic and clinical data of 86 glaucoma patients with five visits over 12 months. The proposed method was used to predict VF changes 12 months after the first visit by combining past multimodal inputs with synthesized future images generated using generative adversarial network (GAN). The patients were classified into two classes based on their VF mean deviation (MD) decline: slow progressors (< 3 dB) and fast progressors (> 3 dB). We showed that our generative model-based novel approach can achieve the best AUC of 0.83 for predicting the progression 6 months earlier. Further, the use of synthetic future images enabled the model to accurately predict the vision loss even earlier (9 months earlier) with an AUC of 0.81, compared to using only structural (AUC = 0.68) or only functional measures (AUC = 0.72). This study provides valuable insights into the potential of using synthetic follow-up OCT images for early detection of glaucoma progression.

Neurovascular segregation of the retinal nerve fiber layer in glaucoma.

The imaging data of one eye from 154 healthy and 143 glaucoma participants were acquired to evaluate the contributions of the neuronal and vascular components within the retinal nerve fiber layer (RNFL) for detecting glaucoma and modeling visual field loss through the use of optical coherence tomography (OCT) and OCT angiography. The neuronal and vascular components within the circumpapillary RNFL were independently evaluated. In healthy eyes, the neuronal component showed a stronger association with age (r = -0.52, p < 0.001) compared to measured RNFL thickness (r = -0.46, p < 0.001). Using the neuronal component alone improved detection of glaucoma (AUC: 0.890 ± 0.020) compared to measured RNFL thickness (AUC: 0.877 ± 0.021; χ2 = 5.54, p = 0.019). Inclusion of the capillary components with the sectoral neuronal component resulted in a significant improvement in glaucoma detection (AUC: 0.927 ± 0.015; χ2 = 15.34, p < 0.001). After adjusting for potential confounders, AUC increased to 0.952 ± 0.011. Results from modeling visual field loss in glaucoma eyes suggest that visual field losses associated with neuronal thinning were moderated in eyes with a larger capillary component. These findings suggest that segregation of the neurovascular components could help improve understanding of disease pathophysiology and affect disease management in glaucoma.

Combining retinal structural and vascular measurements improves discriminative power for multiple sclerosis patients.

Data on how retinal structural and vascular parameters jointly influence the diagnostic performance of detection of multiple sclerosis (MS) patients without optic neuritis (MSNON) are lacking. To investigate the diagnostic performance of structural and vascular changes to detect MSNON from controls, we performed a cross-sectional study of 76 eyes from 51 MS participants and 117 eyes from 71 healthy controls. Retinal macular ganglion cell complex (GCC), retinal nerve fiber layer (RNFL) thicknesses, and capillary densities from the superficial (SCP) and deep capillary plexuses (DCP) were obtained from the Cirrus AngioPlex. The best structural parameter for detecting MS was compensated RNFL from the optic nerve head (AUC = 0.85), followed by GCC from the macula (AUC = 0.79), while the best vascular parameter was the SCP (AUC = 0.66). Combining structural and vascular parameters improved the diagnostic performance for MS detection (AUC = 0.90; p<0.001). Including both structure and vasculature in the joint model considerably improved the discrimination between MSNON and normal controls compared to each parameter separately (p = 0.027). Combining optical coherence tomography (OCT)-derived structural metrics and vascular measurements from optical coherence tomography angiography (OCTA) improved the detection of MSNON. Further studies may be warranted to evaluate the clinical utility of OCT and OCTA parameters in the prediction of disease progression.

Assessing the external validity of machine learning-based detection of glaucoma.

Studies using machine learning (ML) approaches have reported high diagnostic accuracies for glaucoma detection. However, none assessed model performance across ethnicities. The aim of the study is to externally validate ML models for glaucoma detection from optical coherence tomography (OCT) data. We performed a prospective, cross-sectional study, where 514 Asians (257 glaucoma/257 controls) were enrolled to construct ML models for glaucoma detection, which was then tested on 356 Asians (183 glaucoma/173 controls) and 138 Caucasians (57 glaucoma/81 controls). We used the retinal nerve fibre layer (RNFL) thickness values produced by the compensation model, which is a multiple regression model fitted on healthy subjects that corrects the RNFL profile for anatomical factors and the original OCT data (measured) to build two classifiers, respectively. Both the ML models (area under the receiver operating [AUC] = 0.96 and accuracy = 92%) outperformed the measured data (AUC = 0.93; P < 0.001) for glaucoma detection in the Asian dataset. However, in the Caucasian dataset, the ML model trained with compensated data (AUC = 0.93 and accuracy = 84%) outperformed the ML model trained with original data (AUC = 0.83 and accuracy = 79%; P < 0.001) and measured data (AUC = 0.82; P < 0.001) for glaucoma detection. The performance with the ML model trained on measured data showed poor reproducibility across different datasets, whereas the performance of the compensated data was maintained. Care must be taken when ML models are applied to patient cohorts of different ethnicities.

Dietary Intake and Diabetic Retinopathy: A Systematic Review of the Literature.

Diabetic retinopathy (DR) is a common microvascular complication of diabetes mellitus. The evidence connecting dietary intake and DR is emerging, but uncertain. We conducted a systematic review to comprehensively summarize the current understanding of the associations between dietary consumption, DR and diabetic macular edema (DME). We systematically searched PubMed, Embase, Medline, and the Cochrane Central Register of Controlled Trials between January 1967 to May 2022 for all studies investigating the effect of diet on DR and DME. Of the 4962 articles initially identified, 54 relevant articles were retained. Our review found that higher intakes of fruits, vegetables, dietary fibers, fish, a Mediterranean diet, oleic acid, and tea were found to have a protective effect against DR. Conversely, high intakes of diet soda, caloric intake, rice, and choline were associated with a higher risk of DR. No association was seen between vitamin C, riboflavin, vitamin D, and milk and DR. Only one study in our review assessed dietary intake and DME and found a risk of high sodium intake for DME progression. Therefore, the general recommendation for nutritional counseling to manage diabetes may be beneficial to prevent DR risk, but prospective studies in diverse diabetic populations are needed to confirm our findings and expand clinical guidelines for DR management.